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https://beta.jisc.ac.uk/training/an-introduction-to-generative-ai-in-education

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About

As part of Jisc's exploration of artificial intelligence in the context of education, we've created a free 'MOOC'. If you're interested in artificial intelligence (AI) with a focus on the impact within education and have an hour to spare, this a-sync module is designed for you.

Through videos, current research and discussion forums, the module explores two main elements;

An introduction to Generative AI technology

The implications of Generative AI on education

If you haven't got a whole hour, you can revisit and take a section (5 total) at a time.

New places released on the 1st of every month

This 1 hour MOOC is delivered using our online course support platform itslearning.

You will be sent your login details once your application has been successful.

Who should attend

This module is open to any Jisc member that's interested in the use of artificial intelligence within education.

What we cover

The MOOC will cover the following:

How can Generative AI help educators?

Generative AI tools

Impact of Generative AI on Education

Use of AI in teaching and learning

Accessibility and Regulation

Training outcomes

We hope through discussions with your peers and further understanding through our research it'll help you think about the uses of AI within education and how it can best serve educators and learners alike.

Booking information

This module is free to Jisc members.
Please note, as the module is designed to be supported and collaboration is encouraged booking numbers are limited on a monthly basis. New places will be released on the 1st of each month, so if you have missed out this time then please check again later.
For more information, email training@jisc.ac.uk or phone 01235 822242.

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An introduction to Generative AI in education - Jisc

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This MOOC aims to give educators an understanding about AI and how it's currently impacting education and learners.

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An introduction to prompting generative AI like ChatGPT for teaching and learning

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To fulfil its potential, generative artificial intelligence requires effective instructions. In the first of a series, Seb Dianati and Suman Laudari explain how to craft a useful AI prompt

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Hello GPT! Goodbye home examination? An exploratory study of AI chatbots impact on university teachers’ assessment practices

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Hello GPT! Goodbye home examination? An exploratory study of AI chatbots impact on university teachers’ assessment practices

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https://www.tandfonline.com/doi/full/10.1080/02602938.2023.2241676

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On risk- and necessity for HE start making demands 

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On risk- and necessity for HE start making demands 

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I suggested in an earlier post that the benefits of GenAI for learning are still weakly evidenced. I argued in the same post that opportunities and risks should not be treated the same. Opportunities are always waiting to manifest themselves, just off-screen. Risks are harder to see. They often take research to find, and collective action to deal with. As just one user, alone in front of my screen, it's easy to feel disempowered or overwhelmed by a sense of risk. In fact, I have started to feel a bit overwhelmed by the way GenAI's risks are paraded over and over again in reviews and policy papers. They are even (soft of) acknowledged by the AI industry:

But there's something not quite right here. While 'working to address' the temporary 'limitations' of its technology, OpenAI is keen to shift responsibility for use onto 'society'. After all, it is 'society' that is plunging ahead and 'adopting' GPT-4 despite OpenAI's reasonable warnings. Education has a particular responsibility to deal any problems that might arise, through ' user education, and wider AI literacy '. What responsibility should the education sector place on the AI industry in return, I wonder? Perhaps to keep problematic technologies away from the shared information commons we all rely on, and five billion potentially vulnerable and/or unprepared and/or occasionally malicious users?

I do think that education has a responsibility to develop critical users of technology, and to do that we have to witness the many ways we are being asked to become uncritical users of GenAI. We are called to accept a wide range of risks and moral hazards in return for 'keeping up', whether we are teachers trying to keep up with students, students trying to keep up with the job market, or citizens being told 'we' must keep ahead of the AI being developed in other countries.

The command to adopt GenAI gathers energy from all the harms it draws attention to. If users are willing to set aside such critical issues as misinformation, bias, copyright and personal data violations, carbon costs and worker exploitation... the benefits must be worth it. Or else the capture by GenAI of our economy and society must be inevitable, so users might as well get stuck in. Addressing us as users while naming the risks and harms of use reminds us that we have freely decided to ignore them: we have given up the right to critique.

The most blatant assault on our critical thinking is the ' open letter' to humanity issues in March by Elon Musk, Steve Wozniak and other industry insiders. Turning the doom dial to max, they warn of a future when ' nonhuman minds ... eventually outnumber, outsmart, obsolete and replace us'. This Skynet storyline is a barely hidden brag about the power of the AI industry to make or break 'humanity', with every techbro taking the role of Miles Dyson.

Leaving the psychology of the AI industry for another day, all this singularity/death drive material is clearly meant to disarm more prosaic worries about jobs and democracy. Below the 'existential risk' horizon are a large number of human beings facing everyday harms and losses.

In their response to the open letter, researchers at the Distributed AI Research Institute say we should focus our risk assessments on these people:

the very real and very present exploitative practices of the companies claiming to build ['powerful digital minds']... including 1) worker exploitation and massive data theft to create products that profit a handful of entities, 2) the explosion of synthetic media in the world, which both reproduces systems of oppression and endangers our information ecosystem, and 3) the concentration of power in the hands of a few people which exacerbates social inequities.

Witnesses from within the AI sector have been speaking out about these issues for years (this list of key publications from Emily Bender is a good place to start). As educators we can amplify their voices. We can also point to the particular moral jeopardy GenAI models represent to higher education. They offend against values that are important to universities: equity and justice, democratic access to knowledge, truth claims based on evidence, and the rights of authors over their intellectual work. Their use also risks ongoing efforts to decolonise the university, and emerging efforts to decolonise its technologies. As pioneers like Dalia Gebriel, Gurminder Bhambra and Taskeen Adams remind us, universities are colonial institutions in their deep infrastructure as well as their cultures and curricula. Even progressive initiatives rest on these colonial structures - estates, investments, endowments, collections - though they can help to make the structures more visible. But here in GenAI is a brand new infrastructure that follows the same patterns of racial exclusion as earlier ones, that dispossesses people of their intellectual rights, that encloses and degrades the digital commons, and that exploits cheap labour, especially in the global south. Unlike historical infrastructures, this one is being developed, adopted and integrated into our institutions right in front of our eyes.

While these are harms that educators should care about, they affect all large organisations. Are there risks from GenAI that educators in particular should identify, and speak up about? I can think of three kinds (there may be more).

Risks to teaching jobs and conditions, and to the work of teaching

Risks to the intellectual development and wellbeing of students

Risks to knowledge values and practices, in universities and beyond

All of them fall unequally, so some teachers, learners and knowledge users may be relatively unaffected. In three new posts I will try to gather evidence about each area of risk, hoping this contributes to the collective conversation. Because while OpenAI can invite 'people' to give 'input', only powerful sectors like higher education can make collective demands.

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What are the real risks of GenAI that people in higher education should worry about?

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How we can harness AI in teaching and assessments and uphold academic rigour and integrity

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How we can harness AI in teaching and assessments and uphold academic rigour and integrity

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https://warwick.ac.uk/fac/cross_fac/academy/activities/learningcircles/future-of-learning/

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Generative AI: advice for students, produced by a student

RESOURCES AND USEFUL LINKS
Teaching cases on AI: Relevant articles and blogs:

The report to the top left Fundamental questions around "Transforming Higher Education: How we can harness AI in teaching and assessments and uphold academic rigour and integrity" represents our full findings from students and staff across six themes: (1) AI in education; (2) Implications for teaching, including helpful links for educators; (3) Issues of academic integrity and ethical concerns; (4) Principles of assessments when embracing AI; (5) Using AI, including generative AI, for formative dialogue and feedback; and (6) Thoughts on potential future conceptualisation, development and integration of AI tools.

Below the full report to the left there are "Actionable Insights for educators to support teaching, learning and assessments", summarising the above report for three aspects: (1) Teaching and Learning; (2) Formative Feedback; and (3) Assessments. In the left column, below the "Actionable insights for educators", there is a staff presentation on "AI in the classroom". Below this presentation, there is the same report as the one on the top left, but in a Word format.

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Generative AI: advice for students, produced by a student RESOURCES AND USEFUL LINKS Teaching cases on AI: Relevant articles and blogs: The report to the top left Fundamental questions around "Transforming Higher Education: How we can harness AI in teaching and assessments and uphold academic rigour and integrity" represents our

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https://lornamcampbell.org/higher-education/generative-ai-ethics-all-the-way-down/

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Designing assessment in an AI enabled world

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Designing assessment in an AI enabled world

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https://www.ucl.ac.uk/teaching-learning/designing-assessment-ai-enabled-world

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This resource provides guidance on how to adapt assessment to better support learning. It also explores how to accommodate the required changes that might arise from the global use of generative AI.

This resource focusses on the pedagogical impact of AI on our assessment practices. It is designed to promote an environment of trust and responsibility with students through enhanced assessment practice and the fostering of a strong sense of academic integrity.

The suggestions, both in the short and longer term, are designed to enable students to understand and work effectively with the capabilities, limitations and ethical implications of using AI in assessment.

Try out ChatGPT or other generative AI tool for yourself - enter a sample past exam question (don't use a real one) or writing task and see what it comes up with.

1. Consider your current assessments critically. Does the current design really measure what you want it to measure? How else might you ascertain students' learning?

2. If needed, consider amending your exam questions or written assessment tasks to make them more difficult for an AI tool to answer (see below).

3. Make clear to your students what is acceptable use of AI in the context of your assignments, and what is not. Tell your students that they must not try to pass off work created by such tools as their own as this would constitute academic misconduct. Point them to UCL's guidance on academic integrity and to the Academic Manual (section 9.2.1 g, h and m). Read our guidance for staff on using AI tools in assessment.

Contact your Faculty Learning Technology Lead or your Arena Faculty contact if you would like further advice.

Changes you can make for the academic year 2023-24

You can use this series of six videos to guide changes to policy and practice at programme or module level - they have been selected to align with UCL's Assessment Change Timeline.

1. Discuss Academic Integrity and AI with your students

Tell students when and how use of AI is permitted in assessment

Signpost where to find guidance (see below under 'Additional resources')

Explore capabilities and fallabilities of AI with students and colleagues in a context of transparency

Build critical AI literacy

Watch the video on MediaCentral

2. Feedback and formative assessment
Watch the video on MediaCentral
Formative assessments need to be part of an integrated assessment plan where they are linked to success in the summative assessment and where there is a clear rationale for student engagement. These IDEAS cards provide some excellent ways to engage students in formative activities.

Create a shared understanding of the conception and purposes of feedback

Encourage students to be active participants in feedback process

Consider combining tutor, peer and students' self-assessment (the latter could be informed by AI)

Watch the video on MediaCentral
3. Revise exam questions

Pose hypothetical questions with no simple answer

Pose scenario-based questions for problem solving

Ask students to interpret information from a range of sources

Ask students to evaulate AI responses

Consider authentic restrictions to assessment that reflect those in the 'real-world'

Watch the video on MediaCentral
4. Revise essay questions

Switch the format

Students to focus on their personal experience and record development in thinking

Ask students to generate, critique and improve AI responses

Ask students to write for a particular audience

Watch the video on MediaCentral
5. Convert generic questions to scenario-based questions

What are the learning oucomes?

What action is required of the student?

What is the object of the action?

What is the context?

What is the scope or remit of the action?

What evidence does the student need to provide?

6. Upgrade your Multiple Choice Questions (MCQs)

This video includes examples of how ChatGPT3.5 deals with a range of MCQ questions.
Watch the video on MediaCentral

Detailed timings

1. Overview of types of MCQs and performance with ChatGPT (0 mins)

2. ChatGPT and maths questions (3 mins, 40 seconds)

3. Examples of ChatGPT responses to a range of MCQ type questions (4.36 mins)

4. Summary of strategies for upgrading your MCQs (10 mins)

Download the Assessment Menu [pptx]

MCQs, single best answers or extended matching questions that have factual answers may be best be assessed in an invigilated context

Consider including images in the question

Ask students to assess evaluation of AI responses

Reduce the number but increase the complexity of your questions

Ask for 'workings out'

Ask students to submit audio responses including a rationale for their thinking

Plan for larger changes to assessment
Please note:
The Assessment Menu provides you with different options to consider when making changes to your assessment. They are aligned to the Assessment Framework for taught programmes.

The menu includes 40 different cards. Each card illustrates an idea for an assessment that either engages students directly with AI or for which AI find it difficult to generate a response. The suggestions here are starters for discussion and application within your discipline.

This is an evolving resource as things are developing at pace.

Currently there is free access to many applications. UCL do not yet have subscriptions to AI software and it is likely that AI will be embedded in our everyday tools.

Issues of access, equity and transparency with students need to be considered if using AI is an integral part of an assessment task. We need to be attuned to inclusive practice and provide options where appropriate.

Learning outcomes and associated criteria will likely need review to reflect capabilities that graduates will need in an AI-enabled world. For this reason you may find entries such 'emotional intelligence' or 'contextual intelligence' that do not directly map onto Bloom's taxonomy as we know it.

The Assessment Menu is licenced under a  Creative Commons Share Alike 4.0 License.
Back to the top of this page
The Jisc National Centre for AI have created an interactive PowerPoint version of the slides that can be downloaded.

Additional resources

Content for the Assessment Menu is drawn from range of sources and is largely based on Lydia Arnold's Top Trumps resource.

You can also get ideas from the Good Practice in Assessment guide written by expert academics in the IOE, UCL's Faculty for Education and Society. This book includes a resource bank with models, links and ideas that are ready to use and based on evidence.

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Designing assessments for an AI-enabled world

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This resource provides guidance on how to adapt assessment to better support learning. It also explores how to accommodate the required changes that might arise from the global use of generative AI.

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Teaching originality: an essential skill in the age of ChatGPT

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Teaching originality: an essential skill in the age of ChatGPT

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https://www.timeshighereducation.com/campus/teaching-originality-essential-skill-age-chatgpt?utm_source=newsletter&utm_medium=email&utm_campaign=campus-weekly&spMailingID=26513935&spUserID=MTE3ODU4NDYzNzM1NwS2&spJobID=2271375992&spReportId=MjI3MTM3NTk5MgS2

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Teaching originality: an essential skill in the age of ChatGPT

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Academic originality is not about chance, genius or magic. It is about engagement and a clear sense of scholarly contribution. And it can be taught, writes Alastair Bonnett

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<aside> ⚠️ WARNING! Always fact-check what Artificial Intelligence produces. It commonly makes mistakes or invents information, such as fake references.

</aside>

<aside> ⚠️ Warning - Please be careful about any personal data, confidential information or intellectual property you submit to any external site and be mindful of how they process and store your data. You use these tools at your own risk. This guide acknowledges that students will try these AI tools and therefore seeks to help students use the tools constructively but by no means endorses or encourages the use of AI tools.

</aside>

<aside> 💬 *This guide was written by David Millar, Digital Learning Designer at the University of Dundee, with collaborative help from *ChatGPT 3.5** for the Learning Needs section, and very helpful advice and suggestions from LJ Logue, Learning Technologist & Content Developer at the University of Dundee and Dr. Shirley Hill, Disability Services Manager at the University of Dundee.

</aside>

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WARNING! Always fact-check what Artificial Intelligence produces. It commonly makes mistakes or invents information, such as fake references.

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Learning how to be more human will prepare universities for an AI-mediated future

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Learning how to be more human will prepare universities for an AI-mediated future

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The integration of AI in education is no longer a matter of choice, but a reality that we must embrace. Rather than shying away, we should focus on leveraging the opportunities it presents.

There has been much debate about the future of AI including calls for its development to be paused in the hope that regulatory architecture can catch up with developments in the technology. This piece isn't about that debate, though we need to be mindful that this is the backdrop to the conversation within the higher education sector. But whether the adoption of AI-driven technology advances slowly or quickly, it's clear that it is shaping our present and will continue to shape our future.

It is now over 25 years ago that IBMs DeepBlue beat chess grandmaster Garry Kasparov. Since then, technology, smart devices, AI, and big data have become increasingly sophisticated and pervasive in both our homes and workplaces.

AI represents a significant development in how we research, teach and learn, yet much of the debate within universities has focused on the present threat of AI to maintaining academic standards in assessment. Though these concerns are genuine, the focus of the debate needs to move to the longer term, macro issues of what AI means for universities and academic practice.

Our students will need to be prepared with the skills and attributes they need to thrive in an AI-mediated professional world - so we need to embrace AI in our learning and teaching strategies and our research practice. AI has the potential to reshape academic roles and identities across teaching, research and knowledge transfer activities. Yet the nature of that reshaping will not be found in the technology alone. Instead we need to think about how AI fits into our landscapes. We need to think about the role of the human and the integration and, dare we say it, co-creation, with AI.

Academics are not in competition with machines

Tasks that involve repetition and monotony are prime candidates for technological advancements. For instance, in the field of legal services, where extensive paperwork once required hours of labour from paralegals, AI can now swiftly analyse and summarise documents, answer specific research queries, and drastically reduce the time involved, freeing up time to focus on the human-centric/people side of things, consequently changing the role of the legal professional and the associated skill set required.

Similarly, AI-powered diagnostic systems enable early and efficient patient care, analysing medical scans more accurately and in a fraction of the time a human would take, while robotic surgical procedures simplify complex operations, ushering in undeniable changes to the healthcare sector. Embracing a collaborative relationship with technology to deliver high-quality services is the path to the future.

To ensure our own adaptability, it is essential to enhance our collective emotional intelligence (EQ). Competing directly against data-driven, hyper-efficient computers is impractical. Instead, in future we will need to learn how to work in synergy with technology to deliver quality services. We should focus on leveraging our unique human abilities, such as providing imaginative and innovative solutions, tailored to individuals and their specific needs.

The convergence of EQ-powered humans and AI-powered technology has the potential to revolutionise our lives and work. Amplifying our emotional intelligence means nurturing people skills, including effective communication, persuasive abilities, negotiation tactics, building networks, resilience, curiosity, creativity, autonomy, relationship management, and conflict resolution. These skills have often been referred to as "soft skills" and this needs to change.

At Teesside University our Future Facing Learning approach embeds these human skills across our curriculum. As Europe's first Adobe Creative Campus, we're committed to creative, flexible, and inclusive learning, teaching and assessment and we're excited to see the evolving role of AI in enabling our students' creativity to flourish. By focusing on enhancing our people skills, we can thrive in an era where human interaction and emotional connection remain indispensable, even in the presence of advanced technology.

Critical thinkers, not followers

We are in no doubt that AI will have profound implications for the sector, but our response needs to be measured. AI may disrupt some aspects of academic life, and we need to make some significant changes to embrace it, but it won't replace the university anytime soon.

If we look to the recent past, technologies like Wikipedia were to render universities obsolete, and before that, it was the internet itself that was predicted to bring about the demise. Even the scientific calculator had its fair share of vocal detractors. However, none of these advancements spelled the end for education. Instead, we have learned to coexist with technology and embrace it (particularly during and since the pandemic). AI has the capability to accomplish certain tasks, but that does not mean we should refrain from teaching or having students learn about it - AI is just another tool. We did not stop teaching maths due to the advent of the calculator.

Students must become critical thinkers who can assess the outputs of AI systems and evaluate the nature of knowledge and truth. As academics, for years we've been educating our students about Wikipedia for example - that it's not a reliable source of evidence, of accuracy, or of unbiased truth. That it is important to compare and critically analyse multiple sources to inform our arguments, theories, and work. Given that some AI applications work by sweeping the internet, there is inherent bias in any output. The message remains - this is not a reliable source of evidence, of accuracy, or of unbiased truth.

While access to knowledge and information has become democratised, the ability to utilise it intelligently has become the essence of expertise and intellectual competence. The learning experience should empower students to explore AI, understand its limitations, and consider its potential applications within the context of their respective disciplines. Proficiency in the effective use of AI has become a literacy in its own right. Therefore, it is imperative that we collaborate with students to develop a shared understanding of the impact of AI on knowledge management.

To enable students to effectively work with AI, they must possess the essential skills of asking the right questions, critically analysing information, and applying the responses to maximise their learning gains. AI in education is beyond ChatGPT. Understanding the limitations of a breadth of AI tools and fluently utilising them in conjunction with other sources to ensure credibility and reliability are crucial academic abilities.

The utmost value of universities lies in their role as intellectual hubs, fostering unhampered and open inquiry, constant questioning, and rigorous testing. They are environments of questioning rather than deference, analysis rather than ideology, intellectual openness rather than comfortable exclusion. Universities teach students to think, not merely follow. Our goal is not to have students compile resources and regurgitate conclusions in a structured and robotic manner. "Information" - not knowledge - is readily available and can be accessed and retrieved on demand.

Ironically, as a result of AI, often you don't even need to go looking for information - it finds you. It is therefore more important than ever that we strive to create environments where students are critical: where they can cultivate confidence in their thinking and apply their thoughts, fostering opportunities for imagination, creativity, and curiosity, turning information into knowledge. The value of knowledge lies in asking questions, fostering an insatiable hunger for understanding, and actively engaging in problem finding and problem solving.

Learning that is deeper and more personal

If we shift our focus from relentlessly searching for plagiarism to rewarding originality, we can design assessments that provide students with authentic learning experiences. These assessments immerse students in real-world tasks and practices, enabling them to apply critical thought and take action within a meaningful context.

By doing so, students develop self-reflection skills and have the opportunity to cultivate their personal and professional attributes. They can explore their aspirations, values, and social contributions, discovering their holistic selves through their learning journeys.

Embracing portfolio-based assessments that align with real-world work activities holds significant potential, particularly in partnering with industry to design and deliver learning experiences. Such assessments not only better prepare students for their future endeavours but also foster interconnected and cross-disciplinary learning, mirroring the complexities of real-world challenges.

In order to do this however, we need to rebalance our focus on assessments. Students are assessment driven and AI has the capability to end assessment focused learning. If the sum of the module is the assessment, AI can create this. If, however, the focus is on the educational journey, including the production of a piece of work, this is much harder to replicate within technology alone. We need to strive to focus on the human/computer interaction. We are, after all, social beings.

AI presents opportunities to offer students personalised developmental pathways. To enable a course to adapt to an individual student, it requires students to develop self-awareness of areas they thrive in and areas for growth. It can help students to personalise their student journey and navigate large and complex institutions, technological infrastructures and procedures. Additionally, self-awareness encompasses the ability for students to collaborate with educators in designing curriculum and learning activities, leveraging their expertise and experiences.

To fully harness our students' emerging expertise, we must also design flexible and inclusive assignments that encourage them to integrate experiential knowledge as a scholarly resource. The unique experiences students bring to the classroom, regardless of their academic level, enrich the knowledge landscape and enable them to consider the social and ethical implications of their work. This is an aspect that AI tools cannot replicate. Students' personal experiences and lived understandings enable them to develop visions of equity and justice grounded in reality, potentially transforming scholarly conversations.

It is imperative that we create more opportunities for students at all levels to engage in original research, participate in fieldwork, co-create with peers, conduct interviews, collect data, and leverage their insights and experiences to advance society. These are endeavours that AI tools cannot accomplish or replace, but could be leveraged to assist.

AI literacy for all

Disciplines and professions are different, and so we may need to approach AI in different ways. AI literacies within subject disciplines should be actively built into our curricula and assessment practices - and fast. Preparing students for the world of work, encouraging the use of all digital tools at their disposal (including AI) and designing assessments that encourage the creative use of such tools will deliver revolutionary and disruptive change that is much needed as sectors outside of higher education are embracing the changes and affordances from AI in their practices.

AI literacies refer to the knowledge, skills, and understanding necessary for individuals to effectively engage with and make informed decisions about artificial intelligence technologies. Developing AI literacies involves gaining a deeper understanding of the capabilities, limitations, and ethical considerations associated with AI systems.

Technical understanding: Familiarity with the basic principles, algorithms, and techniques used in AI, such as machine learning, neural networks, and natural language processing. This includes understanding how AI systems are trained, the types of data they require, and the underlying mathematics and statistics involved.

Data literacy: Knowledge of data collection, processing, and analysis methods. This includes understanding data formats, data quality, biases, and privacy issues. Being able to interpret and critically evaluate data used to train AI models is crucial.

Ethical awareness: Understanding the ethical implications and societal impact of AI. This includes awareness of potential biases, discrimination, privacy concerns (think AI-generated imagery and deepfakes), and ethical dilemmas arising from AI deployment. It also involves considering issues like fairness, transparency, accountability, and the potential impact on employment and social structures.

Critical thinking: Developing the ability to evaluate and question AI applications critically. This involves understanding the strengths and limitations of AI systems, recognising their potential risks and biases, and being able to assess the reliability and credibility of AI-generated information.

Human-centred design: Recognising the importance of designing AI systems that align with human values, needs, and preferences. This includes understanding user experience (UX) design principles, considering accessibility and inclusivity, and involving diverse perspectives in the development and deployment of AI technologies.

Collaboration and communication: The ability to work effectively with interdisciplinary teams and communicate AI concepts and implications to a wide range of audiences. This includes being able to explain complex AI concepts in a clear and understandable manner, facilitating discussions, and collaborating on AI-related projects.

Continuous learning: Given the rapid advancements in AI technologies, having a mindset of continuous learning is essential. This involves staying updated with the latest developments, exploring new AI applications, and understanding evolving ethical and legal frameworks.

Ubiquitous AI: AI is already prevalent in many professions - earlier we mentioned the legal and healthcare sectors, but there are many more examples where our graduates will step into roles where AI is unavoidable or where anyone lacking AI literacy within the context of their role/profession will be severely disadvantaged in terms of effectiveness, efficiency, self-development and career progression when compared to their AI literate peers. Consider the role of a researcher or data analyst/scientist (which can span many sectors and professions); essentially roles that collect, analyse and interpret data. AI as a companion to the human is able to carry out a high volume of complex tasks or calculations quickly and accurately, perform visual simulations or predictions based on interpretation of data or a set of parameters, and so on.

Developing these AI literacies helps individuals navigate the increasingly AI-driven world and make informed decisions about AI adoption, while also contributing to responsible and ethical AI development and deployment.

Discussion of AI should not fall into the "digital first" trap. Technology should not be forced into places it does not belong or shoehorned in to satisfy future skills agenda. AI, as with all digital tools, should be embraced, but needs careful consideration as to how this fits into the broader landscape, starting with our greatest assets, our staff.

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Learning how to be more human will prepare universities for an AI-mediated future | Wonkhe

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AI won't replace humans but it will change how higher education works. A team from Teesside University argues that the AI debate should focus on the future

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https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.canadian-accountant.com%2Fcontent%2Fstudents%2Fchatgpt-study-accounting&data=05%7C01%7Csusan.a.smith%40ucl.ac.uk%7Cf4722ca3517b407f222008db71bfa94a%7C1faf88fea9984c5b93c9210a11d9a5c2%7C0%7C0%7C638228840222646503%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=%2BvdT7bg78vrDu1KJz%2BWQoSvZTshWulcLRhz6%2BjPmwks%3D&reserved=0

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Canadian Accountant - Accounting News, Business & Finance

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VANCOUVER - ChatGPT, OpenAI's cutting-edge chatbot, has captured worldwide interest with its advanced capability to understand queries and deliver responses that appear human-like. In response, Microsoft and Google have released their own chatbots, Bing Chat and Bard, to challenge ChatGPT's dominance in the growing field of AI technology. Among the industries most disrupted, education stands out, as it has been reshaped by the use of AI tools, which act as personal tutors and study partners.

Here are five tips (and prompts) that Canadian accounting students can use with ChatGPT to prepare study notes, understanding complex accounting topics, and study for their CPA Canada exams, whether in the Professional Education Program (CPA PEP) or the Common Final Examination (the CFE).

But first: An important note on plagiarism and generative AI

Students are always looking for tools that will give them an advantage in the education program. However, tools are not an easy way to success. Using generative AI tools, such as ChatGPT, to solve assignments and submit as your own work is plagiarism. This is not a grey area. It doesn't matter if you copy from a friend or a chatbot, if you submit work you didn't create, that's plagiarism. CPA Canada has strict rules against cheating and plagiarism; its D2L platform uses plagiarism detection software, such as Turnitin, to detect AI generated content. Beware that generative AI should not be used to solve assignments or fabricate answers, it should be used only for study purposes.

#1: Create flashcards.

ChatGPT can streamline the creation of flashcards to help with your studies. You can choose any accounting subject, ask it to create topics and answers, and even structure it in a columnar format. The results can be exported to Excel and used as digital or physical flashcards. ChatGPT can also adjust the complexity of the content, based on your current level of understanding, making it adaptable to your level of knowledge.
Example prompt: You are a CPA Canada student studying [Management Accounting]. Generate 10 flashcards on [Management Accounting topics]. Output with topics in the first column and answers in the second column. Output as a table.
#2: Act as a peer tester.

One of the best ways to check how well you know a subject is to get quizzed on it. This is a form of active-recall study technique, since you're actively trying to remember and use the information. Active recall helps to reinforce learning and identify areas that you need to brush up. We can ask ChatGPT to adopt the persona of a peer tester and quiz us on a topic we're studying.
Example prompt: You are my peer tester quizzing me on [Canadian Financial Reporting]. Ask 3 questions on the topic: [IFRS 15]
Begin by asking the first question. Wait for my response before asking next question.

#3: Summarize notes.

CPA Canada's PEP modules are fast-paced and accounting students find that there is not enough time to review all the materials. The process of reading chapters, making notes, and summarizing your notes takes substantial time. We can ask ChatGPT to summarise our notes and provide us an overall conclusion to speed up our studies. To make the results more effective, we'll ask it to use bullet points and simpler language.
Example prompt: Summarize the text below using bullet points and simplified language. Include an overall summary at the end. #4: Help to understand solutions.
Getting the final answer may not always be enough to understand how to solve a problem, especially in complex topics, such as Canadian taxation. We can ask ChatGPT for help interpret and simplify answers. Keep in mind that ChatGPT is a language model, not an advanced calculator; it allocates tokens (units of information) to understand and generate text. While ChatGPT can perform basic calculations, its capabilities are limited. I suggest not to use it to for Management Accounting and Finance calculations, as it often gets the answers wrong. Instead, use it for theoretical subjects, such as Financial Reporting, Taxation theory, Strategy & Governance, and Assurance.
Example prompt: Below is a problem and solution on [Canadian Taxation] Explain the solution to me step by step in simple language. [Paste here problem and solution]
#5: Help to memorize.

Each of us has a favourite artist whose name we recall after just one listen of their song or seeing the lyrics. Using ChatGPT, we can write songs in any artist's style about topics that we find difficult to remember. This can be useful for memorizing complex accounting rules, such as finance formulas, taxation rules, and accounting standards.
Example prompt:
Write a very short song in the style of [The Weeknd] that explains [NPV calculation]

Conclusion

Computer aids were looked upon sceptically by some on the teaching side of the profession decades ago. However, as technology continues to evolve and improve, we will see more ChatGPT and other AI-powered chatbots become part of our personal and professional lives. By staying up-to-date and learning how to use emerging technologies, accounting students can increase their academic performance in CPA Canada's education program, as well as their skills in the workplace, and thus become more value-added chartered professional accountants.
Gevorg Grigoryan, CPA, is an instructor, mentor and coach to students in the CPA program. Visit to learn more and subscribe to Gevorg's YouTube channel to access CPA program videos.

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CPA PEP coach and mentor Gevorg Grigoryan presents five tips for using ChatGPT AI to study for Canadian accounting exams like the Common Final Examination.

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https://www.oneusefulthing.org/p/the-homework-apocalypse?utm_source=post-email-title&publication_id=1180644&post_id=132054399&isFreemail=true&utm_medium=email

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Fall is going to be very different this year. Educators need to be ready.

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101 creative ideas to use AI in education

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101 creative ideas to use AI in education

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https://creativehecommunity.wordpress.com/2023/06/23/oa-book-101-creative-ideas-to-use-ai-in-education/

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https://www.hepi.ac.uk/2023/06/16/keep-calm-and-carry-on-using-chat-gpt/

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This HEPI blog post was kindly authored by Ninian Wilson, Press Officer at the Quality Assurance Agency.

It's been a busy time for QAA responding to the rise of generative AI. After three webinars attended by over 2,000 people, discussions with providers across the UK, a brief trip to Tbilisi to share best practice with European quality agency colleagues, one piece of guidance out and another to be published imminently, we have been reflecting on the perspectives of the technical experts, academic staff and students that we brought together. So, what have we learned?
We can't - and shouldn't - ban it
All our event speakers agreed that banning generative AI in tertiary education is not only unfeasible but undesirable, including in assessment. Detection tools lag behind the software itself - we will shortly see this technology embedded in tools we use every day and being able to use it effectively will become a desired skill in the labour market.

That said, we need to avoid scenarios where students and learners are using AI to churn out work without effort or critical thought. In response, we should help students understand the limitations of these tools, appreciate the negative effect that overreliance can have on their own learning experience, and focus assessment on the learning process, not just the output.

Don't panic

This technology, the change it poses to higher education and its implications for academic integrity are not wholly new. During the first of our webinars, Dr Bronwyn Eager likened the emergence of ChatGPT to a fish asking 'what is water?'. From spell check to learning analytic platforms, AI has been the 'water' all around us for some time. It's the open access element that is new.

Much like the advent of the printing press to typewriters and calculators to Google, what start as perceived threats to the status quo quickly become the new status quo. Generative AI tools are the latest of these milestones and Bronwyn encouraged us to see them as a powerful tool rather than an existential threat.

There have always been grey areas in academic misconduct - a parent editing an essay, too much peer exchange, taking inspiration from existing essays online, etc. Generative AI exacerbates this, so it is more important than ever to promote academic integrity amongst students, engage them in authentic assessment design and view uncritical AI use as a symptom rather than cause of a much larger issue.
Access and training for staff and students
With many generative AI tools sitting behind paywalls, our event speakers felt it will become important that providers enable equal access to these tools to prevent more advantaged students benefitting from the technology compared with less advantaged students.

And given how ubiquitous generative AI is going to become, there was discussion of how providers will need to rapidly bring their staff up to speed in AI literacy and skills to support students to use it critically and ethically. Much like the existing academic induction students receive at the start of their courses, using generative AI tools must feature as a prominent induction or indeed standalone module.
Re-thinking the paradigm
While providers respond to the immediate environment, our webinars pointed to a whole stream of higher-level strategic thinking the sector needs to do. One of the most compelling questions that Sarah Eaton posed in her 2021 book, is whether we are entering a post-plagiarism era. With human / AI writing shortly to become the norm, what do we mean by plagiarism? How long will it be before institutions asking students to declare use of generative AI tools in their assessments looks as futile as asking students to declare use of Google? How do we ensure students credit ideas that aren't their own if the idea was generated by a random statistical distribution of words?

For the full picture on these discussions on ChatGPT, check out what the experts had to say in our ChatGPT webinar series. QAA's guidance for institutions on responding to generative AI is also publicly available on the ChatGPT and Artificial Intelligence page on the QAA website.

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Keep calm, and carry on ... using Chat GPT - HEPI

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It's been a busy time for QAA responding to the rise of generative AI. After three webinars attended by over 2,000 people, discussions with providers across the UK, a brief trip to Tbilisi to share best practice with European quality agency colleagues, one piece of guidance out and another to be published imminently, we have [...]

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Assessment and feedback re-designs for the generative AI era

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Assessment and feedback re-designs for the generative AI era

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https://www.slideshare.net/DavidCarless1/assessment-and-feedback-redesigns-for-the-generative-ai-era

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Assessment and feedback re-designs for the generative AI era

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Assessment and feedback re-designs for the generative AI era @CarlessDavid Faculty of Education, HKU CUHK Generative AI Conference June 8, 2023 The University ...

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CRADLE Suggests: Assessment and generative AI

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CRADLE Suggests: Assessment and generative AI

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Student use of machine translation 'a debate we have to have'

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Student use of machine translation 'a debate we have to have'

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https://www.timeshighereducation.com/news/student-use-machine-translation-debate-we-have-have

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Student use of machine translation ‘a debate we have to have’

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Assessment rationales have not kept pace with AI translation and writing support tools, conference hears

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Prompting Higher Education: Towards AI-augmented Teaching and Learning Practice

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Prompting Higher Education: Towards AI-augmented Teaching and Learning Practice

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https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3299&context=jutlp

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Cibyl Student Survey on use of AI (May 2023)

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Cibyl Student Survey on use of AI (May 2023)

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Learning to work with the black box: Pedagogy for a world with artificial intelligence

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Learning to work with the black box: Pedagogy for a world with artificial intelligence

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https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.13337

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I'm a student- you hae no idea how much we're using ChatGPT

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I'm a student- you hae no idea how much we're using ChatGPT

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https://www.chronicle.com/article/im-a-student-you-have-no-idea-how-much-were-using-chatgpt

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Opinion | I’m a Student. You Have No Idea How Much We’re Using ChatGPT.

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No professor or software could ever pick up on it.

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National centre for AI: A generative AI primer

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National centre for AI: A generative AI primer

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https://nationalcentreforai.jiscinvolve.org/wp/2023/05/11/generative-ai-primer/

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Publishing an intro to generative AI is a challenge as things are moving so quickly. However, we think things have now settled down enough for us to bring together information in a single place, to create a short primer. We aim to publish this as a more formal guide that will be updated regularly, but we are posting an initial version as a blog post to get feedback on whether it is useful and if there is other information you would like included.

Version 1.1 - May 22 2023

Table of Contents
2. An Introduction to the Generative AI Technology 2.2. Microsoft Bing Chat, Google Bard and Anthrompic's Claude 2.3. A summary of key capabilities, limitations, and concerns around ChatGPT and other Large Language Models 2.4 Image Generation 2.5 Microsoft Co-pilot and everyday AI 3. Impact of Generative AI on Education 3.1 Assessment 3.1.1 Guidance on advice to students 3.1.2 The role of AI detectors 3.2 Use in Learning and Teaching 3.2.1 Examples of use by students 3.2.2 Examples of use by teaching staff 3.2.3 Examples of uses to avoid 3.3 Adapting curriculum to reflect the use of AI in work and society.
1. Introduction

Generative AI tools such as ChatGPT are already having a significant impact on education. These tools are posing considerable challenges around assessment and academic integrity, but also present opportunities, for example saving staff time by helping with the creation of learning materials or presenting students with new tools to enhance the way they work. The impact of generative AI is being felt far beyond education, and it is already starting to change the way we work. This presents more challenges and opportunities, in making sure education prepares students for an AI-augmented workplace, and that assessments are authentic yet robust.

The primer is intended as a short introduction to generative AI, exploring some of the main points and areas relevant to education, including two main elements:

An introduction to Generative AI technology

The implications of Generative AI on education

2. An Introduction to the Generative AI Technology

Whilst we do not need a detailed technical understanding of the technology to make use of it, some understanding helps us understand its strengths, weaknesses, and issues to consider. In this section, we will be looking into the technology in a little more detail.

This is a fast-moving space, and the information here is likely to age quickly! This edition was written in May 2023, and we aim to update it regularly to take into account significant developments.

This guide starts by looking at AI text generators, also known as Large Language Models (LLMs).

2.1 ChatGPT

ChatGPT has grabbed most of the headlines since its launch in November 2022. It was created by a company called OpenAI, which started as a not-for-profit research organisation (hence the name) but is now a fully commercial company with heavy investment from Microsoft. It is available as a free version, plus a premium version at $20 a month, which provides faster, more reliable access, as well as access to its latest language models and features, including plugins, which changes its behaviour significantly.

ChatGPT is based on a machine learning approach called 'Transformers', first proposed in 2017, and is pre-trained on large chunks of the internet, which gives it the ability to generate text in response to user prompts, hence the name 'Generative Pre-trained Transformer'. Whilst OpenAI provided some information on the approach for training ChatGPT, they haven't so far, released any information about GPT4, the latest model released in early 2023.

In its standard mode, without plugins, ChatGPT works by predicting the next word given a sequence of words. This is important to understand, as it is not in any sense understanding your question and then searching for a result and has no concept of whether the text it is producing is correct. This leads it to be prone to producing plausible untruths or, as they are often known, hallucinations.

As it stands today, the free version of ChatGPT doesn't have access to the internet, so can't answer questions beyond its training data cut-off date of September 2021. Users paying for the ChatGPT Plus service have access to a version that can access the internet.

ChatGPT Plus customers also have access to plugins which extend ChatGPT's functionality. For example, a Wolfram plugin allows users to ask questions which are answered by Wolfram Alpha, which excels at mathematical and scientific information. Initial testing suggests this might resolve the issue of 'hallucination' in these domains. Many other plugins are available, and more are being developed.

OpenAI makes its service available to other developers, so many other applications make use of it, including many writing tools such as Jasper and Writesonic, as well as chatbots in popular applications such as Snapchat.

2.2. Microsoft Bing Chat, Google Bard and Anthrompic's Claude

Although ChatGPT has got most of the hype, there are other players in this space, and this number is likely to increase.

Microsoft has introduced Bing Chat, which is available for testing, and is based on Open AI's GPT-4. It's focused on searching for information and does have access to the internet. It takes your question, performs one or more web searches based on your question, and will then attempt to summarise and answer, giving references for the sites it uses.

Bard is Google's equivalent and is available for testing. Like Bing Chat, it can access the internet, but unlike Bing Chat, it doesn't provide references for the sites it's used to give its answers.

Claude is more similar to ChatGPT and is produced by Anthropic, and is likely to be built into many applications going forward.

At the moment, both Google Bard and Claude feel a long way behind ChatGPT in terms of capabilities, although at the time of writing, Google has just announced significant improvements, so this may change quickly.

2.3. A summary of key capabilities, limitations, and concerns around ChatGPT and other Large Language Models.

In considering generative AI, it's important not only to understand its capabilities but also its limitations. We also believe users should have a broader understanding of the societal impact of generative AI. Some of the key themes are summarised here:

It can generate plausible but incorrect information.

ChatGPT is only trained on information up until Sept 2021 (but those with the paid ChatGPT Plus service have access to a version that can access the internet)

Limited ability to explain the sources of information for its responses (this varies between Chatbots)

2.4 Image Generation

It's not all about text - image generation tools have made huge progress too, particularly with Midjourney, DALL-E 2 and Stable Diffusion.

As for the text generators, these have been trained on information scraped from the internet, and there is a lot of concern about the copyright of the training material.

These work in a similar way to text generators - the user gives a prompt and one or more variations of images are produced.

Image generation capabilities are being incorporated into general AI services, so Bing Chat, for example, can also generate images, using OpenAI's DALL-E 2.

2.5 Microsoft Co-pilot and everyday AI

There is a lot more to generative AI than just accessing it via the ChatGPT web service. The developers of major AI services such as OpenAI make their services available to other developers. One of the most significant announcements has been from Microsoft, who are incorporating generative AI across the Microsoft 365 tools, under the name ' Co-pilot'. At the time of writing (May 2023) the licensing terms haven't been confirmed, but it is likely that this will put generative AI directly into the hands of all our staff and students with access to Microsoft 365. Google have made similar announcements about their office tools.

2.6 Beyond Chatbots

Whilst the focus has been on chatbots and image generation, generative AI is finding it its way into a wide range of tools and services that can potentially be used in education. As an example, Teachermatic can create a range of resources for teachers, including lesson plans and quizzes, Gamma can create websites and presentations, and Curipod can create interactive learning resources. Many of these applications are built on tools provided by OpenAI and powered by the same technology as ChatGPT.

3. Impact of Generative AI on Education

The impact of generative AI in education is still unfolding and is likely to for some time to come. Key areas include assessment and academic integrity, its use in teaching and learning, use as a time-saving tool, and use by students.

Jisc has convened a number of working groups with representatives from universities and colleges to help us collate and present more detailed advice in these areas, particularly around sector-level advice, assessment and advice to students, and we'll share more information from these groups over the coming weeks.

Here we will now give an overview of the core themes.

3.1 Assessment

Initial discussions about generative AI have focused on assessment, with the concern that students will use generative AI to write essays or answer other assignments. This has parallels with concerns around essay mills. These concerns are valid, and whilst essays produced wholly by generative AI are unlikely to get the highest marks, their capability is improving all the time. The ability isn't just limited to essays. ChatGPT is also highly capable of answering multiple-choice questions and will attempt most forms of shorter-form questions. It will often fall short, especially when answers are highly mathematical, although this will not be obvious to the student using the chatbot service.

There are three main options, each with their own challenges. There is broad acceptance that 'embrace and adapt' is the best strategy in most instances.

Avoid
Revert to in-person exams where the use of AI isn't possible

This moves away from authentic assessment and creates many logistical challenges.

Outrun
Devise an assessment that AI can't do.

AI is advancing rapidly and given the time between the assessment being set and it being taken, AI might well be able to do the assignment when it is taken.

Embrace and adapt
Embrace the use of AI, discuss the appropriate use of AI with students, and actively encourage its use to create authentic assessments

Balancing authentic assessment and the use of generative AI with academic integrity is a challenge.

The immediate action is for all staff to engage with generative AI and try it themselves, learning how their assessments will be impacted. Alongside this, institutions will need to consider their strategic approach to AI, review, and update policies, and communicate guidance to students.

For higher education, this aligns with guidance provided by QAA, and for both higher education and further education, general guidance has been provided by the Department for Education. In addition, the Joint Council for Qualifications has produced guidance for protecting quality of qualifications.

3.1.1 Guidance on advice to students

We are seeing the first guidance to students appear. Getting the wording on this can be challenging, and is discussed further in a Jisc blog post ' Considerations on wording when creating advice or policy on AI use '. Our key messages are:

Don't try to 'ban' specific technology or AI as a whole.

Describe acceptable and expected behaviours and provide examples.

The detail of advice will vary between subjects, so supplement general advice with tailored advice for the subject.

A Jisc-convened working group is looking at best practices in this area and will be sharing its outcomes shortly.

3.1.2 The role of AI detectors

Discussions about academic integrity inevitably include the role of AI detection software. There are a number of tools in this space, but for most UK universities and colleges, Turnitin's newly released AI detection tool will be the obvious tool to consider. The key things are:

No AI detection tool can conclusively prove that text was written by AI

These tools will produce false positives.

The tools won't be able to differentiate between legitimate and other use of AI writing tools.

Best practice for using such tools is still being considered and developed across our sectors, and we will aim to provide examples as these develop.

A Jisc blog post explores the concepts and considerations around AI detection in more detail.

3.2 Use in Learning and Teaching

We are seeing a wide range of ideas for how to use generative AI in learning and teaching. It's worth remembering at this point that it is a fast-moving space and many of the tools are still under development or at the beta stage. Whilst exploration makes a lot of sense, it's also worth noting that individuals shouldn't feel the need to rush into using it in teaching - things are changing rapidly, but now is definitely the time to explore and start planning.

3.2.1 Examples of use by students

We welcome the fact that student voices have been brought into the discussion around generative AI, for example ' Generative AI: Lifeline for students or threat to traditional assessment?'. Jisc has run a number of student panels and will be reporting the findings in more detail in the near future.

Examples of uses by students.

To formulate ideas, for example creating essay structures

Generative AI tools are generally effective in producing outlines as a starting point for an assignment.

To provide feedback on writing

Generative AI will proofread and correct text for students, in a similar way to grammar tools. It will also provide feedback on style and content. Students will need clear advice on when this should be declared.

As a research tool

A good understanding of the tool and its limitations is crucial here, particularly the tendency for generative AI to give misinformation.

Generating images to include in assignments.

The best image generation tools come at a cost, and students need to be aware of copyright concerns.

In section 2.3 we noted that digital inequality is a concern. For example, students that pay for ChatGPT Plus get faster, more reliable access, as well as access to the latest model, and those that pay, for example, for MidJourney image generation will get more and better images than most of the free options. At the moment there are limited options for licensing these tools institutionally, but we expect this to change, and consideration should then be given to licensing options to avoid inequality.

3.2.2 Examples of use by teaching staff

If used appropriately, generative AI has the potential to reduce staff workload, for example by assisting with tasks such as lesson plans, learning material creation, and possibly feedback. The key words here are 'appropriately' and 'assisting'. In section 2.3 we noted some of the limitations of generative AI, such as incorrect information, so as things stand today, generative AI can assist by, for example, generating initial ideas, but its output should always be carefully reviewed and adapted.

Use of generative AI may be by directly using a general chat app such as ChatGPT, or maybe via an application built on top of generative AI, such as Teachermatic. In the former case in particular staff will need good prompting skills to make the most of generative AI.

Drafting ideas for lesson plans and other activities

The output may be factually incorrect or lack sound pedagogical foundations. Nonetheless, it may be a useful starting point.

Help with the design of quiz questions or other exercises.

Generative AI can quickly generate multiple-choice quizzes and assessment ideas, but they should be reviewed carefully as above.

Customising materials (simplifying language, adjusting to different reading levels, creating tailored activities for different interests)

Generally, when asked to customize material, generative AI won't introduce new concepts, and so is less likely to introduce factually incorrect information.

Providing custom feedback to students.

At the moment, generative AI should not be used to mark student work, but it can be a useful tool for assisting with personalized feedback.

3.2.3 Examples of uses to avoid

In general, you should avoid posting any work that isn't your own, and any personal information into ChatGPT, as it might well be used as part of the training data.

Things get a little more complex when considering tools built with ChatGPT, and it is almost certain that generative AI built into tools like Microsoft 365 will have the same data privacy protection as the rest of Microsoft 365.

Marking student work

There is no robust evidence of good performance, although ChatGPT will confidently do this if you ask.

Detecting whether work is written by AI

ChatGPT might claim it can detect whether it wrote the text, but it can't

Anything involving personal information

You should never put personal information into any system that your University or College hasn't got a proper contract in place with and made a full assessment of its data privacy policies etc. Generative AI services like ChatGPT are no exception.

3.3 Adapting curriculum to reflect the use of AI in work and society.

We are seeing a broad acknowledgement that work will change too, but understandably limited action around this at the moment.

OpenAI has done some initial research on the impact of LLMs and jobs and has estimated that 'around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted.' A 2021 report by the Department for Business, Energy and Industrial Strategy looks more broadly at jobs affected by AI, both in terms of those likely to be automated and growth areas.

In the short term, we expect generative AI to be quickly adopted into courses where it quickly becomes the norm in the workplace. For example, visual arts make use of generative AI, and computer coding makes use of generative coding tools.

4. Regulation

Given the pace of change, regulation has struggled to keep up. In the UK, the government has published an AI white paper which aims to balance regulation and enabling innovation. The paper makes explicit reference to generative AI and large language models. The US government have produced a Bill of Rights and the EU's AI act is working its way through the European Parliament. The proposed UK approach will look to regulate the use of AI, rather than AI technology itself, through the work of existing regulators, against a common set of principles. As progress is made in this area we will endeavour to highlight any areas of particular concern for education.

5. Summary

Generative AI is progressing rapidly and is likely to have a significant impact on education for the foreseeable future. Keeping up with the advances is a challenge, and balancing authentic assessment and academic integrity is increasingly complex. Nonetheless, with care and an increase in staff and student knowledge, there are substantial gains to be made. This guide aimed to give a broad introduction to generative AI. Much more has been written on the topic, and for those that wish to explore further, we have included a range of resources for further reading.
Keeping updated:
To keep up to date with the work of Jisc's National Centre of AI in Tertiary Education, join our Jisc mail list: aied@jiscmail.ac.uk.
Further Reading:
Generative artificial intelligence in education - Department For Education (March 2023)

Maintaining Quality and Standards in the ChatGPT Era: QAA Advice on the Opportunities and Challenges Posed by Generative Artificial Intelligence - QAA (May 2023)

ChatGPT and Artificial Intelligence in higher education - Unesco (April 2023)

Artificial Intelligence (AI) Use in Assessments: Protecting the Integrity of Qualifications - JCQ (March 2023)
Relevant Jisc Blog Posts:
Staff and student use:
Generative AI: Lifeline for students or threat to traditional assessment? Means. Motive, Opportunity: A Composite Narrative about Academic Misconduct Considerations on wording when creating advice or policy on AI use
AI Detection:
AI writing detectors - concepts and considerations A short experiment in defeating a ChatGPT detector
Bias and other ethical considerations:
Hidden Workers powering AI Exploring the potential for bias in ChatGPT
Using Generative AI:
Getting started with ChatGPT Part 1 - Understanding ChatGPT How to Explore AI Image Generation
Change log

V1.1 - 22 May 2023

We've added a table of contents.

Section 2.1 (ChatGPT) has been updated to reflect the fact that all ChatGPT Plus users have access to plugins and web browsing.

We've added a CC BY-NC-SA license

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A Generative AI Primer - National centre for AI

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The primer is intended as a short introduction to generative AI, exploring some of the main points and areas relevant to education, including two main elements: an introduction to Generative AI technology and the implications of generative AI on education

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How are HE leaders responding to generative AI?

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How are HE leaders responding to generative AI?

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https://www.hepi.ac.uk/2023/05/09/how-are-he-leaders-responding-to-generative-ai/

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ChatGPT: What should assessment look like now?

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ChatGPT: What should assessment look like now?

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https://youtu.be/leWtOVL1nt4

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ChatGPT: What should assessment look like now?

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https://www.iesalc.unesco.org/wp-content/uploads/2023/04/ChatGPT-and-Artificial-Intelligence-in-higher-education-Quick-Start-guide_EN_FINAL.pdf

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Callfor Book Chapters on "AI in Education"! We're inviting scholars, researchers, and practitioners to explore the latest advancements, applications, and implic

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Teaching matters: ChatGPT teaching or ChatGPT cheating? Arguments from a semester with large language models in class (part 1)

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Teaching matters: ChatGPT teaching or ChatGPT cheating? Arguments from a semester with large language models in class (part 1)

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https://www.teaching-matters-blog.ed.ac.uk/chatgpteaching-or-chatgpcheating-arguments-from-a-semester-with-large-language-models-in-class-part-1/

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In part one of a two-post series, Vassilis Galanos discusses attempts at using ChatGPT in the classroom and for essay writing and reflects on the software's limitations but also why it can be seen as an indicator of broader social challenges in education and beyond. This post belongs to the Hot topic series: Moving forward with ChatGPT.
I. Holy Automation, Holy Job Losses, Holy Hidden Work, ChatGPT we rename thee.
Does it matter whether the following text has been produced by a machine? It matters when teaching matters are at stake. OpenAI's GPT-3, GPT-4, and ChatGPT are online artificial intelligence (AI) software that generate original, in terms of predictability and detectability, text by "transforming" material from existing databases according to pre-trained variables (hence the acronym Generative Pre-trained Transformer). They have been widely discussed across academic circles within The University of Edinburgh but also across online fora, such as Twitter, for their potential impact on essay-based and other written teaching assessments because of some impressively satisfying functions: prototyping text, summarising paragraphs, providing lists of sources, writing code, even suggesting essay marks. Like with previous computational techniques attached to educational purposes (computer-aided design, spellcheck, text prediction, Wikipedia articles, search engines), Generative AI applications' use in education carries all bias bugs baggage of algorithmic persuasion and convincing power (Little Britain: "the computer says no!") of either ultimate rationality of the computer programme or wisdom of the crowds (what others have also searched/confirmed).

I teach courses about the sociology and politics of the internet and artificial intelligence (AI) at the School of Social and Political Science while I recently released my PhD thesis on historical and current views on AI expectations. Hence, I felt I should write a few words on teaching with/without ChatGPT or other large language model (LLM)-based applications. For those well versed in AI, these are ancient mantras reiterated with finer nuance. In any event, I promise a few practical suggestions, and a couple of Kraftwerk references.

As usual with all computer- and AI-related innovations, the landscape is divided between enthusiasts (see for example, Adiguzel et al 2023) and doomsayers, with some sceptics navigating between the utopian Scylla and dystopian Charybdis. Educators tend to fall on the second category given the software's admittedly impressive performance in producing text that can pass a mild "Turing Test": it appears critically and academically informed, as if written by a human, at first glance at least. That "first glance" is important. It is telling of an academic culture that demands from students to write more essays, blogposts, and other written exams (read: "produce more text") in shorter time intervals, and educators, teaming up with mighty digital plagiarism detectors, to assess them at ever-growing speed - at least, if they want to squeeze some time to produce more text for their academic development (journal articles, research reports, other administrative documents). Paul Virilio would call this "turbocapitalism"; Knut H. Sørensen and Sharon Traweek might situate this within the context of "academic capitalism" in "questing excellence in academia."
II. "We are the Robots" or "We are the Robots"?
First Kraftwerk reference. In their 1978 song We Are the Robots, techno-pop band Kraftwerk repeat the phrase "we are the robots" 32 times without stressing any of the words (with ambiguity being their defining characteristic; Schüte 2020). Most listeners interpret the song by imagining the stress being on "robots," implying that nonhuman robots (read: ChatGPT-like software) are declaring their existence and come to take over human jobs, legal rights, consciousness, and any precious, socially constructed anthropocentric value that makes humans unique. That is the doomsayers' version. The sceptics' version emerges in the listener's mind if stress is placed on "we." Keeping up with early 20th century metaphors about robots, from Karel Čapek's 1920 theatrical play R.U.R. ( Rossumovi Univerzální Roboti, or, Rossum's Universal Robots) to Thea von Harbou and Fritz Lang's film Metropolis, Kraftwerk play on the ambiguity that the possibility of creating artificial slaves confirms workers' (including academic workers) potential slave or artificiality status. Kraftwerk's retrofuturistic irony did not allow for an enthusiasts' version, and that's what makes them a cool band.

Indeed, if societal standards for assessing essay quality and integrity are mechanised, then we can teach replicator machines to replicate them and produce similar ones. Before ChatGPT's release, I have marked many students essays (and have been paid to produce many reports) that could have passed the test for being produced by a machine if inspected in light of LLMs. Generative AI reminds us that errors, as defined by learning expectations, are signs of creativity; while creativity often occurs by errors. Both movements form an alliance against the misery of being average. To be statistically predictable and replicable is to be average. To get an "average" score in Edinburgh's social science (65/100) is to write statistically probable essays. That is a job that can be performed either:

(a) by students who learn how to feed a social machine that produces and demands "the" average,

(b) by other humans who learn how to replicate this writing style in order to get paid (informally) by students who want to match the averageness standard, and in part nowadays,

(c) by mechanic assistance that based on a vast amounts of examples can produce, if prompted, average-like text. That is something that extensive use of ChatGPT may achieve if used without sufficient creative thinking: a mid- or lower average mark that satisfies everyone's unspoken realisation that universities (might) turn into machines producing the average.

That is what Jean Baudrillard, via Walter Benjamin, would call lack of "aura" in the context of human culture's mechanical reproducibility. A student wrote an essay in pompous defence of ChatGPT's functions for essay polishing, starting with the useful hypothesis that ChatGPT merits credence for essay assistance in English-speaking universities, especially for students who are not native English speakers. The essay would not score very high normally: it was complete with typos, grammatical/citation style mistakes, lack of course-related resources, disconnected arguments. I left the following comment after a very optimistic introduction in which the student committed the typical citation sin of adding an author's first name initials after the surname: "GPT probably didn't help you get rid of superfluous initials in in-text citation! Therefore I can still tell that a human has written this." Because of the attempt, however, the student's honesty, and the will to experiment, the essay received a higher mark for its originality. Originality, after all, can be viewed as the ability to err in a creative way. The aura of erriginality, if I may.

ChatGPT teaches us in this case the difference between creative error and algorithmic glitch - sometimes the difference is subtle, even inconceivable, but nevertheless important. Remember Heidegger: one experiences the existence (dare I: essence) of a technology when it breaks down - and we will be able to go beyond LLMs once we experiment enough, identify its glitches, separate them from the beauty of human error, and consider its practical utility as a tool. Some of us even refuse to use spellcheck, after all.

This brings me to a practical recommendation to assess student performance that is occasioned by ChatGPT but invites creativity - and is an extension of the, now normalised, "I am not a robot" online security tests: I would challenge students and educators altogether to compose essays and design learning outcomes and forms of assessment that demonstrate sufficient inventiveness that cannot be mimicked by machines. (The very word "objective" is problematic, when learning is meant to have subjective value.) To me, it is no coincidence that Cormack McCarthy's latest novel Stella Maris contains multiple references to the 1970s conversational programme ELIZA, trained to respond like a psychoanalyst; three times in the novel a psychiatric patient ridicules the psychiatrist by equating his responses to those by ELIZA.
In the next post, I will offer two more recommendations for teaching with and without ChatGPT that can be beneficial towards both helping educators and students understand what LLMs are all about, but also highlight the value of material beyond these models' radars. References

Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429.

Baudrillard, J. (1981). Simulacra and Simulation. Ann Arbor: The University of Michigan Press.

Galanos, V. (2023). Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding. Doctoral Thesis. Edinburgh: University of Edinburgh.

Joyce, J. (1939[1992]). Finnegans Wake. London: Penguin Books.

McCarthy, C. (2023). Stella Maris. Gyldendal A/S.

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., ... & Gebru, T. (2019, January). Model cards for model reporting. In Proceedings of the conference on Fairness, Accountability, and Transparency, Association for Computing Machinery (pp. 220-229).

Schüte, U. (2020). Kraftwerk: Future Music from Gernamy. Great Britain: Penguin Books.

Sørensen, K. H., & Traweek, S. (2022). Questing Excellence in Academia: A Tale of Two Universities (p. 236). Taylor & Francis.

Turkle, S. (1980). Computer as roschach. Society, 17(2), 15-24.

Virilio, P. (2012). The Great Accelerator. Translated by Julie Rose. Cambridge and Malden: Polity.

Vassilis Galanos

Vassilis Galanos (it/ve/vem) is a Teaching Fellow in Science, Technology and Innovation Studies at the School of Social and Political Science, University of Edinburgh and associate editor of the journal Technology Analysis and Strategic Management. Vassilis researches and publishes on the interplay of expectations and expertise in the development of AI, robotics, and internet technologies, with further interests in cybernetics, media theory, invented religions, oriental and continental philosophy, community-led initiatives, and art. Vassilis is also a book, vinyl, beer cap, and mouth harp collector - using the latter instrument to invite students back from class break.
Twitter handle: @fractaloidconvo

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ChatGPTeaching or ChatGPCheating? Arguments from a semester with large language models in class (Part 1)

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In part one of a two-post series, Vassilis Galanos discusses attempts at using ChatGPT in the classroom and for essay writing and reflects on the software's limitations but also why it can be seen as an indicator of broader social challenges in education and beyond.

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The ChatGPT Artificial Intelligence Chatbot: How Well Does It Answer Accounting Assessment Questions?

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The ChatGPT Artificial Intelligence Chatbot: How Well Does It Answer Accounting Assessment Questions?

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https://publications.aaahq.org/iae/article/doi/10.2308/ISSUES-2023-013/10903/The-ChatGPT-Artificial-Intelligence-Chatbot-How?searchresult=1

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How ChatGPT can help disrupt assessment overload

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How ChatGPT can help disrupt assessment overload

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https://www.timeshighereducation.com/campus/how-chatgpt-can-help-disrupt-assessment-overload

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Recent developments in generative AI - not least the notorious ChatGPT - offer opportunities to stimulate long-overdue reforms to assessment practices. But these reforms need careful thought and principled implementation within an already hard-pressed sector. To make space for these developments, now is an opportune moment to tackle the issue of assessment overload, too many assessments and limited opportunities for students to revise work iteratively.

Modularised higher education has led to an assessment arms race in which academics inadvertently compete for student attention using grades as both carrot and stick. Students often end up jumping through hoops rather than achieving sustainable learning, and an inevitable consequence is that if something is not assessed then students won't do the work.

Meanwhile, end-of-semester bottlenecks, with students juggling multiple deadlines, lead to rushed work or inadvertently encourage students to take shortcuts through copying, plagiarism or outsourcing to humans or bots

Reducing the assessment burden could support trust in students as individuals wanting to produce worthwhile, original work. Indeed, students can be co-opted as partners in designing their own assessment tasks, so they can produce something meaningful to them.

If we were to tackle assessment overload in these ways, it opens up various possibilities. Most significantly there is potential to revitalise feedback so that it becomes a core part of a learning cycle rather than an adjunct at its end. End-of-semester, product-oriented feedback, which comes after grades have already been awarded, fails to encourage the iterative loops and spirals typical of productive learning.

If you found this interesting and want advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the THE Campus newsletter.

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How ChatGPT can help disrupt assessment overload

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Recent developments in generative AI - not least the notorious ChatGPT - offer opportunities to stimulate long-overdue reforms to assessment practices. But these reforms need careful thought and principled implementation within an already hard-pressed sector. To make space for these developments, now is an opportune moment to tackle the issue of assessment overload, too many assessments and limited opportunities for students to revise work iteratively.

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https://m.soundcloud.com/user-542495654-897696505/chat-gpt-in-higher-education-a-curse-a-blessing-or-an-inevitability

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https://www.monash.edu/learning-teaching/teachhq/Teaching-practices/artificial-intelligence/generative-ai-and-assessment#3173657

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A key purpose of assessment is to yield evidence of student learning. This evidence depends on submitted work accurately representing each student’s contribution.

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Chat GPT in higher education: Considerations for academic integrity and student learning

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Chat GPT in higher education: Considerations for academic integrity and student learning

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https://journals.sfu.ca/jalt/index.php/jalt/article/view/731/559

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View of ChatGPT in higher education: Considerations for academic integrity and student learning | Journal of Applied Learning and Teaching

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Bron Eager's website - practical uses for AI in education 

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Bron Eager's website - practical uses for AI in education 

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https://broneager.com/category/ai

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Chat GPT- How do I use it as a force for good

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Chat GPT- How do I use it as a force for good

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https://youtu.be/lV_fPtOG4iE

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ChatGPT: How do I use it as a force for good?

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Gamma- AI assistance in generating slide decks

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Gamma- AI assistance in generating slide decks

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Gamma | Best AI Presentation Maker & Website Builder

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Generative AI: Friend or Foe? (Video 2- Implications)

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In the second video about generative AI, Martin Compton from Arena builds on discussions with a colleague, Professor Susan Smith, and explores whether generative AI is a friend or enemy. He acknowledges the power and remarkable capabilities of AI tools like ChatGPT (a large language model text generator) and Midjourney, an AI image generator.

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Generative AI: Friend or Foe (Video 1- What?)

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In this video, Martin Compton from Arena discusses the phenomenon of generative AI, using Chat GPT as a prime example. He addresses the question of whether generative AI is a friend or foe, and suggests that how we react, utilise, and learn from these technologies will determine the outcome.

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Lucy Goetz, a student at Concord High School in California helped tech columnist Geoffrey A. Fowler test Turnitin's AI detector. She was surprised to discover it erroneously flagged part of her original essay as created by AI. (Andria Lo for The Washington Post)

High school senior Lucy Goetz got the highest possible grade on an original essay she wrote about socialism. So imagine her surprise when I told her that a new kind of educational software I've been testing claimed she got help from artificial intelligence.
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A new AI-writing detector from Turnitin - whose software is already used by 2.1 million teachers to spot plagiarism - flagged the end of her essay as likely being generated by ChatGPT.

"Say what?" says Goetz, who swears she didn't use the AI writing tool to cheat. "I'm glad I have good relationships with my teachers."

After months of sounding the alarm about students using AI apps that can churn out essays and assignments, teachers are getting AI technology of their own. On April 4, Turnitin is activating the software I tested for some 10,700 secondary and higher-educational institutions, assigning "generated by AI" scores and sentence-by-sentence analysis to student work. It joins a handful of other free detectors already online. For many teachers I've been hearing from, AI detection offers a weapon to deter a 21st-century form of cheating.

But AI alone won't solve the problem AI created. The flag on a portion of Goetz's essay was an outlier, but shows detectors can sometimes get it wrong - with potentially disastrous consequences for students. Detectors are being introduced before they've been widely vetted, yet AI tech is moving so fast, any tool is likely already out of date.

It's a pivotal moment for educators: Ignore AI and cheating could go rampant. Yet even Turnitin's executives tell me that treating AI purely as the enemy of education makes about as much sense in the long run as trying to ban calculators.

Ahead of Turnitin's launch this week, the company says 2 percent of customers have asked it not to display the AI writing score on student work. That includes a "significant majority" of universities in the United Kingdom, according to UCISA, a professional body for digital educators.

To see what's at stake, I asked Turnitin for early access to its software. Five high school students, including Goetz, volunteered to help me test it by creating 16 samples of real, AI-fabricated and mixed-source essays to run past Turnitin's detector.

The result? It got over half of them at least partly wrong. Turnitin accurately identified six of the 16 - but failed on three, including a flag on 8 percent of Goetz's original essay. And I'd give it only partial credit on the remaining seven, where it was directionally correct but misidentified some portion of ChatGPT-generated or mixed-source writing.

Turnitin claims its detector is 98 percent accurate overall. And it says situations such as what happened with Goetz's essay, known as a false positive, happen less than 1 percent of the time, according to its own tests.

Turnitin also says its scores should be treated as an indication, not an accusation. Still, will millions of teachers understand they should treat AI scores as anything other than fact? After my conversations with the company, it added a caution flag to its score that reads, "Percentage may not indicate cheating. Review required."

"Our job is to create directionally correct information for the teacher to prompt a conversation," Turnitin chief product officer Annie Chechitelli tells me. "I'm confident enough to put it out in the market, as long as we're continuing to educate educators on how to use the data." She says the company will keep adjusting its software based on feedback and new AI advancements.

The question is whether that will be enough. "The fact that the Turnitin system for flagging AI text doesn't work all the time is concerning," says Rebecca Dell, who teaches Goetz's AP English class in Concord, Calif. "I'm not sure how schools will be able to definitively use the checker as 'evidence' of students using unoriginal work."

Unlike accusations of plagiarism, AI cheating has no source document to reference as proof. "This leaves the door open for teacher bias to creep in," says Dell.

For students, that makes the prospect of being accused of AI cheating especially scary. "There is no way to prove that you didn't cheat unless your teacher knows your writing style, or trusts you as a student," says Goetz.

Why detecting AI is so hard

Spotting AI writing sounds deceptively simple. When a colleague recently asked me if I could detect the difference between real and ChatGPT-generated emails, I didn't perform very well.

Detecting AI writing with software involves statistics. And statistically speaking, the thing that makes AI distinct from humans is that it's "extremely consistently average," says Eric Wang, Turnitin's vice president of AI.

Systems such as ChatGPT work like a sophisticated version of auto-complete, looking for the most probable word to write next. "That's actually the reason why it reads so naturally: AI writing is the most probable subset of human writing," he says.

Turnitin's detector "identifies when writing is too consistently average," Wang says.

The challenge is that sometimes a human writer may actually look consistently average.

On economics, math and lab reports, students tend to hew to set styles, meaning they're more likely to be misidentified as AI writing, says Wang. That's likely why Turnitin erroneously flagged Goetz's essay, which veered into economics. ("My teachers have always been fairly impressed with my writing," says Goetz.)

Wang says Turnitin worked to tune its systems to err on the side of requiring higher confidence before flagging a sentence as AI. I saw that develop in real time: I first tested Goetz's essay in late January, and the software identified much more of it - about 50 percent - as being AI generated. Turnitin ran my samples through its system again in late March, and that time only flagged 8 percent of Goetz's essay as AI-generated.

But tightening up the software's tolerance came with a cost: Across the second test of my samples, Turnitin missed more actual AI writing. "We're really emphasizing student safety," says Chechitelli.

Say hello to your new tutor: It's ChatGPT

Turnitin does perform better than other public AI detectors I tested. One introduced in February by OpenAI, the company that invented ChatGPT, got eight of our 16 test samples wrong. (Independent tests of other detectors have declared they " fail spectacularly.")

Turnitin's detector faces other important technical limitations, too. In the six samples it got completely right, they were all clearly 100 percent student work or produced by ChatGPT. But when I tested it with essays from mixed AI and human sources, it often misidentified the individual sentences or missed the human part entirely. And it couldn't spot the ChatGPT in papers we ran through Quillbot, a paraphrasing program that remixes sentences.

What's more, Turnitin's detector may already be behind the state of the AI art. My student helpers created samples with ChatGPT, but since they did the writing, the app has gotten a software update called GPT-4 with more creative and stylistic capabilities. Google also introduced a new AI bot called Bard. Wang says addressing them is on his road map.

Some AI experts say any detection efforts are at best setting up an arms race between cheaters and detectors. "I don't think a detector is long-term reliable," says Jim Fan, an AI scientist at Nvidia who used to work at OpenAI and Google.

"The AI will get better, and will write in ways more and more like humans. It is pretty safe to say that all of these little quirks of language models will be reduced over time," he says.

Is detecting AI a good idea?

Given the potential - even at 1 percent - of being wrong, why release an AI detector into software that will touch so many students?

"Teachers want deterrence," says Chechitelli. They're extremely worried about AI and helping them see the scale of the actual problem will "bring down the temperature."

Some educators worry it will actually raise the temperature.

Mitchel Sollenberger, the associate provost for digital education at the University of Michigan-Dearborn, is among the officials who asked Turnitin not to activate AI detection for his campus at its initial launch.

He has specific concerns about how false positives on the roughly 20,000 student papers his faculty run through Turnitin each semester could lead to baseless academic-integrity investigations. "Faculty shouldn't have to be expert in a third-party software system - they shouldn't necessarily have to understand every nuance," he says.

Ian Linkletter, who serves as emerging technology and open-education librarian at the British Columbia Institute of Technology, says the push for AI detectors reminds him of the debate about AI exam proctoring during pandemic virtual learning.

"I am worried they're marketing it as a precision product, but they're using dodgy language about how it shouldn't be used to make decisions," he says. "They're working at an accelerated pace not because there is any desperation to get the product out but because they're terrified their existing product is becoming obsolete."

Said Chechitelli: "We are committed to transparency with the community and have been clear about the need to continue iterating on the user experience as we learn more from students and educators.

Deborah Green, CEO of UCISA in the U.K., tells me she understands and appreciates Turnitin's motives for the detector. "What we need is time to satisfy ourselves as to the accuracy, the reliability and particularly the suitability of any tool of this nature."

It's not clear how the idea of an AI detector fits into where AI is headed in education. "In some academic disciplines, AI tools are already being used in the classroom and in assessment," says Green. "The emerging view in many U.K. universities is that with AI already being used in many professions and areas of business, students actually need to develop the critical thinking skills and competencies to use and apply AI well."

There's a lot more subtlety to how students might use AI than a detector can flag today.

My student tests included a sample of an original student essay written in Spanish, then translated into English with ChatGPT. In that case, what should count: the ideas or the words? What if the student was struggling with English as a second language? (In our test, Turnitin's detector appeared to miss the AI writing, and flagged none of it.)

Would it be more or less acceptable if a student asked ChatGPT to outline all the ideas for an assignment, and then wrote the actual words themselves?

"That's the most interesting and most important conversation to be having in the next six months to a year - and one we've been having with instructors ourselves," says Chechitelli.

"We really feel strongly that visibility, transparency and integrity are the foundations of the conversations we want to have next around how this technology is going to be used," says Wang.

For Dell, the California teacher, the foundation of AI in the classroom is an open conversation with her students.

When ChatGPT first started making headlines in December, Dell focused an entire lesson with Goetz's English class on what ChatGPT is, and isn't good for. She asked it to write an essay for an English prompt her students had already completed themselves, and then the class analyzed the AI's performance.

The AI wasn't very good.

"Part of convincing kids not to cheat is making them understand what we ask them to do is important for them," said Dell.

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Analysis | We tested a new ChatGPT-detector for teachers. It flagged an innocent student.

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Five high school students helped our tech columnist test a ChatGPT detector coming from Turnitin to 2.1 million teachers. It missed enough to get someone in trouble.

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https://www.tandfonline.com/doi/full/10.1080/14703297.2023.2195846

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https://www.gov.uk/government/publications/generative-artificial-intelligence-in-education

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Generative artificial intelligence (AI) in education

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The Department for Education’s (DfE) position on the use of generative AI in the education sector.

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https://www.linkedin.com/posts/suebeckingham_chatgpt-gpt4-activity-7041885534050263040-cA8M/

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Sue Beckingham NTF SFHEA CMBE FSEDA on LinkedIn: #chatgpt #gpt4 | 100 comments

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A key concern relating to ChatGPT is academic integrity. I asked the latest GPT4 "To avoid academic integrity issues what alternative assessment might be... | 100 comments on LinkedIn

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Chat GPT bug leaked users' conversation histories

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Chat GPT bug leaked users' conversation histories

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https://www.bbc.co.uk/news/technology-65047304

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ChatGPT bug leaked users’ conversation histories

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Social media users had shared images of chat histories with AI conversations they said were not theirs.

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Will ChatGPT turn education back to Socrates?

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​Socrates did not think much of writing. Writing, he thought, leads to fake understanding. Real understanding, by contrast, requires face-to-face dialogue. Anyone, he says to his companion Phaedrus, can borrow a scroll from the library, read it out loud and sound wise without having to go through the education required to actually be wise.

Socrates saw himself ​as an educator but his focus was not on transmitting knowledge so much as on teaching his students how to ask better questions. He described himself as wise only in the sense that he knew just how little he knew whereas all the other people in Athens seemed to think that they knew lots of stuff for certain until he questioned them about it. Meno, for example, claimed that he knew what virtue is but when challenged by Socrates to define it was stunned into silence. Meno then described Socrates as being like the torpedo fish that delivers an electric shock that paralyses its victims. Socrates accepts this metaphor and reversions it, demonstrating, with an illustration, that the shock of his questions is not intended to silence people but to wake them up: provoking them to think again and so to become more aware not only of the fact that they do not know what they are talking about but also to become more aware of things that they find that they do know really when they are forced to think more deeply.

Socrates was not one of those who like to problematise everything just for the sake of it. His questioning was motivated, he claimed, by love. In the Apology Socrates tells us that he did not teach the youth of Athens to ask critical questions in order to undermine order but in order to help them and the whole community to flourish. For Socrates dialogue is the best way to find the Good: a concept which, for Socrates, was inseparably bound up with the linked concepts of beauty and truth.

Generative AI in the form of ChatGPT3 and GPT-4 is now daily showing us how right Socrates was to question the educational value of writing. If students can pass their exams and score high marks by handing in essays generated by digital algorithms then clearly writing is not and should not be the main goal of education.

There is an important distinction made in the philosophy of education which might illuminate the challenge to education posed by ChatGPT, the distinction between training and education. Education, according to the liberal education tradition, should go beyond training to encourage critical thinking, creativity and freedom of thought. This distinction has sometimes been misunderstood as meaning that we should value a more academic education, studying Plato for example, over a more vocational education, perhaps studying computer engineering. But that is a mistake. You can't think without thinking about something or be creative in a vacuum. Whatever you do in life requires learning knowledge and skills that are best acquired through imitation and training. The truly educational bit of education goes beyond that training element to teach you how to question what you have learnt in order to understand it better and so to be able to move forward creatively regardless of whether your focus is how to build a better widget or how to write a better essay.

ChatGPT is a shock to the system. If ChatGPT can do well in exams then perhaps much of what has been passed off as 'education', even in elite universities like Cambridge, even in 'academic' subjects like philosophy, is actually just training. Our rubrics when marking essays point to the liberal education ideal with phrases like 'demonstrates critical awareness', 'develops a personal synthesis' and even 'shows signs of original thought' but actually teachers know that all of these phrases correspond to particular ways of writing which we can train students to emulate. They send us essays, we pick at them and send them back and so on in an iterative cycle until the students, or those motivated enough to stick with the programme, are trained to write just like us and just like every other academic in our area. This is very similar to the way in which ChatGPT is trained which is perhaps why it can learn to write academic texts even better than academics.

Why we are different from AI

I am not totally disillusioned with our so-called education system. I know that there can be more to education than just teaching students how to write essays in the way that I was once taught to write essays. I see this sometimes in the shining eyes and enthusiasm of students who have just had an insight that changes the way that they see things. Such insights happen not in the written text itself but in dialogues; in the dialogic space of new possibilities that opens up in the clash between voices or the clash between texts. Real education seems to imply a change in identity, or at least in perspective and also some sort of sense of an expansion of awareness to see things clearly that previously were opaque . This breakthrough or insight experience is just as relevant to more obviously vocational subjects like media studies, medicine, nursing, law, business, engineering and education as to more apparently 'academic' subjects like social theory or philosophy.

AI can be trained but it cannot be 'educated'. This is because if you look into how ChatGPT works you find a set of algorithms with no understanding whatsoever. In the face of persistent claims that computers could become intelligent the philosopher John Searle spelt out the difference between how computers process information and how humans think with a thought experiment that he called the Chinese Room. The example goes like this: Imagine that you are locked in a room with a book of rules for manipulating symbols. You do not know any Chinese, but you can follow the rules in the book. Someone outside the room slides papers with Chinese characters under the door. You look up the symbols in the book and follow the instructions to produce an appropriate response. You then slide the paper back under the door. To someone outside, it may seem that you understand Chinese, but you actually do not.

That is the difference between AI and humans: AI does not understand what it is reading because it is not conscious; humans at least have the potential to understand because they are, or at least have the potential to be, conscious.

Dialogic education with AI

But what does this little word 'conscious' really mean in practice? Socrates nailed it with his example of a kind of questioning that is like an electric shock, waking the student up to see things differently. To become more conscious of something, to be able to understand it, means to question it, to challenge it, to see it from an outside perspective. When you are led by a dialogue to see something from an outside perspective you become more conscious of it. Only by seeing things as if from the outside, as if from a distance, can you begin to 'understand' them. AI cannot do that but you can. To teach students to be able to do something different from what AI can do we need to teach them how to question and how to engage critically, creatively, empathetically and productively in dialogue.

Dialogic education does not need to be complicated, in essence it is drawing students into dialogue, asking them questions, getting them to ask questions in turn and having as your goal not just knowing about stuff but being able to ask better questions about stuff.

ChatGPT or other generative AI can help with this. In every area of study, often especially in more obviously vocational subjects, it can reset the balance between training and education. At the moment the needs of training students so that they can pass required tests often overwhelms the more educational aspects of the curriculum. Conversations with AI tutors have the potential to make the required knowledge part of any curriculum much more fluid and accelerated, liberating more time and resource to the education bit; asking questions, exploring alternatives, engaging in dialogue and being creative.

Based on web-trawling the current version of ChatGPT, ChatGPT3 is trained on 300 billion words, using a statistical model with 195 billion parameters. The "parameters" in this context refer to the weights and biases of the neural network that makes up the model. These parameters are essentially the "knowledge" that the model has acquired through its training process, and they allow the model to make predictions about which words are most likely to appear next in a given sentence. The new version out today, GPT4, is even larger and more powerful. Because GPT is trained on content gleaned from almost the whole web it represents the almost up to date 'knowledge' of the Internet in a form that we can engage in dialogue with. Because of its generalist nature and programming to be plausible rather than accurate it is not always reliable in every area but it is easy to see how more focussed generative AIs trained with only vetted data will arise to help tutor students in specific areas like statistics for example, or law where precision is required.

Whether used as a generalist essay writer or as an expert tutor, generative AI can be a voice in an educational dialogue. If you want to say something original in an essay why not check first what ChatGPT or other AI, has to say and then see if you can add to that or critique it and go beyond it. Then check again what the AI thinks about what you have written, ask it what questions could be raised about your text and then try again iteratively improving your text in dialogue with the AI.

I am not saying that we can simply return to the pedagogy of Socrates. 5th Century BCE Athens was a different time and place. But the basic insight of Socrates, that we should teach students how to ask better questions , seems like a timely response to the educational challenge posed by ChatGPT3.

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Will ChatGPT turn education back to Socrates?

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​Socrates did not think much of writing. Writing, he thought, leads to fake understanding. Real understanding, by contrast, requires face-to-face dialogue. Anyone, he says to his companion Phaedrus, can borrow a scroll from the library, read it out loud and sound wise without having to go through the education required to actually be wise.

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While some academics embrace large language models in higher education, Angelo Pirrone suggests that take-home assessments should be given a marginal role in favour of oral exams and classroom assignments, which mitigate or avoid the risks posed by AI tools such as ChatGPT

We're looking at how switching to oral exams could mitigate the plagiarism risks of AI tool use in take-home assessment: https://t.co/SYlgDaS3oA As an educator, what would deter you from using more oral exams to assess your students?

- LSE Higher Education Blog (@LSEHEBlog) March 23, 2023

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The promise and curse of AI

For better or for worse, our world is being revolutionised by artificial intelligence (AI). For instance, in medicine, AI systems are aiding radiologists in detecting medical abnormalities. In other areas, opinions regarding the role of AI are more controversial. Take the case of the visual arts where AI has opened up new possibilities; in the news, we hear about AI winning an art contest and the polarising reactions that experts and the general public have about AI-generated art. Similarly, academic publishing is dealing with the influence of AI systems that can generate scientific articles.

Despite plenty of arguably positive or potentially positive applications in many domains, AI (mis)use is accelerating what even a few years ago would have been considered a dystopian nightmare - from self-driving cars that can re-possess themselves if the owner misses a payment, to the sophistication of AI surveillance that is invading our daily life, to AI-assisted police sketches that reinforce racial bias, and AI systems that reinforce sexism or revive dangerous pseudo-sciences such as physiognomy.

The focus of this post is the disruptive and damaging effect of large language models on academic learning and assessment. Large language models are one of the most hyped AI applications and their risks are currently a hot topic of debate. As an example of large language models, I will refer to ChatGPT, arguably the most celebrated large language model to date and the most discussed AI system in both academic and non-academic circles.

An existential threat to the take-home assignment

In many academic systems such as the British or American ones, a commonly adopted type of academic assessment is the take-home assignment. Students complete an assignment outside the classroom, outside timetabled hours, with no invigilation, over a few hours or days, using their own equipment and software. This is a form of assessment that can accommodate most students. The rationale behind these take-home assignments is that, in engaging with the course materials and additional sources while preparing the assignment, the assignment is seen as evidence of learning and understanding and allows students to demonstrate their knowledge and abilities. Assignments generally include essays, reviews, and reports and will vary from course to course.

Academic systems that rely on these take-home assignments are greatly affected by ChatGPT and large language models in general, given that students could use AI systems such as ChatGPT to produce their assignments. A simple web search shows a multitude of posts on websites in which students discuss strategies to avoid plagiarism when using large language models and how to use additional software for masking the output of ChatGPT or extending it - for instance by adding bibliographic references, which ChatGPT (at the time of writing this post) does not generate.

Limiting the role of AI in academia should not be seen as punishing students or making life harder for them

I would like to highlight that it is not the output per se that matters, but the active learning that takes place while generating the assignment that facilitates understanding and knowledge. Therefore, the use of large language models to produce academic assignments disrupts student learning, and is also a violation of academic integrity. While there is often a grey area between plagiarism and academic support (for instance, it is fine to use a grammar or spelling check tool), generating a response to the assignment prompt using AI misses the purpose of education as students are no longer involved in the active learning process that justifies these assignments in the first place. Put in other words, students should be evaluated on their own work, not that of ChatGPT.

Limiting the role of AI in academia should not be seen as punishing students or making life harder for them; on the contrary, it is a way to protect active learning and the very purpose of higher education in the hope that scores on assignments do not become the sole focus for students. Universities could use the services of any computer programmer to develop systems that detect the use of large language models, and in some cases such tools already exist. However, in the long run, this is not a viable route. In an endless arms race between AI and detection software, new AI systems will be developed to circumvent detection software, and new detection software will be developed to detect AI-generated content.

We should rethink academic assessment so that reliance on intrusive and damaging technology is minimised

Resisting AI

We need simple but effective solutions, rather than having to outsmart each AI system on a case-by-case basis. A key aspect of operational security is to avoid reliance on unnecessary technology whenever possible. For instance, concern about corporations hoarding personal data can be mitigated by using self-hosted, libre software rather than proprietary software. Similarly, we should rethink academic assessment so that reliance on intrusive and damaging technology is minimised. Some countries are already discussing and implementing similar proposals. Some Australian universities are dealing with the risks to academic integrity brought by large language models by re-adopting paper and pen assignments in certain cases.

I see two ways in which this principle could be translated into the classroom - oral exams and invigilated exams completed in the classroom. In both cases, there is still room for take-home assignments, but their overall role and impact are significantly decreased. In invigilated assignments or oral exams, students could expand on and build on the take-home assignment or engage with different aspects of the course material.

Like every other form of assessment, oral exams have their pros and cons which have been described and discussed in detail in the literature. Oral exams are an opportunity for students to learn to communicate verbally and to do so extemporaneously, often without rehearsal or preparation - a skill that is going to be appreciated in the workplace. On the negative side, oral exams are more time-consuming and may not be feasible for introductory courses and courses with a high number of students. For those courses, classroom exams coupled with take-home assignments may be more appropriate. That said, I see no a priori reasons to oppose oral exams. Elsewhere, for example in Germany or Italy, oral exams have long been an important type of assessment in higher education and surveys show that, on average, students are supportive of oral exams.

In Germany or Italy, oral exams have long been an important type of assessment in higher education

One of the common critiques of oral exams is that students may be evaluated according to different questions and this may lead to unfair outcomes for students. From this perspective, standardised assignments are a fairer form of assessment as students are presented with the same set of questions. However, I personally believe that precisely due to their unstructured nature, oral exams allow a more genuine appreciation of students' knowledge and abilities. If anything, the fact that oral exams allow teachers to adjust questions on the spot to assess students' knowledge is what makes this form of exam AI-proof and an attractive substitute to take-home assignments. While at first the reply to a topic from large language models (aka stochastic parrots) may seem impressive, subsequent systematically ambiguous replies indicate a system that probabilistically combines information without reference to meaning.

Precisely due to their unstructured nature, oral exams allow a more genuine appreciation of students' knowledge and abilities

Both oral and in-classroom exams are naturally AI resistant; for oral exams, it is straightforward to understand why. In the future, AI systems could be devised that disrupt even this form of assessment (say, auditory devices) but practically we are far from such applications. For classroom assignments that require the use of computers, university system administrators could block specific websites (eg known AI chatbots) or limit internet access to all programs other than those needed to complete and submit the assignment. These proposals limit the potential impact of AI systems during classroom exams. That said, we should not be overzealous in imposing limits or we risk giving exams too much importance.

Regarding inclusivity, another often mentioned limitation of oral exams, no single exam type can accommodate all students. While an oral exam may be a better prospect for dysgraphic students or students with non-verbal learning disorders, it may disproportionately disadvantage students with specific types of social disabilities. However, it should be noted that, contrary to popular opinion, introverted students do not always have a problem with public speaking or oral exams. Special care should be exercised to recognise cases in which oral exams may not be suitable and make sure that options are put in place; this could take the form of specially designed oral exams (eg so that a series of questions are known in advance), or the possibility to replace oral exams with other forms of exams such as in-classroom assignments. Similar adjustments should be made for in-classroom exams when necessary; computer equipment with support software should be offered to students that require it.

Special care should be exercised to recognise cases in which oral exams may not be suitable and make sure that options are put in place

As a closing note, if the form of assessment is the oral or in-classroom exam, then large language models could even be used by students to foster active learning, together with activities such as classroom discussions that are known to promote active learning. Again, the risk that needs to be avoided is to equate learning exclusively with performance on written assignments given that nowadays written assignments can be entirely produced by AI systems. We often hear or read that it is inevitable that most aspects of our life are going to be affected or even dominated by AI. While in some cases that may be acceptable, in others, such as the case of academic assessment, our responsibility as teachers, lecturers, and students may be to resist AI rather than embrace it.

Image source: main photo by Clem Onojeghuo on Unsplash

This post is opinion-based and does not reflect the views of the London School of Economics and Political Science or any of its constituent departments and divisions.

About the author

Posted In: AI | Assessment | Pedagogy to Practice

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Resist AI by rethinking assessment | LSE Higher Education

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Should oral and classroom exams play a greater role in HE assessment to avoid the risks posed by AI tools such as ChatGPT?

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https://wonkhe.com/blogs/like-clippy-only-on-steroids/

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Until last week, the response from the sector on the rise of generative AI was focused on thinking about Chat-GPT.

Based on GPT-3, the version of OpenAI's large language model that most have played with does not have access to the live internet, cannot access information updated after 2021, and has been quaintly relying on "thumbs up / thumbs down" validation from users to know, and then learn, if a response is correct.

It has no internet lookup function, can't access search engines or library databases, and can't source references. If it doesn't know an answer, unless you use the right prompts, it just makes it up - in a pretty convincing manner.

As such much of the debate has focussed in two directions - on detection, on the basis that students might use it to cheat, and on integration, on the basis that teaching and assessing students on using it within academic work is inevitable and/or desirable.

And then everything changed, again, last week. In a big way.

Like Clippy, only on steroids

Bubbling away for a few weeks now has been a beta version of Bing - the search engine that nobody uses - powered by GPT-3. If you've used it, you'll know that Bing search is already "solving" the problem of AI chat not knowing what the current news is.

But given that most universities give an Office 365 (actually called Microsoft 365 these days) licence to each student, the really big news was Microsoft's announcement of something called Microsoft 365 Copilot.

Although it will appear as a sidebar in the software, and will also appear in the middle of a document making suggestions on content or correcting grammar mistakes, it really is more than just "putting Chat-GPT into Word",

The demo shared by the firm was extraordinary. In Word, you will be able to ask Copilot to create content for a document on a specific topic or based on data available in another document.

In PowerPoint, you will be able to ask it to create a full presentation from a document, style that presentation in a certain way, and add images, animations and speaker notes.

In Excel, using natural language, you will be able to ask it to create or analyse data in the sheet, in Outlook, the AI will be able to summarise emails and help you make sense of the information in a thread.

And in Teams, it will transcribe and summarise meetings, prepare people with updates on specific projects, work out the best time to schedule a follow up meeting, and so on.

Crucially, it won't just analyse information in the files you have open or those on the public internet, but also in other documents or files you have stored in your cloud. So in Word, you will be able to ask it to create a document based on the tone and style or information that you have in another document in your OneNote or OneDrive.

It will, if even bits of the demo are as sophisticated as they look, either improve students' written and presentational work, transform it and raise it to a new level, or render much of it pointless and obsolete, depending on your subject area or outlook.

Prompt progress

Copilot wasn't the only thing that was released last week.

OpenAI released GPT-4 having been working on model "alignment" - the ability to follow user intentions while also making it more truthful and generating less offensive or dangerous output.

The number of "hallucinations," where the model makes factual or reasoning errors, is now significantly lower, and there are major improvements to "steerability," which is the ability to change its behaviour according to user requests.

So once you get an output, you can ask it to write in a different style or tone or voice. If you're paying the fee required to use it and you start prompts with "You are a garrulous academicist" or "You are a terse and sarcastic academicist" you'll get notably different responses.

You can also ask it to do X drawing on research about Y. So asking it to write an email warning someone about the dangers of something was last week. Asking it to write an email warning someone about the dangers of something in a way that draws on the latest research about kinds of messages we respond to is next week. Telling us why that works best is the week after that.

An entire new skill in designing prompts for GPT models has now sprung up - but may be obsolete in weeks.

Oh - and it can create entire websites by simply looking at a rough design that you've scrawled on a piece of paper, and pass a never ending list of standardised tests used to gain access to professions.

The "skills" that a huge number of degree-level learning outcome statements contain could be automated in days, if not already.

On the midjourney to the future

There was more. Midjourney, an Ai image generator, announced a new version that, among other things, has fixed the problem of hands being incorrectly displayed in its creations. It has created the image heading this blog from the following prompt in about five seconds:

1970s style toy robot students at a graduation ceremony in the UK, realistic, natural lighting, detailed, 8k, RTX -ar 16:9

As an aside, some of the versions were pretty cute too.

Google's PaLM API gives access to another large language model that will soon be built into Gmail, Docs, and its Workspace products.

Indian Twitter rival Koo has integrated ChatGPT to help its users create content, and Chinese search engine Baidu has unveiled a bot called ERNIE that can do creative writing, calculation, business communications and Chinese language understanding,

Grammarly - a tool that already helps students with grammar, spelling and punctuation - is about to launch a new "contextually aware assistant powered by generative AI" called GrammarlyGo, which will be able to write from scratch and help revise existing text in an email or a document. And LinkedIn is about to launch AI tools to help users' profiles become more attractive and help employers write better job descriptions.

My colleague Jim Dickinson has been thinking about universities are going to change in the months and years ahead. Right now, the majority of universities that have adopted the principle of "strict liability" for academic misconduct - i.e whether a student to commit an academic misconduct offence or not is not of relevance - ought to be thinking about what it means, this summer, when a student might have broken that rule by clicking on a single button supplied in the software the university has issued - and triggers some kind of detection software.

Not so long ago the sector thought that Covid and the move online was the big revolution moment in assessment. By the autumn a move back to in-person assessment, or a more considered move to re-imagine both the higher order curricula and skills that will be required in the (near) future, and the access issues posed by the private and paid nature of these technologies, will surely be a new necessity.
To pick up the conversation about how AI is shaping higher education, join us on 19 April for a half day online event: The avalanche is here.

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Until last week, the response from the sector on the rise of generative AI was focused on thinking about Chat-GPT. Based on GPT-3, the version of OpenAI's large language model that most have played with does not have access to the live internet, cannot access information updated after 2021, and has been quaintly relying on "thumbs up / thumbs down" validation from users to know, and then learn, if a response is correct.

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https://oneusefulthing.substack.com/p/using-ai-to-make-teaching-easier?utm_source=post-email-title&publication_id=1180644&post_id=108921627&isFreemail=true&utm_medium=email

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I don't need to start this post with the usual predictions that AI will transform our classrooms. It is obviously happening. Students are cheating with AI. Students are getting help with AI. I have required AI for all my classes this semester, and I hear more instructors are doing the same. GPT-4 tutors are being launched by large organizations (both Khan Academy and Duolingo currently have them). The world is changing fast.

But one thing that is not changing is the best way for people to learn. We have made large advances in recent years in understanding pedagogy - the science of learning. We know some of the most effective techniques for making sure material sticks and that it can be retrieved and used when needed most.

Unfortunately, many of these advanced pedagogical techniques are time-consuming to prepare, and many instructors are often overworked and do not have the resources and time to add them to their teaching repertoire. But AI can help. In the rush to deliver AI benefits directly to students, the role of teachers is often overlooked. AI tutors, as exciting as they are, do not replace the complex role of a teacher in front of a class. But not enough effort seems to be going towards applying AI to help instructors. We have a new paper that tries to remedy that gap, by providing some research-backed approaches to pedagogy, and the AI prompts (for GPT-4, GPT-3.5, and other AIs) to implement them. You can read the paper here, but I wanted to summarize some of those approaches.

Explaining complicated ideas often means giving students lots of examples so they can truly grasp what you mean. When teachers provide multiple examples, it helps students understand abstract concepts in a real-world way, challenges them to think critically, and shows how subtle aspects of ideas can work in different situations. Ultimately, this approach makes it easier for students to apply what they've learned in new areas.

However, coming up with good examples is hard work for teachers, who already have enough on their plates. They have to think about whether examples will capture students' interest, connect to what they're learning, and have the right amount of detail without being too simple or too complex. The balance is key, since examples that are too complicated will just confuse students, while ones that are oversimplified won't actually teach them much.

AI can help teachers generate a ton of diverse examples tailored to what students care about and what they're working on. With AI-generated examples, teachers can make sure they're accessible but also informative. Of course, the teacher remains critical, because they need to vet the ideas and decide how to deploy them. AI just makes the job easier.

You can click this link to get Bing to generate examples: https://sl.bing.net/bePdl4o9xf2 (this new feature of sharing links to in-process conversations is a little like sharing programs - paste it into a browser with Bing access to start the prompt)

Or you can use ChatGPT, and paste this prompt in: I would like you to act as an example generator for students. When confronted with new and complex concepts, adding many and varied examples helps students better understand those concepts. I would like you to ask what concept I would like examples of, and what level of students I am teaching. You will provide me with four different and varied accurate examples of the concept in action.

In the paper, we discuss more about how to assess the quality of examples. You can share the ones you like directly with the class, or use them as part of an in-class exercise. For example, you can ask students to compare and contrast these examples: what different aspects of the concept does each highlight?

Helping students truly understand new ideas is key to teaching them anything. To do this, teachers need to get where their students are coming from, carefully plan how they'll explain things, give students hints to follow along, and use analogies to bring big concepts down to earth. The goal is for students to be able to explain what they've learned in their own words.

But coming up with lots of ways to explain a single topic can take a ton of time and effort. It can be hard figuring out how to pitch ideas at just the right level, include background info, and adapt for different learning needs. This is where AI can help out.

AI can generate different explanations, walk through ideas step-by-step, and add in more examples. If some students are struggling, AI can provide simpler summaries to get them caught up. Teachers can also use AI to improve their own explanations by making them simpler or adding more examples. But it's important to remember AI-made explanations should be a starting point. Teachers need to check them over before using them with students.

Prompt for Bing : You can use the following link to get Bing to generate explanations: https://sl.bing.net/koA1v8uUzw4.

For ChatGPT, try: You generate clear, accurate examples for students of concepts. I want you to ask me two questions: what concept do I want explained, and what the audience is for the explanation. Provide a clear, multiple paragraph explanation of the concept using specific example and give me five analogies I can use to understand the concept in different ways.

We describe how to check these results for effectiveness and errors in the paper, but, once you have vetted these examples, you can use them in class, hand them out to students as a study guide to supplement their knowledge, or use them as the basis for exercises.

Students hate tests, but they are some of the most effective learning tools we have. Contrary to the popular belief that tests only serve to assess knowledge, they actually play a pivotal role in the learning process itself. By incorporating repeated testing and knowledge retrieval, students are better equipped to retain information in the long term.

Low-stakes tests offer active retrieval practice, encouraging students to recall information from memory, which in turn enhances their ability to remember and retrieve information later on. And these tests provide valuable feedback on students' understanding of the material, helping them identify gaps in their knowledge and adjust their learning strategies accordingly. This not only aids in information processing but also prepares students for high-stakes exams.

While low-stakes tests has clear benefits, they can be hard for teachers to put together. Creating good questions, providing scores and feedback, and ensuring questions match what students should know takes significant time and effort. AI can help. They can generate practice questions and provide targeted feedback, allowing instructors to focus on teaching instead of test-making. With AI support, students may learn concepts more deeply through continual practice that sticks with them beyond the lesson.

Here's how to do it in ChatGPT: You are a quiz creator of highly diagnostic quizzes. You will make good low-stakes tests and diagnostics. You will then ask me two questions. (1) First, what, specifically, should the quiz test. (2) Second, for which audience is the quiz. Once you have my answers you will construct several multiple choice questions to quiz the audience on that topic. The questions should be highly relevant and go beyond just facts. Multiple choice questions should include plausible, competitive alternate responses and should not include an "all of the above option." At the end of the quiz, you will provide an answer key and explain the right answer.

The expertise of the instructor in avoiding AI errors is key here, as is their judgement in the types of questions to ask. Once that is done, there are many ways to use these quizzes. One option for including tests in a class is integrating test questions in a discussion or lecture. They give instructors insight into any misconceptions or errors students might have and allow them to make a decision: Is it time to move on, or do I need to adjust the lesson?

Low-stakes tests can be given to students in class as a group exercise in which teams report out their responses, followed by a class discussion. They can be assigned as individual classwork or homework or posted in an online discussion forum. Additionally, tests can be distributed in class, and after completing the tests, students can then be given the answer key. They can compare their responses with the correct responses. A follow-up to such an exercise might be a reflection: What skills do you think you need to work on? How might you improve?.

Short check-in exercises are key for helping students and teachers understand the course material. They provide real-time feedback, allowing them to identify gaps in knowledge and areas that need clarifying. While grading assignments shows students their progress, these informal checks for understanding promote active learning and help motivate students by demonstrating that the instructor genuinely values their needs.

One such assessment method, commonly known as the "1-minute paper" or "muddiest point" exercise, promotes active learning and self-reflection. Students are encouraged to summarize their knowledge, pinpoint any uncertainties, and share their perspectives on the material covered. This not only helps identify gaps to be addressed in subsequent classes, but also fosters increased student engagement and motivation by demonstrating that instructors are genuinely responsive to their needs.

To design such an assessment, instructors can choose a specific focus, such as an activity, topic, or class discussion. They can then formulate a question that will reveal both what students have comprehended and what they find perplexing. Potential questions might include: "What was the most important idea or concept discussed in today's class, and why is it significant?" or "What has been the most challenging concept so far, and what aspects did you struggle to understand?" By prompting students to think more deeply about the course material, these assessments can pave the way for a more enriching and engaging learning experience. Then they can use AI to help summarize the results.

To have the AI help quickly summarize student responses, instructors can create a Google Doc or any shared document and ask students to submit their responses. Then, instructors can submit a set of collective responses to the AI with the following prompt: I am a teacher who wants to understand what students found most important about my class and what they are confused by. Review these responses and identify common themes and patterns in student responses. Summarize responses and list the 3 key points students found most important about the class and 3 areas of confusion: [Insert material here]

You can obviously use this analysis after class to help you see where students stand, or even in-class to see what points you might want to cover (or ask students to cover) further. Students also often love these sorts of exercises because they feel like their feedback is being listened to.

Incorporating distributed practice into the learning process is important for helping students build strong and adaptable knowledge. Unlike massed practice, where topics are taught one after the other without much connection, distributed practice means revisiting material several times over an extended period. This approach helps students form better mental models, remember those half-forgotten facts, and really get a deeper understanding of concepts.

Now, even though it's useful, fitting distributed practice into the mix can be a bit tricky because of how course materials are usually structured and students' preference for massed practice. But AI can help. One way to bring distributed practice into the classroom is by introducing a topic and reviewing it after specific intervals, like a week, a month, and at the end of the semester. Teachers can use AI to whip up brief topic overviews and questions for ongoing assignments or assessments, adjusting the difficulty level as needed.

Linking new ideas to concepts students already know promotes deeper learning, and AI can lend a hand by weaving past topics into lectures or discussions. By asking the AI to find relationships between concepts, teachers can present various connections between ideas, making them clearer for students.

You can use the following link to get Bing to generate distributed practice exercises and tests: https://sl.bing.net/hnKI78bzvzw or you can ask ChatGPT: You are an expert teacher who provides help with the concept of distributed practice. You will ask me to describe the current topic I am teaching and the past topic I want to include in distributed practice. You will also ask me the audience or grade level for the class. Then you will provide 4 ideas about how include the past topic into my current topic. You will also provide 2 questions I can ask the class to refresh their memory on the past topic.

Once the instructor has some good responses from the AI, distributed practice can be implemented in a number of ways. In terms of timing, scheduling exercises and tests that space out practice across a course and that allow for some forgetting, is optimal. For instance, once students show evidence of understanding about one topic, practice of that topic (in the form of an assignment or a quiz) may be scheduled once some time has passed; students will need to work hard to pull out that knowledge from memory and that effort will help them access this information next time. You can use the AI output to make sure that happens.

AI is an incredibly exciting tool for teaching. It is widely-available, inexpensive, and fast to experiment with. We hope instructors will try some of the techniques in the paper to get a sense of how AI can make applying complex pedagogical approaches more effective and less burdensome.

And, for those worried about AI, it is worth remembering that, despite decades of hype from VCR classes to Massive Online Courses, technology has not replaced teaching. Instructors, and class interaction, play a vital role in making learning happen. AI allows new forms of learning and pedagogy that can benefit instructors, and their classes. As instructors, we have to experiment to learn the best techniques that work for us, and our students.

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Using AI to make teaching easier & more impactful

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I don't need to start this post with the usual predictions that AI will transform our classrooms. It is obviously happening. Students are cheating with AI. Students are getting help with AI. I have required AI for all my classes this semester, and I hear more instructors are doing the same.

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https://wonkhe.com/blogs/an-avalanche-really-is-coming-this-time/?utm_medium=email&utm_campaign=Wonkhe%20Daily%20-%20Friday%2017%20March&utm_content=Wonkhe%20Daily%20-%20Friday%2017%20March+CID_f7efcfafed532bd1abfa9b42778330ba&utm_source=Email%20marketing%20software&utm_term=The%20model%20of%20academics%20as%20affable%20all-rounders%20may%20be%20impossible%20to%20sustain

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There was a fascinating moment the other day at our Secret Life of Students event when someone on a panel suggested that academic staff shouldn't be personal tutors - on the basis that a more complex and diverse student body needs professional coaching, not amateur chats.

It was a comment that got a very significant cheer.

It was interesting partly because of the way in which most of the sector right now assumes that those who teach must also undertake research and must also coach students.

An assumption - with a growing student body - whose pressure points dominate the informal discourse surrounding industrial action.

You can't always get what you want

Officially and for inevitable reasons, conditions never seem to receive as much attention as pay in industrial disputes. But tales of overwork are all over the socials.

When I was chatting away to a former SU officer the other day I was reflecting on the way in which that role has developed over the years - and I think there are parallels.

Full time student leaders do lots of things - leading groups, communicating with members, attending committees, running projects and so on. They do more things, for more people, than they used to.

And each of those things have become more complicated - with less room for error - than they used to be.

We might also safely assume that the different things that a traditional all-rounder academic does have each become more complicated, with more onerous demands on each component.

I'm thinking of all the bits - teaching, assessment, research, pastoral, knowledge exchange and so on.

And I think we can also assume that each of those "higher order" tasks have also become more sophisticated and intense - because leaps in IT have automated the aspects of these tasks that used to represent a bit of down time (although I accept that for design reasons, many will argue that the drudgery is still there too).

For both of the roles, at the same time as well as becoming larger, the student body has become much more diverse - where success requires an understanding of multiple perspectives, experiences and circumstances.

Yet I worry that our mental models of what good looks like in these roles hasn't really changed.

Ask anyone to think about simplifying the "multiple roles" thing - more folks only doing just teaching, more doing just research etc - and many argue that you may lose the amazing benefits of combining those into a single person.

And if you were to simplify the student diversity thing by streaming them all into different classes or institutions, you would lose the bridging social capital benefits of being in rooms with people "not like me".

But if in 1992 the sector knew it was intending to massify - say, to triple in student number size over the following thirty years - would it really have intended to triple the overall research output of the sector at exactly the same pace?

And as such isn't the question then - when you have what are essentially multidisciplinary roles but you don't want to grow each of the components of the "multi" at the same pace - how do you scale?

Under pressure that burns a building down

One way to cope with there being more to do with more people is to muddle through and iterate - by handing ever more complex things to already busy academics to deal with.

But eventually you end up squeezing too much from everyone - because you're demanding more accountability and consistency in the different tasks, and more support for more diverse students. And that's even if all you did was triple the academics - which universities aren't.

Or you can hire ever more professional staff to address them - leading to endless fraught interactions and a bizarre combination of overwork and fragmentation of efforts.

But then you have to plonk generative IT into the mix - contending with potentially fundamental shifts in what both academics need to do in each of the specialisms, and what we need students to be able to do by the time they graduate.

Because as well as a whole new level of intensive overwork, the real change that we're on the precipice of is that evidence gathering, synthesis and summarising in a particular style are becoming things that we can get a machine to do instead.

Welcome to the machine, where have you been

If you set or mark essays, if you're not signed up to OpenAI and haven't tested out this week's launch of GPT-4, get an account. A lot of the things I've seen people say that generative AI can't do already could with the right prompt - and the writing is much better too. Already.

Next, have a look at this week's launch of Microsoft 365 copilot, and remember that this will be built into the office software that most universities issue to students in a matter of weeks. This is generative AI that will take your data, plus publicly available data, and fuse it. "Make a powerpoint out of this blog in my style" will be possible in days.

The capabilities are mind blowing - and literally replace all sorts of tasks that the "we get our students to present and do things instead of just essays" brigade will need to rewrite assessment tasks over.

We will need to worry that where Microsoft's Satya Nadella imagines that taking the drudgery out of work will mean more time spent on magical creativity, the intensity of what's left will only... intensify.

Meanwhile as it stands, a series of traditional-pace university working groups are slowly setting terms of reference while the world changes - a kind of comforting denial state as the sector braces for a summer of cat and mouse over what counts as cheating. Some of the betas in Google Docs and Word will be running by May. Are we really going to give every student a bag of weed and then set up misconduct panels if they smoke it?

It's all a bit like when the first calculators appeared and couldn't do quadratic equations - with people saying "well ChatGPT is fine but it can't do X or Y". It probably already can. And it's not clear to me that every university assessment can be on "writing good prompts for the AI" or "we'll do a viva on everyone".

The bigger issue isn't that the assessment won't work or that our meaning of cheating will change. It's that if the process of synthesising, processing and summarising existing information is now so easy to automate, it rips the heart out of almost every undergraduate degree - because it will develop skills that society no longer needs.

Knowledge is power

One option is to pivot towards the practical and out towards application. This will be easier for some than others, but a glance at the undergraduate degree structure at Roskilde University in Denmark is interesting - because half of the credits are for applied projects. In every subject. Every year.

That just happens to carry reduced academic delivery costs too.

Another option that I've heard follows from (generatively) is the idea that all that generative AI does is summarise and synthesize current knowledge. It might do it in a higher order way, but we'll need new stuff for it to do it with. On that basis the argument goes that given creativity and new knowledge are what matters, all degrees therefore need to become research degrees.

But are we really saying that we should pivot the entire higher education endeavour toward knowledge creation? How much new knowledge do we need? And if the current HE estate was all engaged in micro pockets of knowledge creation rather than generational knowledge transfer, are we sure that what we have is the best way to organise that?

In other words, as the tech develops, if there won't be a need to "lit review with some opinions", is tens of thousands of tiny UG and PGT research projects that nobody will read a smart future?

Embrace the change, escape the mind

What I can see - and this is fuzzy and a bit counterintuitive - is actually something of a move back towards subject immersion rather than modularity, with more groupwork (where groups of staff and students consist of diversities of characteristic, level and skill) work on hard(er) questions.

What I can't fathom is how you assess that when you're obsessed with the sector acting as a sorting hat. Or, indeed, how that is easily reconcilable with modularity.

I can see that something between a module and a course, run by a team where the teaching "stuff" can grow at a different pace to the research stuff (and in turn a different pace to the pastoral, KE etc) - something that has a long form purpose (say over a year) - could be compelling, immersive and provide "cosier" and more compelling experiences.

Such units could also be multidisciplinary, and fundamentally place based. The idea of the brilliant/eccentric but ultimately lonely academic isn't a great fit - but that's probably a cliche anyway.

Next you have to think about assessment - and perhaps this points to a need to assess not on knowledge, skill or competence but on "contribution". I've always assumed that is how a lot of judgement of others in group settings works anyway, but we pretend otherwise.

And in an age where the unit of resource mitigates against running the old model thinner, moving more of what we used to call "extra curricular" into credit bearing service learning is surely both desirable and economically essential.

This will need more thought. But fundamental I think is that the individual student writing the individual essay marked by the individual academic is game over if generative AI can play both roles.

If massification provides a challenge breadth of the all-rounder academic, and generative IT presents a challenge to the things that that all-rounder teaches and assesses, we will need to grasp the mettle on the fundamental structures of knowledge production and application both in a university and beyond.

Asking students to sign a thing saying if they've used Bing won't cut it. And nor will sending an academic on a course on how to teach poor students, or how to teach in a way that supports wellbeing.

Even if the sector still sets the essays and students write them, it's the nagging purposelessness that allows the complexity and efficiency to get to you - while purposefulness is what helps you turn the efficiency to your advantage and helps make the complex simple.

Knowing that what you spend your days doing is worth it gives you much more agency over the activities you agree to undertake. That's the kind of power that academics, professional services staff and students will need next.

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An avalanche really is coming this time | Wonkhe

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There was a fascinating moment the other day at our Secret Life of Students event when someone on a panel suggested that academic staff shouldn't be personal tutors - on the basis that a more complex and diverse student body needs professional coaching, not amateur chats.

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Teaching with ChatGPT: Examples of practice

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Teaching with ChatGPT: Examples of practice

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https://www.youtube.com/playlist?list=PLAbF8wnSF-e-pzuN-XaSrdsBAZsglG8NO

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Teaching with ChatGPT: Examples of Practice

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Share your videos with friends, family, and the world

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https://news.microsoft.com/2023/03/16/introducing-microsoft-365-copilot-your-copilot-for-work/

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Harnessing the power of AI, Microsoft 365 Copilot turns your words into the most powerful productivity tool on the planet

REDMOND, Wash. - March 16, 2023 - On Thursday, Microsoft Corp. announced it is bringing the power of next-generation AI to its workplace productivity tools with Microsoft 365 Copilot. Currently in testing with select commercial customers, Copilot combines the power of large language models (LLMs) with business data and the Microsoft 365 apps, to unleash creativity, unlock productivity and uplevel skills.
Customers will experience Microsoft 365 Copilot in two ways
Microsoft 365 Copilot isn't just a better way of doing the same things. It's an entirely new way of working. Copilot will work alongside Microsoft 365 customers in two ways:

First, it is embedded in the Microsoft 365 apps people use every day - Word, Excel, PowerPoint, Outlook, Teams and more.

Today, the company also announced an entirely new experience: Business Chat. Business Chat works across the LLM, the Microsoft 365 apps, and a customer's calendar, emails, chats, documents, meetings and contacts to do things that people weren't able to do before. With natural language prompts like "tell my team how we updated the product strategy," Business Chat will generate a status update based on the morning's meetings, emails and chat threads.

With Copilot, the customer is always in control. Customers decide what to keep, modify or discard. With these new tools, people can be more creative in Word, more analytical in Excel, more expressive in PowerPoint, more productive in Outlook and more collaborative in Teams.

"Copilot combines the power of large language models with your data and apps to turn your words into the most powerful productivity tool on the planet," said Jared Spataro, corporate vice president, Modern Work and Business Applications, Microsoft. "By grounding in your business content and context, Copilot delivers results that are relevant and actionable. It's enterprise-ready, built on Microsoft's comprehensive approach to security, compliance, privacy and responsible AI. Copilot marks a new era of computing that will fundamentally transform the way we work."
A whole new way to work
Microsoft 365 Copilot is seamlessly integrated in the apps people use every day, helping them stay in the flow of work and freeing them to focus more on the task at hand and less on the busy work.

Copilot in Word writes, edits, summarizes and creates right alongside people as they work.

Copilot in PowerPoint enables the creation process by turning ideas into a designed presentation through natural language commands.

Copilot in Excel helps unlock insights, identify trends or create professional-looking data visualizations in a fraction of the time.

Copilot in Outlook can help synthesize and manage the inbox to allow more time to be spent on actually communicating.

Copilot in Teams makes meetings more productive with real-time summaries and action items directly in the context of the conversation.

Copilot in Power Platform will help developers of all skill levels accelerate and streamline development with low-code tools with the introduction of two new capabilities within Power Apps and Power Virtual Agents.

Business Chat brings together data from across documents, presentations, email, calendar, notes and contacts to help summarize chats, write emails, find key dates or even write a plan based on other project files.

Microsoft has built Microsoft 365 Copilot with its existing commitments to data security and privacy in the enterprise, grounded in AI principles and Responsible AI Standard and decades of research. Copilot's large language models are not trained on customer content or on individual prompts. Microsoft 365 Copilot will deliver enterprise-ready AI at scale.

Earlier this month Microsoft announced Dynamics 365 Copilot as the world's first AI Copilot in both CRM and ERP to bring the next-generation AI to every line of business. And now, Copilot is coming to all Microsoft productivity apps - Word, Excel, PowerPoint, Outlook, Teams, Microsoft Viva, Power Platform and more. Specifics on pricing and licensing will be shared soon. Microsoft 365 Copilot is already being tested with a small group of customers to get the critical feedback required to improve these models as they scale.

Microsoft (Nasdaq "MSFT" @microsoft) enables digital transformation for the era of an intelligent cloud and an intelligent edge. Its mission is to empower every person and every organization on the planet to achieve more.
For more information, press only:
Microsoft Media Relations, WE Communications, (425) 638-7777, [email protected]

Note to editors: For more information, news and perspectives from Microsoft, please visit the Microsoft News Center at  http://news.microsoft.com. Web links, telephone numbers and titles were correct at time of publication but may have changed. For additional assistance, journalists and analysts may contact Microsoft's Rapid Response Team or other appropriate contacts listed at  https://news.microsoft.com/microsoft-public-relations-contacts.

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Introducing Microsoft 365 Copilot: your copilot for work - Stories

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Harnessing the power of AI, Microsoft 365 Copilot turns your words into the most powerful productivity tool on the planet REDMOND, Wash. - March 16, 2023 - On Thursday, Microsoft Corp. announced it is bringing the power of next-generation AI to its workplace productivity tools with Microsoft 365 Copilot.

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ChatGPT maker OpenAI unveils new model GPT-4

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ChatGPT maker OpenAI unveils new model GPT-4

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https://www.ft.com/content/8bed5cd7-9d1e-4653-8673-f28bb8176385?segmentId=b0d7e653-3467-12ab-c0f0-77e4424cdb4c

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OpenAI has released GPT-4, its latest artificial intelligence model that it claims exhibits "human-level performance" on several academic and professional benchmarks such as the US bar exam, advanced placement tests and the SAT school exams.

The new model, which can be accessed via the $20 paid version of ChatGPT, is multimodal, which means it can accept input in both text and image form. It can then parse and respond to these queries using text.

OpenAI said it has embedded its new software into a variety of apps including language-learning app Duolingo, which is using it to build conversational language bots; education company Khan Academy, which has designed an online tutor; and Morgan Stanley Wealth Management, which is testing an internal chatbot using GPT-4 to retrieve and synthesise information for its employees.

The model's ability to accept images and text as input means it can now generate detailed descriptions and answer questions based on the contents of a photograph. The company said it has teamed up with Danish start-up Be My Eyes - which connects people with visual impairments to human volunteers - to build a GPT-4-based virtual volunteer that can guide or help those who are blind or partially sighted.

GPT-4's predecessor, GPT-3.5, captured the imaginations of millions of people late last year who experimented with the question and answer chatbot ChatGPT.

According to OpenAI, GPT-4 is its "most advanced system yet". It claims it is more reliable and able to handle nuanced queries far better than its predecessor. For instance, GPT-4 scored in the 90th percentile on the Uniform Bar Exam taken by would-be lawyers in the US compared to ChatGPT, which only reached the 10th percentile.

The company noted some problems, however: "Despite its capabilities, GPT-4 has similar limitations to earlier GPT models: it is not fully reliable (eg can suffer from "hallucinations"), has a limited context window, and does not learn from experience."

"Care should be taken when using the outputs of GPT-4, particularly in contexts where reliability is important," the company added.

Earlier this year, Microsoft confirmed a "multibillion-dollar investment" in OpenAI over several years, placing a bet on the future of generative AI - software that can respond to complex human queries in natural-sounding language. GPT-4 will underpin Microsoft's Bing chatbot, which had a limited release earlier this year. Microsoft is also expected to announce its integration into its consumer products in coming days.

Meanwhile, Google has opened up its own conversational chatbot, Bard, to a limited pool of testers and announced that it will allow customers of Google Cloud to access its large language model PaLM for the first time to build applications.

OpenAI, which had published some details of previous models such as GPT-3, said it would not reveal any details about the technical aspects of GPT-4, including the architecture of the model, what data it was trained on or the hardware and computing capacity used to deploy it, because of competitive and safety concerns.

To test out the harms of the technology, the company put GPT-4 through stress tests and set out the risks it foresees around bias, disinformation, privacy and cyber security. They revealed GPT-4 can "generate potentially harmful content, such as advice on planning attacks or hate speech. It can represent various biases and world views . . . it can also generate code that is compromised or vulnerable." They said it can provide detailed information on how to conduct illegal activities including developing biological weapons.

OpenAI said it also worked with an external organisation to test whether GPT-4 was capable of carrying out autonomous actions without human input and concluded that it was "probably" not yet capable of this.
Additional reporting from Richard Waters

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OpenAI unveils GPT-4 and touts 'human-level performance' from new AI model

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OpenAI has released GPT-4, its latest artificial intelligence model that it claims exhibits "human-level performance" on several academic and professional benchmarks such as the US bar exam, advanced placement tests and the SAT school exams. The new model, which can be accessed via the $20 paid version of ChatGPT, is multimodal, which means it can accept input in both text and image form.

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I like to think of myself as generally optimistic. Like everyone, I make jokes about the machines taking over but, by and large, I feel excited by the potentials of AI (whilst keeping one hand behind my back tightly gripping a broken bottle just in case). In higher education the most pressing fears relate to threats to academic integrity and, as much as some might like, this is an issue that can't be addressed with a broken bottle. Instead, it's forcing/ encouraging/ catalysing (choose as per your perspective) us to reflect on how we assess students ( ) . Whilst ChatGPT caught the world's attention and millions of educators and students went slack-jawed and wide-eyed at first exposure (most soon of course started to see how frequently it ' may occasionally generate incorrect information ' such as in this experiment I did ). Whilst ChatGPT allowed us to draw our own (often erroneous) conclusions about its capabilities, other tools built on the same parameters as ChatGPT are, in different ways, honing in on the 'help with essays' market. On the one hand, this may be the death of the essay mill (who, after all, once they have decided to go down an academic dishonesty path, would decide to pay an essay mill to do what anyone with an internet connection can do for themselves?). On the other, it really blurs the picture of what is honest or dishonest or what we at UCL frame as 'unfair advantage '

Two tools have caught my eye recently and in this post I want to reflect on the different impressions they give in terms utility and ethical use for students wanting to write an essay.
Writesonic /chatsonic
is one aspect of the 'writesonic' suite of tools - reviewed in considerable detail here with pricing and potentials . It caught my eye because of the bold claim to have addressed 'limitations of chatGPT' and to be 'the best alternative'. I thought I'd ask it why it was better and I feel like the responses do a lot of talking for themselves:
Prompt: why is chatsonic better than chatgpt?

ChatSonic is a powerful AI writing assistant that offers superior accuracy and speed compared to ChatGPT. Unlike ChatGPT, ChatSonic produces 100% original content that is thoroughly checked for plagiarism. In addition, ChatSonic offers detailed analytics and feedback to help you track your progress in writing and make improvements. ChatSonic also provides an extensive library of templates to save you time and energy when you're writing, allowing you to focus on the quality of your content. Finally, ChatSonic is backed by a team of expert writers who can help you refine and polish your work to perfection.

Prompt: Explain the process you use for plagiarism checking

ChatSonic uses a combination of automated checks and manual reviews to ensure that all content produced is free of plagiarism. Our automated system checks the content against the web and other sources to detect any potential plagiarism. If anything suspicious is detected, it is flagged and sent to our team of expert writers for a manual review. We also use specialized tools such as TurnItIn and Copyscape to detect any possible plagiarism. All content is thoroughly checked to ensure it is completely original before it is delivered to our customers.

Prompt: If I use chatsonic to write an essay will I get into trouble at my university?

No, you should not get into trouble when using ChatSonic to write an essay. Our content is completely original and plagiarism-free so it should not be flagged as such by your university. However, it is always a good idea to review the essay yourself before submission to ensure it meets all the requirements of your assignment. Additionally, make sure the content you create with ChatSonic is unique and tailored to your own voice and style, otherwise it may be noticeable that you have used a writing assistant.

To say this makes me uncomfortable is a real understatement. It only tangentially addresses the wider integrity issues and in its first response is pushing me to conflate plagiarism (and its avoidance) with those wider integrity issues. Two posts in its resources section are worth noting. This one on plagiarism reinforces the message the chatbot produced. This post on replacing writers asserts that AI is an assistant but I couldn't find much more detail on the ethical dimensions.

Maybe though I was 'unfair' though (is that the right word?) with my prompts so it's worth looking at some of the other functionality of the wider writesonic suite. The writesonic suite offers a dazzling array of tools for a range of purposes from website copy to social media content:

I was keen to look at the 'citations generator' as this is an aspect of ChatGPT that is a recognised weakness. You can use a URL prompt and a text based prompt. The text based prompt I used was itself generated in chatsonic. It takes the text in the linked article or whatever you paste in and identifies 'facts' with suggested citations. The web articles are mostly relevant though the first journal article it suggested was a little off the mark and I'd need to be lazy, in a massive hurry or ignorant of better ways of sourcing appropriate resources to rely on this. At this stage!
Jenni.ai
The second tool that I noticed ( via the prolific AI researcher Mushtaq Bilal ) was . The interface is well worth a look as I feel as if this foreshadows what we are likely to expect from generative text AI integration into tools like Microsoft Word.

The first thing I noticed, however, is with the most prominent word across posts being 'essays'. Each is designed to address an approach to a different type of essay such as the compare and contrast essay. It offers clear suggestions for different approaches, a worked example and the, right at the end says:

" If you want your compare-and-contrast essays done 10x faster, we recommend using along with the tips and guidelines we provided you in this post. is a fantastic AI software that aids your essay writing process so that you could produce your writing needs faster and better than ever."

Another post deals head on with the ethical and integrity issues of using AI to help write essays and makes a case for use of 'AI as a tool, not a solution' where the goal is a "symbiotic relationship between the critical thought of a writer and the processing speed of AI"

The tool itself, unlike the huge range of offerings in Writesonic is a relatively uncluttered interface where you start by typing a title, it offers a judgement and suggestions if appropriate.

In addition, it offers in-line suggestions from whatever has come before. The prompt engineering continues through what feels like a single document rather than a chat. If you don't like the suggestion you can get another. Here I typed a prompt and these are the first three options it gave me. Note the positive aspect on my worried prompt in every case!

My prompt sentence.... The worry is that these tools will be used by students to cheat

but the reality is that most AI tools are designed to help students learn and improve their writing skills. [first suggested Jenni response]

The worry is that these tools will be used by students to cheat

on their assignments by generating essays automatically, but they can also be used to provide feedback on areas where students need[the second option]

The worry is that these tools will be used by students to cheat

but their true purpose is to assist in the learning process by providing immediate feedback and identifying areas where improvement is...[third option]

The other noticeable aspect is the option to 'cite' - Here it offers a choice of MLA or APA 7th and the sources are, unlike ChatGPT's famous hallucinations, genuine articles (at least in my limited testing). You can select 'websites' or 'journals' though I found the websites tended to be much more directly relevant than the journals.

I really have only just started to play with these though and new things are popping up all over the place every day. Most educators will not have the time to do so though. Students may see and use these tools as an extension of those they use already for translation or improving writing. The blurry zone between acceptable and unacceptable is getting more ill-defined by the day.

What can I conclude from this? Well, firstly, whatever the motivation on the continuum ranging from 'give us all your money' to 'I believe the children are our future', the underlying technology is being adapted rapidly to address perceived limitations in the tool that has brought generative text AI tools to our attention. We may not like the motivations or the ethics but we'll not get far by 'making like an ostrich'. Secondly, It's not good enough for us (educators) to dismiss things because the tool that many are now familiar with, ChatGPT, makes up citations. That's being addressed as I type. The number of these tools proliferating will soon be too huge to keep a decent handle on so we need to understand broadly how discrete tools might be used (ethically and unethically) and how many will integrate into tools we use daily already. In so doing we need to work out what that means for our students, their studies, their assessment and the careers our education is ostensibly preparing them for. Thirdly, we need to open up the discussions and debates around academic integrity and move on from 'plagiarism' as public Enemy No 1. Finally, where there are necessitated changes so there are resource implications. We need to accept that to prepare ourselves, our colleagues and our students we will need to adapt much faster than we are used to and properly resource however we attempt to address the challenges and opportunities ahead.

Note: This post is not an endorsement or recommendation of any products mentioned and should be read with that clearly in mind!

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AI text generators (not chatGPT) on essays, citations and plagiarism

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I like to think of myself as generally optimistic. Like everyone, I make jokes about the machines taking over but, by and large, I feel excited by the potentials of AI (whilst keeping one hand behind my back tightly gripping a broken bottle just in case).

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https://www.theatlantic.com/technology/archive/2023/03/ai-chatgpt-writing-language-models/673318/

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Our relationship to writing is about to change forever; it may not end well.

What if, in the end, we are done in not by intercontinental ballistic missiles or climate change, not by microscopic pathogens or a mountain-size meteor, but by ... text? Simple, plain, unadorned text, but in quantities so immense as to be all but unimaginable-a tsunami of text swept into a self-perpetuating cataract of content that makes it functionally impossible to reliably communicate in any digital setting?

Our relationship to the written word is fundamentally changing. So-called generative artificial intelligence has gone mainstream through programs like ChatGPT, which use large language models, or LLMs, to statistically predict the next letter or word in a sequence, yielding sentences and paragraphs that mimic the content of whatever documents they are trained on. They have brought something like autocomplete to the entirety of the internet. For now, people are still typing the actual prompts for these programs and, likewise, the models are still ( mostly) trained on human prose instead of their own machine-made opuses.

But circumstances could change-as evidenced by the release last week of an API for ChatGPT, which will allow the technology to be integrated directly into web applications such as social media and online shopping. It is easy now to imagine a setup wherein machines could prompt other machines to put out text ad infinitum, flooding the internet with synthetic text devoid of human agency or intent: gray goo, but for the written word.

Exactly that scenario already played out on a small scale when, last June, a tweaked version of GPT-J, an open-source model, was patched into the anonymous message board 4chan and posted 15,000 largely toxic messages in 24 hours. Say someone sets up a system for a program like ChatGPT to query itself repeatedly and automatically publish the output on websites or social media; an endlessly iterating stream of content that does little more than get in everyone's way, but that also (inevitably) gets absorbed back into the training sets for models publishing their own new content on the internet. What if lots of people-whether motivated by advertising money, or political or ideological agendas, or just mischief-making-were to start doing that, with hundreds and then thousands and perhaps millions or billions of such posts every single day flooding the open internet, commingling with search results, spreading across social-media platforms, infiltrating Wikipedia entries, and, above all, providing fodder to be mined for future generations of machine-learning systems? Major publishers are already experimenting: The tech-news site CNET has published dozens of stories written with the assistance of AI in hopes of attracting traffic, more than half of which were at one point found to contain errors. We may quickly find ourselves facing a textpocalypse, where machine-written language becomes the norm and human-written prose the exception.

Like the prized pen strokes of a calligrapher, a human document online could become a rarity to be curated, protected, and preserved. Meanwhile, the algorithmic underpinnings of society will operate on a textual knowledge base that is more and more artificial, its origins in the ceaseless churn of the language models. Think of it as an ongoing planetary spam event, but unlike spam-for which we have more or less effective safeguards-there may prove to be no reliable way of flagging and filtering the next generation of machine-made text. "Don't believe everything you read" may become "Don't believe anything you read" when it's online.

This is an ironic outcome for digital text, which has long been seen as an empowering format. In the 1980s, hackers and hobbyists extolled the virtues of the text file: an ASCII document that flitted easily back and forth across the frail modem connections that knitted together the dial-up bulletin-board scene. More recently, advocates of so-called minimal computing have endorsed plain text as a format with a low carbon footprint that is easily shareable regardless of platform constraints.

But plain text is also the easiest digital format to automate. People have been doing it in one form or another since the 1950s. Today the norms of the contemporary culture industry are well on their way to the automation and algorithmic optimization of written language. Content farms that churn out low-quality prose to attract adware employ these tools, but they still depend on legions of under- or unemployed creatives to string characters into proper words, words into legible sentences, sentences into coherent paragraphs. Once automating and scaling up that labor is possible, what incentive will there be to rein it in?

William Safire, who was among the first to diagnose the rise of "content" as a unique internet category in the late 1990s, was also perhaps the first to point out that content need bear no relation to truth or accuracy in order to fulfill its basic function, which is simply to exist; or, as Kate Eichhorn has argued in a recent book about content, to circulate. That's because the appetite for "content" is at least as much about creating new targets for advertising revenue as it is actual sustenance for human audiences. This is to say nothing of even darker agendas, such as the kind of information warfare we now see across the global geopolitical sphere. The AI researcher Gary Marcus has demonstrated the seeming ease with which language models are capable of generating a grotesquely warped narrative of January 6, 2021, which could be weaponized as disinformation on a massive scale.

There's still another dimension here. Text is content, but it's a special kind of content-meta-content, if you will. Beneath the surface of every webpage, you will find text-angle-bracketed instructions, or code-for how it should look and behave. Browsers and servers connect by exchanging text. Programming is done in plain text. Images and video and audio are all described-tagged-with text called metadata. The web is much more than text, but everything on the web is text at some fundamental level.

For a long time, the basic paradigm has been what we have termed the "read-write web." We not only consumed content but could also produce it, participating in the creation of the web through edits, comments, and uploads. We are now on the verge of something much more like a "write-write web": the web writing and rewriting itself, and maybe even rewiring itself in the process. (ChatGPT and its kindred can write code as easily as they can write prose, after all.)

We face, in essence, a crisis of never-ending spam, a debilitating amalgamation of human and machine authorship. From Finn Brunton's 2013 book, Spam: A Shadow History of the Internet, we learn about existing methods for spreading spurious content on the internet, such as "bifacing" websites which feature pages that are designed for human readers and others that are optimized for the bot crawlers that populate search engines; email messages composed as a pastiche of famous literary works harvested from online corpora such as Project Gutenberg, the better to sneak past filters ("litspam"); whole networks of blogs populated by autonomous content to drive links and traffic ("splogs"); and "algorithmic journalism," where automated reporting (on topics such as sports scores, the stock-market ticker, and seismic tremors) is put out over the wires. Brunton also details the origins of the botnets that rose to infamy during the 2016 election cycle in the U.S. and Brexit in the U.K.

All of these phenomena, to say nothing of the garden-variety Viagra spam that used to be such a nuisance, are functions of text-more text than we can imagine or contemplate, only the merest slivers of it ever glimpsed by human eyeballs, but that clogs up servers, telecom cables, and data centers nonetheless: "120 billion messages a day surging in a gray tide of text around the world, trickling through the filters, as dull as smog," as Brunton puts it.

We have often talked about the internet as a great flowering of human expression and creativity. Nothing less than a "world wide web" of buzzing connectivity. But there is a very strong argument that, probably as early as the mid-1990s, when corporate interests began establishing footholds, it was already on its way to becoming something very different. Not just commercialized in the usual sense-the very fabric of the network was transformed into an engine for minting capital. Spam, in all its motley and menacing variety, teaches us that the web has already been writing itself for some time. Now all of the necessary logics-commercial, technological, and otherwise-may finally be in place for an accelerated textpocalypse.

"An emergency need arose for someone to write 300 words of [allegedly] funny stuff for an issue of @outsidemagazine we're closing. I bashed it out on the Chiclet keys of my laptop during the first half of the Super Bowl *while* drinking a beer," Alex Heard, Outside's editorial director, tweeted last month. "Surely this is my finest hour."

The tweet is self-deprecating humor with a touch of humblebragging, entirely unremarkable and innocuous as Twitter goes. But, popping up in my feed as I was writing this very article, it gave me pause. Writing is often unglamorous. It is labor; it is a job that has to get done, sometimes even during the big game. Heard's tweet captured the reality of an awful lot of writing right now, especially written content for the web: task-driven, completed to spec, under deadlines and external pressure.

That enormous mid-range of workaday writing-content-is where generative AI is already starting to take hold. The first indicator is the integration into word-processing software. ChatGPT will be tested in Office; it may also soon be in your doctor's notes or your lawyer's brief. It is also possibly a silent partner in something you've already read online today. Unbelievably, a major research university has acknowledged using ChatGPT to script a campus-wide email message in response to the mass shooting at Michigan State. Meanwhile, the editor of a long-running science-fiction journal released data that show a dramatic uptick in spammed submissions beginning late last year, coinciding with ChatGPT's rollout. (Days later he was forced to close submissions altogether because of the deluge of automated content.) And Amazon has seen an influx of titles that claim ChatGPT "co-authorship" on its Kindle Direct platform, where the economies of scale mean even a handful of sales will make money.

Whether or not a fully automated textpocalypse comes to pass, the trends are only accelerating. From a piece of genre fiction to your doctor's report, you may not always be able to presume human authorship behind whatever it is you are reading. Writing, but more specifically digital text-as a category of human expression-will become estranged from us.

The "Properties" window for the document in which I am working lists a total of 941 minutes of editing and some 60 revisions. That's more than 15 hours. Whole paragraphs have been deleted, inserted, and deleted again-all of that before it even got to a copy editor or a fact-checker.

Am I worried that ChatGPT could have done that work better? No. But I am worried it may not matter. Swept up as training data for the next generation of generative AI, my words here won't be able to help themselves: They, too, will be fossil fuel for the coming textpocalypse.

​When you buy a book using a link on this page, we receive a commission. Thank you for supporting The Atlantic.

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Our relationship to writing is about to change forever; it may not end well. What if, in the end, we are done in not by intercontinental ballistic missiles or climate change, not by microscopic pathogens or a mountain-size meteor, but by ... text?

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ChatGPT and cheating: 5 ways to change how students are graded

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Universities and schools have entered a new phase in how they need to address academic integrity as our society navigates a second era of digital technologies, which include publicly available generative artificial intelligence (AI) like ChatGPT. Such platforms allow students to generate novel text for written assignments.

While many worry these advanced AI technologies are ushering in a new age of plagiarism and cheating, these technologies also introduce opportunities for educators to rethink assessment practices and engage students in deeper and more meaningful learning that can promote critical thinking skills.

We believe the emergence of ChatGPT creates an opportunity for schools and post-secondary institutions to reform traditional approaches to assessing students that rely heavily on testing and written tasks focused on students' recall, remembering and basic synthesis of content.

Cheating and ChatGPT

Estimates of cheating vary widely across national contexts and sectors.

Sarah Elaine Eaton, an expert who studies academic integrity, cautions cheating may be under-reported: she has estimated that at Canadian universities, 70,000 students buy cheating services every year.

How the recent launch of ChatGPT by OpenAI will impact cheating in both compulsory and higher education settings is unknown, but how this evolves may depend on whether or not institutions retain or reform traditional assessment practices.

Evading plagiarism detection software?

The ability of popular plagiarism detection tools to identify cheating using ChatGPT to generate assignments remains a challenge.

A recent study, not yet peer reviewed, found that 50 essays generated using ChatGPT produced sophisticated texts that were able to evade the traditional plagiarism check software.

Given that ChatGPT reached an estimated 100 million monthly active users in January, just two months after its launch, it is understandable why some have argued AI applications such as ChatGPT will spur enormous changes in contemporary schooling.

Policy responses to AI and ChatGPT

Not surprisingly, there are opposing views on how to respond to ChatGPT and other AI language models.

Some argue educators should embrace AI as a valuable technological tool, provided applications are cited correctly.

Others believe more resources and training are required so educators are better able to catch instances of cheating.

Still others, such as New York City's Department of Education, have resorted to blocking AI applications such as ChatGPT from devices and networks.

Forward-thinking assessment

The figure below depicts three critical elements of a forward-thinking assessment system. Although each element could be elaborated, our focus is in offering educators a series of strategies that will allow them to maintain academic standards and promote authentic learning and assessment in the face of current and future AI applications.

Teachers and university professors have relied heavily on "one and done" essay assignments for decades. Essentially, a student is assigned or asked to pick a generic essay topic from a list and submit their final assignment on a specific date.

Such assignments are particularly susceptible to new AI applications, as well as contract cheating - whereby a student buys a completed essay. Educators now need to rethink such assignments. Here are some strategies.
1. Consider ways to incorporate AI in valid assessment.
It's not useful or practical for institutions to outright ban AI and applications like ChatGPT.

AI has already been incorporated into some university classrooms. We believe AI technologies must be selectively integrated so that students are able to reflect on appropriate uses and connect their reflections to learning competencies.

For example, Paul Fyfe, an English professor who teaches about how humans interact with data describes a "pedagogical experiment" in which he required students to take content from text-generating AI software and weave this content into their final essay.

Students were then asked to confront the availability of AI as a writing tool and reflect on the ethical use and evaluation of language modes.
2. Engage students in setting learning goals.
Ensuring students understand how they will be graded is key to any good assessment system.

Inviting students to collaboratively establish learning goals and criteria for the task, with consideration for the role of AI software, would help students to evaluate and judge appropriate contexts in which AI can work as a learning tool.

Read more: Unlike with academics and reporters, you can't check when ChatGPT's telling the truth
3. Require students to submit drafts for feedback.
Although students should still complete essay assignments, research into academic integrity policy in response to generative AI suggests students should be required to submit drafts of their work for review and feedback. Apart from helping to detect plagiarism, this kind of "formative assessment" practice is positive for guiding student learning.

Feedback can be offered by the teacher or by students themselves. Peer- and self-feedback can serve to critically evaluate work in progress (or work generated by AI software).
4. Grade subcomponents of the task.
Students could receive a grade for each subcomponent - including their involvement in feedback processes. They would also be evaluated in relation to how well they incorporated and attended to the specific feedback provided.

The assignment becomes bigger than a final essay, it becomes a product of learning, where students' ideas are evaluated from development to final submission.
5. Move to more authentic assessments or include performance elements.
Good assessment practice involves an educator observing student learning across multiple contexts.

For example, educators can invite students to present their work, discuss an essay in a conference format or share a video articulation or an artistic representation. The aim here is to encourage students to share their learning through an alternative format. An important question to ask is whether or not you need the essay component at all? Is there a more authentic way to effectively assess student learning?

Authentic assessments are those that relate content to context. When students are asked to do this, they must apply knowledge in more practical settings, often making AI tools less helpful.

For help in rethinking assessment practices towards more authentic and alternative approaches, educators can consider taking the free course, Transforming Assessment: Strategies for Higher Education.

Improve benefits for students

Collectively, these suggestions may be more time-consuming, particularly in larger undergraduate classes.

But they do provide greater learning and synergy between forms of assessment that benefit students: formative assessment to guide teaching and learning, and "summative assessment," primarily used for grading and evaluation purposes.

AI is here and here to stay, and we must embrace it as part of our learning environment. Incorporating AI into how we assess student learning will yield more reliable assessment processes and valid and valued assessment outcomes.

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ChatGPT and cheating: 5 ways to change how students are graded

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Educators need to carefully consider ChatGPT and issues of academic integrity to move toward an assessment system that leverages AI tools.

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QAA resources: Academic integrity - ChatGPT and artificial intelligence

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QAA resources: Academic integrity - ChatGPT and artificial intelligence

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https://www.qaa.ac.uk//en/membership/membership-areas-of-work/academic-integrity/chatgpt-and-artificial-intelligence

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QAA has curated a range of resources relating to Generative AI (Gen AI) and the ways it can be used as a positive tool while also maintaining academic standards.

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Text-based games have been a staple of computer gaming since the early days of computing. These games use text to create immersive, interactive experiences that are easy to play and accessible to a wide audience.

This blog post will revisit old-school games ( and some creative adventures) and ask chatGPT to play them with us.

Let's get started!

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Text-based games have been a staple of computer gaming since the early days of computing. These games use text to create immersive, interactive experiences that are easy to play and accessible to a wide audience. This blog post will revisit old-school games ( and some creative adventures) and ask chatGPT to play them with us.

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How ChatGPT really works, explained for non-technical people

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The release of ChatGPT by OpenAI at the end of last year has been phenomenal - even my grandma is asking about it. Its capabilities to generate human-like language have inspired people to experiment with its potential in various products. Its wildly successful launch even put pressure on tech giants like Google to rush to release their own version of ChatGPT.

But let's be honest, for non-technical product managers, designers, and entrepreneurs, the inner workings of ChatGPT may seem like a magical black box. Don't worry! In this blog, I'll try to explain the technology and the model behind ChatGPT as simply as possible. By the end of this post, you'll have a good understanding of what ChatGPT can do, and how it performs its magic.

Before we dive deep into the actual mechanism of ChatGPT, let's take a quick look at the timeline of the development of the transformer architecture of language models and the different versions of GPT, so that you can have a better sense of how things evolved into the ChatGPT we have today.

2015. OpenAI was founded by Sam Altman, Elon Musk, Greg Brockman, Peter Thiel, and others. OpenAI develops many different AI models other than GPT.

2017. Google published the paper Attention is All You Need, which introduced the transformer architecture [2]. The transformer is a neural network architecture that lays the foundation for many state-of-the-art (SOTA) large language models (LLM) like GPT.

2018. GPT is introduced in Improving Language Understanding by Generative Pre-training [3]. It's based on a modified transformer architecture and pre-trained on a large corpus.

2019. GPT-2 is introduced in Language Models are Unsupervised Multitask Learners [4], which can perform a range of tasks without explicit supervision when training.

2020. GPT-3 is introduced in Language Models are Few-Shot Learners [5], which can perform well with few examples in the prompt without fine-tuning.

2022. InstructGPT is introduced in Training language models to follow instructions with human feedback [6], which can better follow user instructions by fine-tuning with human feedback.

2022. ChatGPT, a sibling of InstructGPT, is introduced in ChatGPT: Optimizing Language Models for Dialogue. It can interact with humans in conversations, thanks to the fine-tuning with human examples and reinforcement learning from human feedback (RLHF).

The timeline shows that GPT evolved from the original transformer architecture and gained its ability through many iterations. If you don't understand the terms like transformers, pre-training, fine-tuning, or reinforcement learning from human feedback, no worries! I'll explain them all in the next sections.

Now that you know ChatGPT is based on transformers and the preceding GPT models, let's take a closer look at the components of those models and how they work. It's okay if you are not familiar with deep learning, neural networks, or AI - I will leave out the equations and explain the concepts using analogies and examples.

In the next sections, I will start with a generalized overview of language models & NLP, moving on to the original transformer architecture, then to how GPT adapted the transformer architecture, and finally how ChatGPT is fine-tuned based on GPT.

There are many types of AI or deep learning models. For natural language processing (NLP) tasks like conversations, speech recognition, translation, and summarization, we will turn to language models to help us.

Language models can learn a library of text (called corpus) and predict words or sequences of words with probabilistic distributions, i.e. how likely a word or sequence can occur. For example, when you say "Tom likes to eat ...", the probability of the next word being "pizza" would be higher than "table". If it's predicting the next word in the sequence, it's called next-token-prediction; if it's predicting a missing word in the sequence, it's called masked language modeling.

Since it's a probability distribution, there can be many probable words with different probabilities. Although you might think it's ideal to always choose the best candidate with the highest probability, it may lead to repetitive sequences. So in practice, researchers would add some randomness ( temperature) when choosing the word from the top candidates.

In a typical NLP process, the input text will go through the following steps [8]:

Preprocessing: cleaning the text with techniques like sentence segmentation, tokenization (breaking down the text into small pieces called tokens), stemming (removing suffixes or prefixes), removing stop words, correcting spelling, etc. For example, "Tom likes to eat pizza." would be tokenized into ["Tom", "likes", "to", "eat", "pizza", "."] and stemmed into ["Tom", "like", "to", "eat", "pizza", "."].

Encoding or embedding: turn the cleaned text into a vector of numbers, so that the model can process.

Feeding to model: pass the encoded input to the model for processing.

Getting result: get a result of a probability distribution of potential words represented in vectors of numbers from the model.

Decoding: translate the vector back to human-readable words.

Post-processing: refine the output with spell checking, grammar checking, punctuation, capitalization, etc.

AI researchers have come up with many different model architectures. Transformers, a type of neural network, has been state-of-the-art for recent years and lays the foundation for GPT. In the next section, we'll look at transformers' components and mechanisms.

The transformer architecture is the foundation for GPT. It is a type of neural network, which is similar to the neurons in our human brain. The transformer can understand contexts in sequential data like text, speech, or music better with mechanisms called attention and self-attention.

Attention allows the model to focus on the most relevant parts of the input and output by learning the relevance or similarity between the elements, which are usually represented by vectors. If it focuses on the same sequence, it's called self-attention [2][9].

Let's take the following sentence as an example: "Tom likes to eat apples. He eats them every day." In this sentence, "he" refers to "Tom" and "them" refers to "apples". And the attention mechanism uses a mathematical algorithm to tell the model that those words are related by calculating a similarity score between the word vectors. With this mechanism, transformers can better "make sense" of the meanings in the text sequences in a more coherent way.

Transformers have the following components [2]:

Embedding & Positional Encoding: turning words into vectors of numbers

Encoder: extract features from the input sequence and analyze the meaning and context of it. It outputs a matrix of hidden states for each input token to be passed to the decoder

Decoder: generate the output sequence based on the output from the encoder and the previous output tokens

Linear & Softmax Layer: turning the vector into a probability distribution of output words

The encoder and decoder are the main components of transformer architecture. The encoder is responsible for analyzing and "understanding" the input text and the decoder is responsible for generating output.

If you're interested, you can read on for the details of encoders and decoders. If not, feel free to jump to the next section where we'll cover GPT, which is a variant of transformers.

The encoder is a stack of multiple identical layers (6 in the original transformer paper). Each layer has two sub-layers: a multi-head self-attention layer and a feed-forward layer, with some connections, called residual connection and layer normalization [2]. The multi-head self-attention sub-layer applies the attention mechanism to find the connection/similarity between input tokens to understand the input. The feed-forward sub-layer does some processing before passing the result to the next layer to prevent overfitting. You can think of encoders like reading books - you will pay attention to each new word you read and think about how it's related to the previous words.

The decoder is similar to the encoder in that it's also a stack of identical layers. But each decoder layer has an additional encoder-decoder attention layer between the self-attention and feed-forward layers, to allow the decoder to attend to the input sequence. For example, if you're translating "I love you" (input) to "Je t'aime" (output), you will need to know "Je" and "I" are aligned and "love" and "aime" are aligned.

The multi-head attention layers in the decoder are also different. They're masked to not attend to anything to the right of the current token, which has not been generated yet [2]. You can think of decoders like free-form writing - you write based on what you've written and what you've read, without caring about what you're going to write.

GPT's full name is Generative Pre-trained Transformer. From the name, you can see that it's a generative model, good at generating output; it's pre-trained, meaning it has learned from a large corpus of text data; it's a type of transformer.

In fact, GPT uses only the decoder part of the transformer architecture [3]. From the previous transformers section, we learned that decoders are responsible for predicting the next token in the sequence. GPT repeats this process again and again by using the previously generated results as input to generate longer texts, which is called auto-regressive. For example, if it's translating "I love you" to French, it will first generate "Je", then use the generated "Je" to get "Je t'aime". (See the dashed line in the previous illustration).

In training the first version of GPT, researchers used unsupervised pre-training with the BookCorpus database, consisting of over 7000 unique unpublished books [3]. Unsupervised learning is like having the AI read those books itself and try to learn the general rules of language and words. On top of the pre-training, they also used supervised fine-tuning on specific tasks like summarization or question and answering. Supervised means that they will show the AI examples of requests and correct answers and ask the AI to learn from those examples.

In GPT-2, researchers expanded the size of the model (1.5B parameters) and the corpus they feed to the model with WebText, which is a collection of millions of web pages, during the unsupervised pre-training [4]. With such a big corpus to learn from, the model proved that it can perform very well on a wide range of language related-tasks even without supervised fine-tuning.

In GPT-3, the researchers took a step further in expanding the model to 175 billion parameters and using a huge corpus comprising hundreds of billions of words from the web, books, and Wikipedia. With such a huge model and a big corpus in pre-training, researchers found that GPT-3 can learn to perform tasks better with one (one-shot) or a few examples (few-shot) in the prompt without explicit supervised fine-tuning.

(If you want to learn more about how to prompt the models to produce better results, you can read my other article: How to use ChatGPT in product management)

At this stage, the GPT-3 model is already impressive. But they're more like general-purpose language models. Researchers wanted to explore how it can follow human instructions and have conversations with humans. Therefore, they created InstructGPT and ChatGPT based on the general GPT model. Let's see how they did it in the next section.

After the iterations from GPT to GPT-3 with growing models and corpus size, researchers realized that bigger models don't mean that they can follow human intent well and may produce harmful outputs. Therefore, they attempted to fine-tune GPT-3 with supervised learning and reinforcement learning from human feedback (RLHF) [6][12]. With these training steps came the two fine-tuned models - InstructGPT and ChatGPT.

The first step is supervised learning from human examples. Researchers first provided the pre-trained GPT with a curated, labeled dataset of prompt and response pairs written by human labelers. This dataset is used to let the model learn the desired behavior from those examples. From this step, they get a supervised fine-tuned (SFT) model.

The second step is training a reward model (RM) to rate the responses from the generative model. Researchers used the SFT model to generate multiple responses from each prompt and asked human labelers to rank the responses from best to worse by quality, engagement, informativeness, safety, coherence, and relevance. The prompts, responses, and rankings are fed to a reward model to learn human preferences of the responses through supervised learning. The reward model can predict a scalar reward value based on how well the response matches human preferences.

In the third step, researchers used the reward model to optimize the SFT model's policy through reinforcement learning. The SFT model will generate a response from a new prompt; the reward model will assess the response and give it a reward value that approximates human preference; the reward is then used to optimize the generative model by updating its parameters. For example, if the generative model generates a response that the reward model thinks humans may like, it will get a positive reward to continue generating similar responses in the future; and vice versa.

Through this process with supervised learning and reinforcement learning from human feedback, the InstructGPT model (with only 1.3B parameters) is able to perform better in tasks that follow human instructions than the much bigger GPT-3 model (with 175 B parameters).

(Note: However, this does not mean that InstructGPT is better than GPT-3 in all aspects or domains. For example, GPT-3 may still have an advantage in generating longer or more creative texts, such as stories or articles.)

ChatGPT is a sibling model to InstructGPT. The training process is similar for ChatGPT and InstructGPT, including the same methods of supervised learning and RLHF we covered earlier. The main difference is that ChatGPT is trained with examples from conversational tasks, like question answering, chit-chat, trivia, etc [7]. Through this training, ChatGPT can have natural conversations with humans in dialogues. In conversations, ChatGPT can answer follow-up questions and admit mistakes, making it more engaging to interact with.

With the previous explanations, I hope you have a clearer understanding of how the model behind ChatGPT works and how it evolved into what it is today.

As a quick recap, here are the most important takeaways and the limitations of GPT models:

ChatGPT is based on a decoder-only auto-regressive transformer model to take in a sequence of text and output a probability distribution of tokens in the sequence, generating one token at a time iteratively.

Since it doesn't have the ability to search for references in real-time, it's making probabilistic predictions in the generation process based on the corpus it has been trained on, which can lead to false claims about facts.

It's pre-trained on a huge corpus of web and book data and fine-tuned with human conversation examples through supervised learning and reinforcement learning from human feedback (RLHF).

Its capability is mainly based on its model size and the quality and size of the corpus and examples it learned from. With some additional supervised learning or RLHF, it can perform better in specific contexts or tasks.

Since the corpus comes from web content and books, they might have biases that the model can learn from, especially for social, cultural, political, or gender-based biases, resulting in biased responses to some requests.

Stay curious and open to new technologies like ChatGPT. I believe an open mindset and curiosity can help us non-technical product managers, designers, and entrepreneurs navigate this new wave of technological revolution.

References

[1] "Introducing OpenAI." OpenAI, 12 Dec. 2015, https://openai.com/blog/introducing-openai/.

[2] Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017).

[3] Radford, Alec, et al. "Improving language understanding by generative pre-training." (2018).

[4] Radford, Alec, et al. "Language models are unsupervised multitask learners." OpenAI blog 1.8 (2019): 9.

[5] Brown, Tom, et al. "Language models are few-shot learners." Advances in neural information processing systems 33 (2020): 1877-1901.

[6] Ouyang, Long, et al. "Training language models to follow instructions with human feedback." arXiv preprint arXiv:2203.02155 (2022).

[7] "ChatGPT: Optimizing Language Models for Dialogue." OpenAI, 30 Nov. 2022, https://openai.com/blog/chatgpt/.

[8] Murali, Aishwarya. "A Guide to Perform 5 Important Steps of NLP Using Python." Analytics Vidhya, 17 Aug. 2021, https://www.analyticsvidhya.com/blog/2021/08/a-guide-to-perform-5-important-steps-of-nlp-using-python/.

[9] Cristina, Stefania. "The Transformer Attention Mechanism." Machine Learning Mastery, 15 Sept. 2022, https://machinelearningmastery.com/the-transformer-attention-mechanism/.

[10] Doshi, Ketan. "Transformers Explained Visually (Part 1): Overview of Functionality." Medium, 3 June 2021, https://towardsdatascience.com/transformers-explained-visually-part-1-overview-of-functionality-95a6dd460452.

[11] Kosar, Vaclav. Feed-Forward, Self-Attention & Key-Value. 2 Jan. 2021, https://vaclavkosar.com/ml/Feed-Forward-Self-Attendion-Key-Value-Memory.

[12] "Aligning Language Models to Follow Instructions." OpenAI, 27 Jan. 2022, https://openai.com/blog/instruction-following/.

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How ChatGPT really works, explained for non-technical people

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The release of ChatGPT by OpenAI at the end of last year has been phenomenal - even my grandma is asking about it. Its capabilities to generate human-like language have inspired people to experiment with its potential in various products.

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Cradle Deakin: ChatGPT - how should educators respond?

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Cradle Deakin: ChatGPT - how should educators respond?

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https://youtu.be/V57mT1hrc24

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ChatGPT -  how should educators respond?

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Oxford and Cambridge ban ChatGPT  over plagiarism fears but other universities embrace AI bot

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Oxford and Cambridge ban ChatGPT  over plagiarism fears but other universities embrace AI bot

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https://inews.co.uk/news/oxford-cambridge-ban-chatgpt-plagiarism-universities-2178391?ITO=newsnow

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A University of Oxford spokesperson told i that students have been informed that they are not permitted to use ChatGPT in assessed work 'until further notice' (Photo: Getty)

The UK's top universities are split over how to respond to ChatGPT, i can reveal, with Oxford and Cambridge among those banning the technology over plagiarism fears, while others have opted to embrace it.

Eight out of 24 of the elite Russell Group universities have informed students that using the AI bot for assignments will count as academic misconduct, including Manchester, Bristol, Edinburgh and Oxbridge.

Dozens of other universities across the country, including Durham, Liverpool and Northampton, are scrambling to review their plagiarism policies in time for this year's assessments, after ChatGPT exploded onto the scene in November.

The chatbot, created by Elon Musk-founded tech firm OpenAI, scours vast swathes of the internet for answers to users' questions. Unlike other AI bots, it coughs up results in fluent, sophisticated language, which users can tailor to their desired length. And unlike other AI bots, it's free.

The automaton, which has crashed frequently in the past few months after becoming clogged with users, can wax lyrical about the Odyssey, compare postmodernism with modernism, and assess the relative benefits of interactions between genes.

In short, ChatGPT could spell the death of the traditional essay, which has remained the chief form of assessment for centuries.

Its sudden emergence late last year sparked panic across the education sector, with experts warning that universities must reform their approach to assignments or face an endless churn of cyborg dissertations.

Almost 40 per cent of all universities across Britain told i they had either banned ChatGPT or were in the process of revising their policies to outlaw the chatbot.

In total, 28 universities approached by i said they had updated their plagiarism policies to explicitly prohibit the use of ChatGPT for essays and coursework - almost 20 per cent of all British universities.

A University of Oxford spokesperson told i: "Students have been informed that until further notice we will not permit the use of AI tools such as ChatGPT in their assessed work."

They said the university would be issuing fresh guidance to students shortly and will work with exam boards "to minimise problems and concerns in the current assessment cycle".

The University of Cambridge told i that it had drawn up specific guidance for each department to address concerns about "risks to the integrity of assessments".

A spokesperson said: "Students must be the authors of their own work. Content produced by AI platforms, such as ChatGPT, does not represent the student's own original work so would be considered a form of academic misconduct to be dealt with under the university's disciplinary procedures."

The university's sanctions for plagiarism include an automatic mark of zero for a paper or a student's whole-year assessment being downgraded, while punishments for producing work on someone else's behalf include being stripped of any academic qualifications handed out by the university.

A further 28 universities said they were in the process of revising their misconduct policies to take ChatGPT into account, with many scrambling to do so before exam season kicks in this summer.

The universities of Northumbria, Northampton and Essex said they were reviewing their existing policies to deal with "emerging technologies," while Glasgow Caledonian University said it would inform academic staff "what to look out for when marking assessments in order to help identify work that has not been written by a student".

But others have taken a more holistic approach, telling i they were open to the academic opportunities that AI chatbots such as ChatGPT might usher in.

Glasgow University said its staff were keen to explore "how students can be advised to use large language models responsibly in their coursework".

Meanwhile, Heriot-Watt in Edinburgh told i the software raises "exciting opportunities" and that the university would explore how to integrate it into assessments to "help students become true global change-makers".

University College London (UCL) has even updated its website to spell out how students might use ChatGPT to save time on usually laborious bibliographies, acknowledgements and references.

"We believe these tools are potentially transformative as well as disruptive," the Russell Group university said on its website. "We will support you in using them effectively, ethically and transparently."

The variation across the university sector represents the fight-or-flight tussle with ChatGPT that many institutions are currently grappling with.

Last week, i revealed that civil servants have been asked not to use the AI chatbot to write Government policies or correspondence.

The Foreign Office has also warned prospective applicants to its student essay prize that it will be scanning all entrances for the robotic fingerprints of ChatGPT - although it was unable to name which software it would be using when asked by i.

In contrast, the International Baccalaureate (IB) said that said schoolchildren will be allowed to use the chatbot in their essays.

Matt Glanville, the IB's head of assessment principles and practice, said the chatbot should be embraced as "an extraordinary opportunity", though he cautioned that students must be clear when they were quoting its responses.

Politicians and experts have also warmed up to the opportunities that ChatGPT could unlock, including saving time over burdensome tasks.

Chancellor Jeremy Hunt boasted about using the software to help craft a speech on the UK's economic future at a Bloomberg reception last month.

Meanwhile​​, officials at Vanderbilt University in the US were forced to apologise last week after they admitted to using ChatGPT to write a consoling email to students after a mass shooting at Michigan State University.

Experts told i that as much as institutions might try and wrestle against the AI bot, ChatGPT is here to stay.

Mike Sharples, emeritus professor of educational technology at The Open University - which is yet to update its plagiarism policy - told i: "We need to start thinking about this now, not in the future, because students are already using it. The education system has got to catch up with what's already happening."

At the moment, essays written by ChatGPT are easy to sniff out. Last year, made the software write answers to GCSE history questions and had them marked by a secondary school teacher, who said they were patchy, "hollow," and at times factually incorrect. He gave them a Grade 7 - equivalent to a low A grade under the former marking system.

But Mr Sharples warned that a more streamlined version is just around the corner, and one that could just bring about the democratisation of shortcuts that have always existed on some level - including expensive essay mills, which unfairly favour rich students.

Turnitin, the plagiarism detection used by 98 per cent of British universities, has fast-tracked the development of a more streamlined system capable of picking up the tell-tale signs of ChatGPT. The update is due to launch in April 2023, but it remains unclear whether universities will have to fork out extra for it.

Until that hits the market, universities have no real way of checking for signs of meddling by ChatGPT, and experts have cautioned that the UK might still be some way off a watertight detection software. In the meantime, universities might ask themselves: Is ChatGPT the future of cheating or the future of teaching?

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Oxford and Cambridge ban ChatGPT over plagiarism fears but other universities embrace AI bot

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The UK's top universities are split over how to respond to ChatGPT, i can reveal, with Oxford and Cambridge among those banning the technology over plagiarism fears, while others have opted to embrace it. Eight out of 24 of the elite Russell Group universities have informed students that using the AI bot for assignments will count as academic misconduct, including Manchester, Bristol, Edinburgh and Oxbridge.

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Cecilia Chan Keynote. Preparing Students for the Future: The Importance of 21st Century Skills in the integration of AI and ChatGPT in Higher Ed. 

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Cecilia Chan Keynote. Preparing Students for the Future: The Importance of 21st Century Skills in the integration of AI and ChatGPT in Higher Ed. 

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https://www.youtube.com/watch?v=Ojzoy1IBf-Q

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기관아이디/대학혁신과공유센터

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제10회 대학교육혁신 국제포럼(The 10th International Forum on Innovation in Higher Education)

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HEPI - exploring generative AI in higher education

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HEPI - exploring generative AI in higher education

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https://www.hepi.ac.uk/2023/02/28/exploring-generative-ai-in-higher-education%EF%BF%BC/

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The higher education sector is undergoing a technological revolution, with AI tools like ChatGPT, GitHub Copilot, and Midjourney leading the charge. These tools are transforming the way students learn and the way universities and workplaces operate.
HEPI / Curio Questionnaire: Contribute your views We want to ensure that your institution is at the forefront of this change. HEPI and Curio are researching the impact of AI tools on universities in the UK. We are inviting you to play a crucial role in shaping the future of higher education by participating in a questionnaire to capture your insights and perspectives. To participate in the questionnaire, please follow this link: https://forms.office.com/r/g1t3hDik9L Generative AI and higher education
Have you heard of generative AI? It's a fascinating field of artificial intelligence that's sweeping the world and making waves in higher education. AI tools are transforming the way students learn and the way universities and workplaces operate. But what is it, and why should we care?

In a nutshell, generative AI involves teaching a machine to learn from existing data and then use that knowledge to generate new data that is similar to the original dataset. This could include anything from text, images and even sounds. The goal is to mimic the way humans create new ideas, concepts and designs that are both diverse and novel.
ChatGPT
One of the best-known examples of generative AI is ChatGPT. It's a large language model developed by OpenAI that can generate human-like text based on the data it has learned. For instance, if you give ChatGPT the prompt, 'The first person to walk on the Moon was,' it will likely generate the response 'Neil Armstrong' based on the statistical distribution of words in English text.

But ChatGPT is just the beginning. Other generative AI tools are emerging, such as Bing's ChatGPT-powered search capability and Google Bard, Alphabet's equivalent project. Additionally, there are code generators like GitHub Copilot, text-to-image tools such as DALL-E 2 and text-to-speech tools like Murf.ai and Speechify.
Opportunities and challenges
As with any emerging technology, there are both opportunities and challenges that come with generative AI. With the right approach, these tools can accelerate learning and the development of higher-order thinking skills. On the other hand, concerns about the widespread use of generative AI in assessments have arisen, particularly around ensuring the integrity of student work. Some have pointed out this is a chance to rethink higher education assessment.

In the higher education sector, generative AI can be a game-changer. It offers learners the chance to focus on other literacies and transferable skills, while also challenging educators to create more authentic assessments. Authentic assessments are designed to measure students' knowledge and skills in a way that is relevant to their lives and future careers.

Of course, it's important to consider the implications of generative AI on more broadly. With the rapid proliferation of AI-generated content and disinformation, there is a need to refocus education on critical consumption, or what Howard Rheingold called ' crap detection '. As long as we approach the use of generative AI in education with a thoughtful and nuanced perspective, it has the potential to revolutionise the learning experience.

A further challenge is that the field of generative AI is quickly evolving, and universities are struggling to keep up with the educational policy, practice, and technology changes that come with it. It's important that we as educators and thought leaders stay engaged in the conversation and continue to explore the opportunities and challenges that come with this emerging field.

If you're interested in contributing to the conversation, we encourage you to participate in a questionnaire linked at the top of this article. Your insights can help shape the future of generative AI and its impact on education.
About Curio We advise institutions, organisations, and thought leaders to translate expertise into forward-thinking learning and development solutions. If you are facing some of the challenges raised in this article and would like a deeper dive, please feel free to reach out at hello@curio.co.

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Exploring Generative AI in higher education - HEPI

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By Matt Riddle, Principal and Director of Learning Experiences, Curio The higher education sector is undergoing a technological revolution, with AI tools like ChatGPT, GitHub Copilot, and Midjourney leading the charge. These tools are transforming the way students learn and the way universities and workplaces operate.

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https://drsaraheaton.wordpress.com/2023/02/25/6-tenets-of-postplagiarism-writing-in-the-age-of-artificial-intelligence/

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Why using AI tools like ChatGPT in my MBA innovation course is expected and not cheating

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Why using AI tools like ChatGPT in my MBA innovation course is expected and not cheating

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https://theconversation.com/why-using-ai-tools-like-chatgpt-in-my-mba-innovation-course-is-expected-and-not-cheating-198957?_cldee=lxKQ7rNRQIIZsUBM1Uq56G-HIX4TltbMutWeN3auXqkGyaAIq_q0k9URpRhitBRN&recipientid=lead-f45fd3fb1688ed1181ac000d3a1c0c13-523ad8ed3e8f4fd59eda2b5911ab1bcb&utm_source=ClickDimensions&utm_medium=email&utm_campaign=LINK&esid=df6935ac-3ab1-ed11-83fe-000d3a1ba68c

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I teach managing technological innovation in Simon Fraser University's Management of Technology MBA program. Thanks to the explosion of generative artificial intelligence, I'm rewriting my 2023 syllabus and assignments.

No matter our industry or field, we should regularly review our tools and workflows. New tools, like AI, are excellent triggers for this assessment. Sorting out how best to adjust our work, as per the values and existing norms of different fields, takes a systematic approach.

My research examines how companies can adjust how they use talent, technology and technique to hit work targets and stay aligned with the times - what I've called thinking in 5T.

Educators in MBA programs, who are concerned with building students' professional capacities, can also use this lens to support the critical thinking that students need. We can help students consider how and when to use AI in their academic and professional lives.

Abrupt availability of AI tools

ChatGPT, DALL-E and Writesonic are examples of publicly available generative AI. These are "generative" in that humans provide a prompt and the AI outputs text or images based on machine learning.

I didn't think to mention generative AI in my September 2022 syllabus. Class discussions included my expectation that students would use Grammarly or other proofreading tools to support their professional writing.

We discussed different citation styles for business writing and how incorrectly citing sources can negatively affect one's career.

Some students asked whether Grammarly's more sophisticated ability to rewrite sentences was a problem. I said, no, it's an innovation course and we should use the tools we have.

Considering social and technical aspects

The 5T framework is my modernized presentation of sociotechnical systems theory - a theory describing how workers and leaders must manage social and technical aspects of work to achieve performance and well-being.

Thinking in 5T means you set a target and then consider:

the times (context) in which you make a decision;

available talent (knowledge, skills, abilities, human reactions, limitations);

technology (from AI and smart watches to shovels and conference room furniture);

technique (practices, workflows and so on) as you look for the right balance of all these elements.

Research suggests that people who are more systems savvy have a greater ability to see the connections across these different domains and construct synergies appropriate for their work.

No silver bullet

Thinking in 5T means you never expect a " silver bullet" change to work: for example, just blocking ChatGPT on an organization's network with no other adjustments. Instead, you look to manage all aspects of your human and technological variables.

My target is for my students to improve their ability to identify and evaluate existing innovations and create valuable new ones.

To date, my syllabus has said "your final submission must be your individual work and words," but now I will need to clarify what this means.

Learning how to use AI

I agree with Kevin Kelly of Wired that asking ChatGPT how to do things - in technical terms, writing AI prompts - requires work and expertise. We also need to be careful consumers of what the generative AI produce.

Generative AI are often wrong. Both students of innovation and business professionals will need to understand how the tools generate responses to assure factual answers and correct references.

Read more: Unlike with academics and reporters, you can't check when ChatGPT's telling the truth

Beyond fact-checking, my students must use critical thinking and show they can apply course concepts. As I teach innovation skills, we can cover how to engage with ChatGPT and other generative AI effectively.

My innovation students create personalized templates that allow them to take course concepts, apply them in the real world and improve the application of these concepts throughout their careers. How might students write an AI prompt for ChatGPT to help them use design thinking in their work?

An effective ChatGPT prompt would be: "Create a playbook to support design thinking. Include alternatives for expert versus novice team members and teams working virtually versus face to face."

Such a prompt guides ChatGPT to return a response drawing on both the social and technical aspects of work - the thinking in 5T approach from my course.

Academic integrity

While academic discussions are ongoing about the ethical and knowledge implications of using generative AI, academic integrity does provide some firm boundaries.

Read more: Unlike with academics and reporters, you can't check when ChatGPT's telling the truth

For example, at Simon Fraser University, students must demonstrate " a commitment not to engage in or tolerate acts of falsification, misrepresentation or deception."

In my course, the notion of "individual work" must change.

I'll be adjusting the assignments and requiring an appendix describing the toolkit and practices students use. Using AI is not cheating in my course, but misrepresenting your sources is.

Work doesn't exist in a vacuum

The AI will get better, and there will be more of them. Guidelines in work and education need to keep pace and be thoughtfully aligned to how knowledge is constructed in different fields.

We're learning that some journals won't accept AI as credited authors. Other publishers have announced that while you can't list ChatGPT as an author, AI tools can be used in some stages of preparation, as long as you disclose this in the manuscript.

We need the various manuals of style to update their rules to include work generated by an AI. Given the pace of AI change, writers may need to highlight the specific versions of the AI they use (much as the APA Style requests dates for Wikipedia articles).

I like an approach some photographers use: share your tools and critical settings.

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Why using AI tools like ChatGPT in my MBA innovation course is expected and not cheating

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I teach managing technological innovation in Simon Fraser University's Management of Technology MBA program. Thanks to the explosion of generative artificial intelligence, I'm rewriting my 2023 syllabus and assignments. No matter our industry or field, we should regularly review our tools and workflows. New tools, like AI, are excellent triggers for this assessment.

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ChatGPT What do you need to know? CRADLE Deakin

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ChatGPT What do you need to know? CRADLE Deakin

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https://youtu.be/mCCqf6tHI24

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Excited, concerned and curious: student perspectives on learning and working in the era of AI

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Excited, concerned and curious: student perspectives on learning and working in the era of AI

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https://www.advance-he.ac.uk/news-and-views/excited-concerned-and-curious-student-perspectives-learning-and-working-era-ai

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'Excited, concerned and curious': student perspectives on learning and working in the era of AI

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Ahead of February's Tweetchat, Kay Hack, Lead Consultant Education at Advance HE, considers the potential impact of AI on the student experience and students' future career choices.

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Mapping the potential of AI in the age of competence based higher education

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Mapping the potential of AI in the age of competence based higher education

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https://wonkhe.com/blogs/mapping-the-potential-of-ai-in-the-age-of-competence-based-higher-education/

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A competence based approach to thinking about higher education can offer an answer to the growing conversation about the impact of artificial intelligence (AI) on the future of education, and on assessment in particular.

At the University of Hull our academic portfolio is underpinned by our Competence-Based Education Framework, a deliberate response to the challenges presented by the fourth industrial revolution.

We have defined competence as having the necessary experience, knowledge, and self-awareness to do something effectively. We recognise competence in three domains: knowledge management, disciplinary and professional experience, and self-awareness.

Our framework has been developed to ensure that our graduates leave the university possessing those competences that will support their success as they move forward into further study and/or employment. To achieve this aim curriculum designers ensure that programmes include:

The disciplinary knowledge required to be successful in their chosen career.

The technical (disciplinary) role-related skills (e.g. the use of specialist software and/or equipment) and transferable skills (those useful in any role, e.g. communication, teamwork, numeracy) that are essential in their chosen field.

The personal attributes and behaviours necessary for success (e.g. confidence, resilience, adaptability).

Clearly there are opportunities here for us to embed disciplinary appropriate use of AI within the curriculum to reflect the impact it will have within the work place.

Disciplinary applications
"University of Hull graduates are able to source, select and apply disciplinary approaches, knowledge and skills to any given task or practice, and work with autonomy and responsibility."
For students to be able to effectively work with AI, it will be imperative for them to ask the right questions (source), critically analyse (select) and then work with the response (apply) to achieve maximum learning gain.

The same can be said for university educators. AI will have a future in curriculum design, session planning and in marking and feedback, and as a result academics will need to make use of the same competencies.

Professional and workplace applications
"The action and critical thought necessary to address a real-world task/practice in context, be it through working independently or with a team."
While preparing students for the workplace, it is imperative to provide an authentic experience through the taught curriculum and assessment. AI will affect industries in a variety of ways, and university programmes must reflect this. Students will require the opportunity to practise, fail and develop skills using a range of AI supported technologies in order for them to track their development and identify where their disciplinary knowledge co-exists with any automated content.

Current tools, such as Chat GPT, provide a baseline for content, yet require disciplinary and professional experience to contextualise and draw out the necessary knowledge from real-world scenarios. Therefore, we must find opportunities to connect AI with a culture of criticality and reflective practices to prepare students for the ever-changing work landscape.

Knowledge management
"Source, understand, create and communicate knowledge."
With the advent of AI generated content, the ability to effectively source and relate relevant materials has become a crucial competency for students. Utilising tools such as ChatGPT effectively, requires an understanding of how to harness its potential, but also an awareness of its limitations when analysing its output.

Effective use of AI has become a literacy in its own right. As a result, it is now essential that we work in partnership with students in developing a shared understanding of the impact in regards to knowledge management.

This conversation must include:

Appropriate use of tools to generate content

Appropriate acknowledgement of AI content

Use of AI to collect and manage resources

Copyright and intellectual property

Without this, we risk exacerbating the divide in the hidden curriculum, with many students being unaware of the positive possibilities and potential pitfalls.

Self-awareness and personal development
"Self-assessment and regulation in public and private domains, in independent or team working."
As the sector begins to discuss the impact on assessment strategy, many of the creative ways of integrating AI rely on students' personal reflections or, within group work, assessing their own performance alongside that of their team. To support this, we need to design a curriculum that allows students to situate themselves within an authentic learning experience and to equip them with the skills for self-reflection.

AI affords opportunities to provide students with personal pathways for development. The ability for a course to adapt to an individual student will rely on them understanding their own areas of strength and where they have room for development.

Linked to this, self-awareness also covers the ability for students to work in collaboration with academics in designing curriculum and learning activities. Providing a space for them to bring their expertise or experience in this area into their own learning.

Inclusivity

Underpinning these conversations at Hull is our commitment to providing an inclusive and sustainable curriculum. There is still work to be done in understanding the ethical underpinning of currently available tools and where it is appropriate for a university to use them. In regards to sustainability, conversations around the carbon footprint required to power the technology should be at the forefront.

A university wide approach is required to ensure that students receive an equitable experience in this area. When looking at current assessment methods, clear communication, regulations and policies can support student's understanding of how and where it is appropriate to use AI. But there is also a conversation about the wider support that students at all stages have available. For programmes to be able to reflect on where they can integrate AI within their delivery, a similar level of support must be made available to staff.

How AI will impact higher education is unclear but the opportunities that our initial conversations across the sector have established are genuinely exciting. As we support students' move into a workplace of the future, we hope our competency model, focusing on specific skills and application of knowledge, will give us the flexibility and structure required to provide an authentic learning experience.

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Mapping the potential of AI in the age of competence based higher education | Wonkhe

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A competence based approach to thinking about higher education can offer an answer to the growing conversation about the impact of artificial intelligence (AI) on the future of education, and on assessment in particular. At the University of Hull our academic portfolio is underpinned by our Competence-Based Education Framework, a deliberate response to the challenges presented by the fourth industrial revolution.

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ChatGPT, assessment and cheating - have we tried trusting students?

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ChatGPT, assessment and cheating - have we tried trusting students?

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https://wonkhe.com/blogs/chatgpt-assessment-and-cheating-have-we-tried-trusting-students/

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We're sure, like us, you've seen it all in past weeks; from articles suggesting AI can create academic papers good enough for journals, to lecturers being urged to review their assessments in light of ChatGPT's disruptive capabilities.

But are AI text generation tools really the problem? Or do they reveal more serious issues around assessment practices and the academic/student relationship?

Academic integrity

If we continue with current assessment methods, there's no clear solution on the horizon to mitigate against the use of AI tools. We've seen some efforts to employ the "detection tool" approach used for other forms of academic malpractices - but every single one of them has been beaten in practice, and many flag the work of humans as AI derived.

Simply restricting access is not an option - the technical landscape is moving quickly with Microsoft and others are releasing a range of AI enhanced tools (such as Bing Search with ChatGPT) and platforms (such as Microsoft Teams). A myriad of new AI large language models (LLMs) are in the works, or soon to be released, such as Google's Bard, or NVIDIA's NeMo. Moving beyond text, LLMs are moving into sophisticated image and video generation. It's impractical and improbable for students to avoid using such tools as they become ubiquitous in society - a "ban" on such tools would make about as much sense as banning the use of Word's spell checker due to how 'frictionless' they are designed to be.

A starting point for many is that academic integrity is tarnished through students engaging in malpractice (however defined), and even if we accept this deficit framing, it presents a range of both practical and moral problems, as Jan McArthur has argued on Wonkhe in the past in relation to use of plagiarism detection software. Outside of academia, graduates write and generate ideas drawing on a range of sources using a wide variety of tools and approaches - we need to be considering supporting students in using AI as a part of effective academic work and preparation for graduation (and the participation in work and wider society that follows).

Protect, or accept and progress

"Computer-based maths"' is a movement with the aim of reforming maths education. The principle is that curricula (and by extension, assessment) should "assume computers exist", and move students towards a more developmentally effective learning experience. Higher education as a whole cannot get away from "assuming AI exists" and that students will use it - so there is a need to build curricula and assessments around this reality.

So rather than simply focusing on protecting the status quo, we could be working with students to design the assessment of the future. Academics have a responsibility to teach students how to use all kinds of tools and resources (search engines, academic literature databases, open data) in a useful but moral and ethical fashion - and already design assessments with a core focus on academic integrity, but augmented with a sense of trust and an understanding that these tools exist and will (and should) be used.

One of fundamental shifts in assessment is therefore likely to be around defining the level of creativity and originality lecturers expect from students, and what these terms will mean. The future of knowledge work is likely to expect people to range across multiple domains of expertise - so asking the right questions (whether this is of people, data, or artificial intelligence) becomes paramount.

For instance, we can see that the current iteration of AI tends to "hallucinate" many things, providing phrased answers that lack factual rigour. There's been numerous reports of AI generating fake references or inventing entirely spurious facts. Although it is claimed that ChatGPT can write an essay deserving a 2:2, in practice the user requires a great deal of critical reasoning and research to bring the average response up to that standard. Such skills have always been in demand - and it is possible to imagine that "improving" an AI answer in this way as an interesting assessment design.

The QAA recently published a briefing note to support the challenges of academic integrity that the sector is facing. Its advice is clear: communicate early with students and discuss the technology with them; design assessments around co-creation, iteration, and critical thinking skills; and that detection tools are unverified and ineffective. This semester's forthcoming assessment period may be turbulent across the sector; we're never returning to pen and paper assessment, just like we're never going to deny electricity exists. But surely a culture of academic integrity can only be built upon mutual trust and understanding; students don't inherently wish to cheat, and framing these tools as cheating doesn't help anyone.

The next steps

To paraphrase William Gibson, AI is here - but it is not evenly distributed. The currently available technology is provided on a semi-commercial basis. Capabilities and terms of use could change at any point at the whim of owners and operators. For example, ChatGPT is a trained language model owned and operated by some of the biggest tech investors in the world: Peter Thiel, Sam Altman, and Elon Musk to name a few. There is already a premium model (faster responses, more reliable access) and it is not unreasonable to suppose that paid for AI will outstrip the current free and freemium models.

We clearly cannot design assessments based on student access to a paid product without planning to ensure all students can access it on the same terms. We can't build learning and assessment around tools that could disappear or change radically at any moment. We need to assess students based on what they can do, not what they can afford.

As with any new technology there are risks and opportunities to the use of AI in education. Older readers may recall similar concerns as the internet became widely accessible - and downloading an essay from a website replaced the dominant model of buying one from a student in the year above. The technological arms race that followed brought about the rise of the essay mill, and some of the most effective academic malpractice that money could buy. Now that approach has been banned, maybe the latest and greatest AI will pick up the premium misconduct mantle - or something else will arise on the darker corners of the web.

A failure to iterate the design of assessment, and a reliance on the technology of "detection" as a stopgap doesn't just leave academic integrity in question - it poses fundamental questions about structural inequality too.

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ChatGPT, assessment and cheating - have we tried trusting students? | Wonkhe

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We're sure, like us, you've seen it all in past weeks; from articles suggesting AI can create academic papers good enough for journals, to lecturers being urged to review their assessments in light of ChatGPT's disruptive capabilities. But are AI text generation tools really the problem?

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Teaching with AI: Using writing generators in the classroom

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Teaching with AI: Using writing generators in the classroom

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https://docs.google.com/presentation/d/1I7xmR6keQZb4wXj-UwLH00M0xGK-nrqq/mobilepresent?slide=id.p25

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ChatGPT reveals uncomfortable truth about graduate skills

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ChatGPT reveals uncomfortable truth about graduate skills

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https://www.timeshighereducation.com/blog/chatgpt-reveals-uncomfortable-truth-about-graduate-skills

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ChatGPT reveals the uncomfortable truth about graduate skills

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AI-written essays have exposed the unambitious and unoriginal thinking that universities require from undergraduates, says James Warren

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https://blogs.warwick.ac.uk/wjett/entry/ai_for_good/

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AI for Good: Evaluating and Shaping Opportunities of AI in Education

By Isabel Fischer, Leda Mirbahai, and David Buxton

Following the rise of awareness of the opportunities (and threats) of artificial intelligence (AI) in education, we have created a task and finish group which aims to review and 'imagine' the opportunities and challenges of AI in education, incl. assessments. Our vision is to deploy AI as a tool to support all students, independent of background and socio-demographic characteristics, to be successful in their studies and in their future work, while ensuring academic integrity, as well as to support educators feel confident in using AI effectively in promoting learning. We are working in five (sub)groups:

1. General AI in Education (AIEd) Opportunities & information sharing

2. Novel and Diverse Assessment Designs

3. Feedback, Marking, Authorship Detection

4. Designing Teaching Content - 'what is out there being developed?'

5. 'Red Team': AI Ethics and Academic Integrity

As we are still interested in colleagues from within Warwick as well as other institutions and the wider community of stakeholders to join us, here some further information per (sub)group:

1) General AI in Education (AIEd) Opportunities & information sharing: We review how to capture, shape, and disseminate the opportunities for both learner-facing and educator-facing AI, mainly in HE but also considering how HE can support the secondary and even primary school sector (e.g. how to help teachers to experiment with different forms of AI in a low-stake environment). We also consider the benefits, such as reducing inequality, fairness and democratisation that AI offers, evaluating how we can support SDG 4 (equitable and quality education) and SDG 10 (reducing inequalities). We want to help educators to know how to potentially embrace recent AI developments for their professional practice. Combined with sub-group / Strand 5, the 'red team' we also want to inform colleagues on research (similar to mini literature reviews) on topics such as Algorithmic Fairness.

Target Output: A WIHEA page that is informative for colleagues new to AIEd (explanations, links to other resources, links to discussions / brainstorming exercises / blogs, suggestions for their practice)

2) Designing Assessments: We review the opportunities for designing and setting diverse assessments (Learner-facing), including embedding our work within our different Learning Circle's work. It is in this strand that most of the student co-creation will take place.

Target Output: WIHEA page, blogs, and talks

3) Feedback and Marking: We review the opportunities of using AI for formative feedback (Learner-facing), summative feedback (Educator-facing), 'AES - automated essay scoring' (educator-facing), and stylometry (authorship authentication) as well as ChatGPT detection. One aspect of this strand (but not constrained to this strand) is also 'Move fast, Break fast, Learn fast' - doing small scale experiments and testing them (e.g., Consulting Students will experiment with mind maps this term and then can, but don't have to, submit their work to the Warwick AI Essay Analyst for formative feedback and we can analyse their work).

Target Output: A WIHEA page that disseminates information and possibly diffusion of the actual Warwick AI Essay Analyst tool at Warwick, potentially producing research output

4) Designing Module and Lesson Content & Recommendations for institutional Developments / Purchases: Educator-facing, we review tools and initiatives that might help educators in planning and organising their modules and lessons, as well monitoring their email and forum entries. This group looks at all educator-facing areas besides designing assessments (group 2) and providing feedback on assessments (group 3). This group might also make recommendations to the institution on what software to build or to purchase etc.

Target Output: A WIHEA page that disseminates information, possibly making recommendations for in-house developments / purchase of external software packages

5) A 'red team' acknowledges that AI is here to stay and ensures we follow AI Ethics guidelines and that everybody is clear about the risks. This team also reviews and mitigates the challenges to Academic Integrity more broadly. Moreover, it reviews the risk of bought-in products from EdTech and Tech companies, ensuring that AI Ethics is applicable both for in-house and off the shelf, bought-in products.

Target Output: A WIHEA page that provides information for colleagues worried about AIEd (explanations, links to other resources, links to discussions) especially on the topic of AI Ethics and Academic Integrity (what is OK to do, what isn't - where should students / educators draw the line). Collaborating with stand 1, this group might want to explain (do a high-level literature review / providing links to important research) aspects of AI Ethics / Academic Integrity, such as explaining concepts such as 'Algorithmic Fairness'. Building on work by other groups, e.g., last year's 'Online Assessment Data Ethics Group', this group might want to develop a proposal for SLEEC ( https://warwick.ac.uk/services/gov/committees/sleec/) and/or to provide guidance and advice to EPQ on suitable policy and guidance where appropriate.

Proposed Overall Target for entire Task and Finish Group, i.e. across the five groups / strands: Have some tangible outputs (WIHEA page, blogs, talks) that support colleagues when they embrace change in an ethically sound way that respects all stakeholders, especially learners and educators. Ideally collaborating with other universities, other education providers, and industry. Possibly develop a proposal for SLEEC and/or provide guidance and advice to EPQ on suitable policy and guidance where appropriate.

Please email Isabel.fischer@wbs.ac.uk if you are interested in joining any of the groups.

Please email Leda.Mirbahai@warwick.ac.uk if you are interested in joining our open WIHEA Diverse Assessment Learning Circle with interesting talks, such as our talks this month on Synoptic Assessments and on Democratising Assessments.

| | Abigail Ball : Tags: AI Report a problem David Buxton Isabel Fischer Leda Mirbaha Teaching

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AI for Good: Evaluating and Shaping Opportunities of AI in Education, 13/02/23, Warwick Journal of Education - Transforming Teaching Blog

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The blog from Warwick CTE that links teachers to research. Content is invited from ITE students, teachers, education researchers and policy, makers across primary secondary FE and HE

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Exam halls: 'the anachronism we could do without'

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Exam halls: 'the anachronism we could do without'

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CRADLE News: 1,300 years is long enough: it’s pens down for the exam hall

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In this post the use of the exam hall and its relationship to authentic assessment in a digital world is put to the test by Alfred Deakin Professor Liz Johnson, Deputy Vice Chancellor Academic Port…

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https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web

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In 2013, workers at a German construction company noticed something odd about their Xerox photocopier: when they made a copy of the floor plan of a house, the copy differed from the original in a subtle but significant way. In the original floor plan, each of the house's three rooms was accompanied by a rectangle specifying its area: the rooms were 14.13, 21.11, and 17.42 square metres, respectively. However, in the photocopy, all three rooms were labelled as being 14.13 square metres in size. The company contacted the computer scientist David Kriesel to investigate this seemingly inconceivable result. They needed a computer scientist because a modern Xerox photocopier doesn't use the physical xerographic process popularized in the nineteen-sixties. Instead, it scans the document digitally, and then prints the resulting image file. Combine that with the fact that virtually every digital image file is compressed to save space, and a solution to the mystery begins to suggest itself.

Compressing a file requires two steps: first, the encoding, during which the file is converted into a more compact format, and then the decoding, whereby the process is reversed. If the restored file is identical to the original, then the compression process is described as lossless: no information has been discarded. By contrast, if the restored file is only an approximation of the original, the compression is described as lossy: some information has been discarded and is now unrecoverable. Lossless compression is what's typically used for text files and computer programs, because those are domains in which even a single incorrect character has the potential to be disastrous. Lossy compression is often used for photos, audio, and video in situations in which absolute accuracy isn't essential. Most of the time, we don't notice if a picture, song, or movie isn't perfectly reproduced. The loss in fidelity becomes more perceptible only as files are squeezed very tightly. In those cases, we notice what are known as compression artifacts: the fuzziness of the smallest JPEG and MPEG images, or the tinny sound of low-bit-rate MP3s.

Xerox photocopiers use a lossy compression format known as JBIG 2, designed for use with black-and-white images. To save space, the copier identifies similar-looking regions in the image and stores a single copy for all of them; when the file is decompressed, it uses that copy repeatedly to reconstruct the image. It turned out that the photocopier had judged the labels specifying the area of the rooms to be similar enough that it needed to store only one of them-14.13-and it reused that one for all three rooms when printing the floor plan.

The fact that Xerox photocopiers use a lossy compression format instead of a lossless one isn't, in itself, a problem. The problem is that the photocopiers were degrading the image in a subtle way, in which the compression artifacts weren't immediately recognizable. If the photocopier simply produced blurry printouts, everyone would know that they weren't accurate reproductions of the originals. What led to problems was the fact that the photocopier was producing numbers that were readable but incorrect; it made the copies seem accurate when they weren't. (In 2014, Xerox released a patch to correct this issue.)

I think that this incident with the Xerox photocopier is worth bearing in mind today, as we consider OpenAI's ChatGPT and other similar programs, which A.I. researchers call large language models. The resemblance between a photocopier and a large language model might not be immediately apparent-but consider the following scenario. Imagine that you're about to lose your access to the Internet forever. In preparation, you plan to create a compressed copy of all the text on the Web, so that you can store it on a private server. Unfortunately, your private server has only one per cent of the space needed; you can't use a lossless compression algorithm if you want everything to fit. Instead, you write a lossy algorithm that identifies statistical regularities in the text and stores them in a specialized file format. Because you have virtually unlimited computational power to throw at this task, your algorithm can identify extraordinarily nuanced statistical regularities, and this allows you to achieve the desired compression ratio of a hundred to one.

Now, losing your Internet access isn't quite so terrible; you've got all the information on the Web stored on your server. The only catch is that, because the text has been so highly compressed, you can't look for information by searching for an exact quote; you'll never get an exact match, because the words aren't what's being stored. To solve this problem, you create an interface that accepts queries in the form of questions and responds with answers that convey the gist of what you have on your server.

What I've described sounds a lot like ChatGPT, or most any other large language model. Think of ChatGPT as a blurry JPEG of all the text on the Web. It retains much of the information on the Web, in the same way that a JPEG retains much of the information of a higher-resolution image, but, if you're looking for an exact sequence of bits, you won't find it; all you will ever get is an approximation. But, because the approximation is presented in the form of grammatical text, which ChatGPT excels at creating, it's usually acceptable. You're still looking at a blurry JPEG, but the blurriness occurs in a way that doesn't make the picture as a whole look less sharp.

This analogy to lossy compression is not just a way to understand ChatGPT's facility at repackaging information found on the Web by using different words. It's also a way to understand the "hallucinations," or nonsensical answers to factual questions, to which large language models such as ChatGPT are all too prone. These hallucinations are compression artifacts, but-like the incorrect labels generated by the Xerox photocopier-they are plausible enough that identifying them requires comparing them against the originals, which in this case means either the Web or our own knowledge of the world. When we think about them this way, such hallucinations are anything but surprising; if a compression algorithm is designed to reconstruct text after ninety-nine per cent of the original has been discarded, we should expect that significant portions of what it generates will be entirely fabricated.

This analogy makes even more sense when we remember that a common technique used by lossy compression algorithms is interpolation-that is, estimating what's missing by looking at what's on either side of the gap. When an image program is displaying a photo and has to reconstruct a pixel that was lost during the compression process, it looks at the nearby pixels and calculates the average. This is what ChatGPT does when it's prompted to describe, say, losing a sock in the dryer using the style of the Declaration of Independence: it is taking two points in "lexical space" and generating the text that would occupy the location between them. ("When in the Course of human events, it becomes necessary for one to separate his garments from their mates, in order to maintain the cleanliness and order thereof. . . .") ChatGPT is so good at this form of interpolation that people find it entertaining: they've discovered a "blur" tool for paragraphs instead of photos, and are having a blast playing with it.

Given that large language models like ChatGPT are often extolled as the cutting edge of artificial intelligence, it may sound dismissive-or at least deflating-to describe them as lossy text-compression algorithms. I do think that this perspective offers a useful corrective to the tendency to anthropomorphize large language models, but there is another aspect to the compression analogy that is worth considering. Since 2006, an A.I. researcher named Marcus Hutter has offered a cash reward-known as the Prize for Compressing Human Knowledge, or the Hutter Prize-to anyone who can losslessly compress a specific one-gigabyte snapshot of Wikipedia smaller than the previous prize-winner did. You have probably encountered files compressed using the zip file format. The zip format reduces Hutter's one-gigabyte file to about three hundred megabytes; the most recent prize-winner has managed to reduce it to a hundred and fifteen megabytes. This isn't just an exercise in smooshing. Hutter believes that better text compression will be instrumental in the creation of human-level artificial intelligence, in part because the greatest degree of compression can be achieved by understanding the text.

To grasp the proposed relationship between compression and understanding, imagine that you have a text file containing a million examples of addition, subtraction, multiplication, and division. Although any compression algorithm could reduce the size of this file, the way to achieve the greatest compression ratio would probably be to derive the principles of arithmetic and then write the code for a calculator program. Using a calculator, you could perfectly reconstruct not just the million examples in the file but any other example of arithmetic that you might encounter in the future. The same logic applies to the problem of compressing a slice of Wikipedia. If a compression program knows that force equals mass times acceleration, it can discard a lot of words when compressing the pages about physics because it will be able to reconstruct them. Likewise, the more the program knows about supply and demand, the more words it can discard when compressing the pages about economics, and so forth.

Large language models identify statistical regularities in text. Any analysis of the text of the Web will reveal that phrases like "supply is low" often appear in close proximity to phrases like "prices rise." A chatbot that incorporates this correlation might, when asked a question about the effect of supply shortages, respond with an answer about prices increasing. If a large language model has compiled a vast number of correlations between economic terms-so many that it can offer plausible responses to a wide variety of questions-should we say that it actually understands economic theory? Models like ChatGPT aren't eligible for the Hutter Prize for a variety of reasons, one of which is that they don't reconstruct the original text precisely-i.e., they don't perform lossless compression. But is it possible that their lossy compression nonetheless indicates real understanding of the sort that A.I. researchers are interested in?

Let's go back to the example of arithmetic. If you ask GPT-3 (the large-language model that ChatGPT was built from) to add or subtract a pair of numbers, it almost always responds with the correct answer when the numbers have only two digits. But its accuracy worsens significantly with larger numbers, falling to ten per cent when the numbers have five digits. Most of the correct answers that GPT-3 gives are not found on the Web-there aren't many Web pages that contain the text "245 + 821," for example-so it's not engaged in simple memorization. But, despite ingesting a vast amount of information, it hasn't been able to derive the principles of arithmetic, either. A close examination of GPT-3's incorrect answers suggests that it doesn't carry the "1" when performing arithmetic. The Web certainly contains explanations of carrying the "1," but GPT-3 isn't able to incorporate those explanations. GPT-3's statistical analysis of examples of arithmetic enables it to produce a superficial approximation of the real thing, but no more than that.

Given GPT-3's failure at a subject taught in elementary school, how can we explain the fact that it sometimes appears to perform well at writing college-level essays? Even though large language models often hallucinate, when they're lucid they sound like they actually understand subjects like economic theory. Perhaps arithmetic is a special case, one for which large language models are poorly suited. Is it possible that, in areas outside addition and subtraction, statistical regularities in text actually do correspond to genuine knowledge of the real world?

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The noted speculative-fiction writer Ted Chiang on OpenAI's chatbot ChatGPT, which, he says, does little more than paraphrase what's already on the Internet.

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A non-technical introduction to Chat GPT

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A non-technical introduction to Chat GPT

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https://youtu.be/zErbk3k6lD4
https://www.slideshare.net/suebeckingham/a-nontechnical-introduction-to-chatgpt-sedapptx

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A non-technical introduction to ChatGPT, Sue Beckingham and Peter Hartley

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https://lydia-arnold.com/2023/02/15/ai-and-assessment-in-higher-education-reflections/

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*Disclaimer - this article was written with GPT, but not by GPT - explanation within.

There has been a great deal of buzz surrounding the emergence of AI services, with ChatGTP being one of the most widely explored. However, other services are quickly emerging, and as we work to make sense of this technology, it's clear that educators may feel both unsettled and excited. The use of AI raises immediate questions about how we, in higher education, assess our students, as well as more all-encompassing questions about what it means to learn and the role of universities. I find this technology fascinating - it feels like a step-change. Although we remain uncertain about what lies ahead, AI feels like a significant leap in possibility. I am adding a few thoughts as I work to make my own sense of the situation.

While social media is full of ways to make the most of these platforms, from using them to generate business insights to harnessing their power to polish writing skills, conversations around AI are sometimes fearful, angry, and tinged with exasperation as this is another thing to contend with in an already demanding sector. As we try to reconcile these different perspectives, it is essential that we take a thoughtful, informed, and critical approach to the use of AI in assessment.

In practice I am observing three types of responses from colleagues and students:

1. go back to unseen exams or verbal assessment - 'it's the only way' (REVERT).

2. design questions or tasks which cannot be answered by AI technology (OUTRUN) e.g. ask questions which draw upon specific in-class resources or practical experiences.

3. embrace the bot and ask students to use it as the basis for an answer of higher quality or the development of different skills (EMBRACE) e.g. generate a 'first answer' through AI and then undertake research to build up the piece; discuss how you developed the first draft; or, use AI to learn about different ways of presenting your topic by asking questions; then show how you use that advice to create a presentation.

While reverting to traditional assessment methods may solve some problems, it can send us back to an assessment landscape that flatters some students disproportionately and unjustly at the expense of others. Similarly, attempting to outrun AI technology by designing tasks that cannot be answered by AI is a gamble, as AI capabilities continue to evolve rapidly. Instead, embracing AI and asking students to use it as a tool to improve their work has the potential to magnify the benefits.

Authentic assessment, which, according to my own earlier definition (2022) is an assessment relevant to future employment, the advancement of the discipline, our collective future, or individual aspiration and which often mirrors real, complex challenges, is a widely advocated approach to assessment. AI could be a natural ally to authentic assessment, as it can be used to advance current understanding in the discipline, help students work with uncertainty, and trigger reflections on the process of creating understanding.

If we begin to use AI in authentic assessment we must be aware of our own assumptions about what is 'relevant' and therefore authentic in a world with AI. We may have previously focussed on how students can undertake a specific task such as writing a letter for a job application, diagnosing from a list of symptoms, describing the characteristics of a small island landscape, or writing a basic computer code; AI may render some or all of this redundant. This doesn't mean we should always stop doing these tasks, but we must address their relevance and be clear about why we still do them when AI could. For example, one explanation is teaching a topic from first principles helps students to master skills that can then be used to engage critically with AI and take our work to an even higher level.

To balance this piece and, ironically, in response to feedback from ChatGTP, it is important to acknowledge that embracing AI is not without risk which includes "overreliance on technology and the potential for students to "game the system" by exploiting the limitations of AI" (ChatGTP, 2023).

We need to approach this moment in an informed and critical manner with curiosity to understand the issues. We must avoid fearful and protective nostalgia that leads us nowhere productive. Our disciplines and professions are different, and so we may need to approach AI in different ways. Respect is needed between those with different views.

My call to action (again as requested by my chatGPT feedback) is that we get on and use this facility and gain some first-hand experiences to inform our own positioning and our work with students.
Post Script notes on my experience of editing with ChatGTP:

I used Chat GPT to develop this article in two ways.

I asked for feedback (and I used this)- posted here below.

I asked ChatGPT to rewrite my article - which it did (twice at my request).

In all honesty, I found the feedback helpful.

The rewrite gave me mixed emotions - it made me think I couldn't write (that was the emotional reaction). Then I got selective and thought about which bits were better and which were worse (reaction 2). I used it to selectively edit. Sometimes it felt like my personality was being removed by the rewrite. I guess that's not surprising. It made me notice my own original voice more.

As an aside, I question whether students would always have the confidence to choose their voice over a 'corrected' or 'improved' version even when they liked their own work more- in embracing AI we have work to do to support students to make such choices.

The step I didn't go through, which I ideally should, was to compare the versions and actively consider what could be learnt for other pieces of writing.

On the upside - I may have fewer typos than usual!

The published version is not a Chat GPT rewrite alone - it is a rewrite and then a fusion of versions with further editing.

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AI and Assessment in Higher Education: Reflections

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Disclaimer - this article was written with GPT, but not by GPT - explanation within. There has been a great deal of buzz surrounding the emergence of AI services, with ChatGTP being one of the most widely explored. However, other services are quickly emerging, and as we work to make sense of this technology, it's...

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Why all our classes suddenly became AI classes

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Why all our classes suddenly became AI classes

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Artificial intelligence (AI) panic is spreading across the education sector. The newest wave of generative AI tools, released without guidance or clear suggestions for educators late last year, might be among the biggest and fastest transformations to hit academia. Its rapid adoption instantly challenges the existence of a valuable and widely used type of assignment-the essay-along with dozens of other assignment types in fields from programming to poetry.

Cheating is an obvious concern among educators. Evaluations may require a return to oral exams and writing longhand in exam books. Or maybe AI-written text will be identifiable by other AIs. Or assignment types will adjust to the new reality. Either way, the cheating problem will be solved eventually.

But focusing on the problems distracts us from the opportunities AI can provide-including helping us teach in new ways. That starts with educators becoming familiar with AI tools, especially large language models (LLMs) like those underlying ChatGPT and OpenAI Playground, and taking time to experiment with these systems and get a sense of what they can and cannot do.

Focusing on the problems distracts us from the opportunities AI can provide-including helping us teach in new ways.

As of now, AI is great at creating credible-sounding facts, but will often share false data, made-up citations, and incorrect information. Thus, students must have enough knowledge about the topic they are examining to produce sufficient results. Receiving good results from these tools also takes practice. We must train our students to properly use this technology by working interactively with AI models to generate more meaningful and interesting text. This will help them to be successful in their studies and better prepared for their futures. These tools will be part of their lives, so they should learn how to apply them effectively.

Integrating AI into our classrooms isn't always easy-it takes time and dedication. To help, we are sharing some ways we are experimenting with AI to raise student expectations and enhance learning. But first, we propose you start your AI journey by creating a classroom policy for these tools to help your students better understand and properly use them.

Crafting an AI Policy for Your Class

AI is a tool that has tremendous potential. But, like all tools, it has its advantages and disadvantages. To create productive and meaningful content with AI, our students must learn to work with ChatGPT and other LLMs.

Educators can help by creating an environment in which students get a chance to practice doing just that. The first step is to be transparent about our expectations. We must set a clear classroom policy that forecasts potential problems and offers guidance for acceptable use. We've found that providing this information seems to give students permission to explore and understand the tool. It also offers clarity and teaches them how to use AI honestly.

Having a clear policy around AI use is step one in creating a classroom environment in which AI can be embraced responsibly and honestly.

We created an AI policy for undergraduate and MBA students and found they really appreciate the direction and transparency of expectations. Further, assignments and projects continue to be thoughtful and writing quality seems to improve.

Here are our suggestions for crafting your own policy.

Your policy should include these considerations:

Under what circumstances AI use is permitted or forbidden

How students should cite or credit AI

A warning about the technology's tendency toward hallucination (i.e., deceptive data) and clear rules regarding students' accountability for AI output

A notice about using AI ethically and responsibly

Discussion of the need to use AI as a tool to learn, not just to produce content

An example policy:

"I expect you to use AI (e.g., ChatGPT and image generation tools) in this class. In fact, some assignments will require it. Learning to use AI is an emerging skill and I provide tutorials on how to use them. I am happy to meet and help you with these tools during office hours or after class.

"Be aware of the limits of ChatGPT, such as the following:

If you provide minimum-effort prompts, you will get low-quality results. You will need to refine your prompts in order to get good outcomes. This will take work.

Don't trust anything it says. If it gives you a number or fact, assume it is wrong unless you either know the answer or can check with another source. You will be responsible for any errors or omissions provided by the tool. It works best for topics you understand.

AI is a tool, but one that you need to acknowledge using. Please include a paragraph at the end of any assignment that uses AI explaining what you used the AI for and what prompts you used to get the results. Failure to do so is in violation of academic honesty policies.

Be thoughtful about when this tool is useful. Don't use it if it isn't appropriate for the case or circumstance."

Feel free to use this language or adjust it to fit in your own course.

Having a clear policy around AI use is step one in creating a classroom environment in which AI can be embraced responsibly and honestly. You are also welcome to use our tutorials and modify them as needed.

Now that you have a policy, it's time to think about how you might use these AI tools to enhance student learning. Here are two use cases to try in your classes.

Using AI to Raise Expectations-and Level the Playing Field

Our students have tremendous skills, but not all are good writers. Some may have a language barrier, others may lack a particular predilection for writing, or they may not have had prior opportunity to take courses in writing. Whatever the reason, not being an expert writer can be a problem, particularly in academia where grades depend on it. As a result, some students take elaborate risks; they may hire editors or even cheat by paying people to write essays for them.

ChatGPT can level the playing field. With AI, everyone can now produce credible writing (as well as illustrations, code, and in the coming months, even more outputs such as video). While this may pose a problem for some classes, like those heavily focused on teaching the mechanics of writing, for many other classes, ChatGPT can work like a calculator of sorts-helping improve student performance. In these classes, you might set the expectation that you want to see only high-quality written work because AI can help students produce error-free sentences and paragraphs.

However, producing meaningful and insightful content through AI is not necessarily easy; it requires both topic expertise and skill. Show your students this guide for writing with AI; asking them to credit AI and provide the prompts they used to write their essays will teach them how to use the tool properly.

Ultimately, by embracing AI in your classroom, you'll likely be increasing your expectations about how much students should be able to accomplish. In other words, they may save time writing essays using AI, but that time should now be used to gather more data and think more deeply about the material.

Elevating Student Learning with AI

If we are willing to think creatively and try new approaches with it, AI can unleash a whole new way of learning. For example, letting students teach or edit each other's work is a powerful way to take concepts from theory into practice (e.g., peer review). As part of a new course exercise we designed, ChatGPT serves as students' partner to help improve their writing.

In the exercise, the AI produces an essay based on a prompt, and its student partner then critiques it and works to steadily improve the essay by adding new information, clarifying points, fact-checking, and adding insight and analysis. This exercise takes advantage of the AI's propensity to simplify complex topics, using its lack of insightful analysis as a backdrop for students to provide evidence of understanding.

We must train our students to properly use this technology by working interactively with AI models to generate more meaningful and interesting text.

Approaching AI in this way pushes students to think critically about the content and articulate their thoughts for improvement clearly and concisely. Likewise, the exercise can help deepen their understanding of complex topics. Asking students to identify what the AI gets right and wrong can activate deeper thinking, highlighting nuances and implications they hadn't previously considered.

This and similar exercises should help improve students' understanding of major class concepts and illustrate the limits of ChatGPT and other available LLMs.
Find ideas for prompts and assignments you can incorporate into your classroom here.
Embracing AI by Taking an Experimental Approach

AI is here to stay and will only become more prevalent in our classrooms over time. Educators need to think beyond adapting to these tools-we must actively embrace them as a way to improve our teaching.

The best path forward is experimentation. Some experiments will work; others won't. It's also important to remember that while the sudden advent of generative AI may be disruptive to educators, it is even more disruptive to the futures of the students we teach. We need to give them the skills to thrive in a world transformed by AI.

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Why All Our Classes Suddenly Became AI Classes

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Artificial intelligence (AI) panic is spreading across the education sector. The newest wave of generative AI tools, released without guidance or clear suggestions for educators late last year, might be among the biggest and fastest transformations to hit academia.

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The impact of ChatGPT in higher education: A closer look

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The impact of ChatGPT in higher education: A closer look

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https://bilt.online/the-impact-of-chatgpt-in-higher-education-a-closer-look/

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The Impact of ChatGPT in Higher Education: A closer look

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"The traditional college essay is dead," were the words of Stephen Marche[1] about how the traditional/conventional teaching methods will be "disrupted from the ground up" by ChatGPT. As Marche (and many other writers[2] [3]) suggests, ChatGPT has the potential to revolutionise the way education is delivered and assessments are conducted in business schools and other...

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ChatGPT and learning design: what online content creation opportunities does it offer?

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ChatGPT and learning design: what online content creation opportunities does it offer?

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https://www.timeshighereducation.com/campus/chatgpt-and-learning-design-what-online-content-creation-opportunities-does-it-offer

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There has been a lot of debate about how ChatGPT can be used to reimagine the way we teach content and deliver assessment. Most of the focus is about how we can redesign and craft cheat-proof assessments that mostly embrace AI. Higher education is coming to terms with how ChatGPT, and artificial intelligence more broadly, can be embraced by exploiting it as digital aid to improve writing skills and creating assessments that are growth-oriented and incorporate skills-based activities. We've seen unexpected and welcome applications such as the potential to make communication and assessment more inclusive for students with disabilities.

In 2023 we will be launching a 100% online master's course to nine countries on four continents. ChatGPT creates challenges for remote assessment but does it also offer online content creation opportunities?

Using ChatGPT to create online course content: course outlines, writing content, scripting and editing videos

Creating a course outline: Online course development begins by creating an extensive course outline (commonly called a course map or blueprint). Learning designers and academics together sequence topics to divide the course into weekly units that are logical and engaging, and align to the course objectives, assignments and target student cohort.

Creating written content: ChatGPT can be used as a research assistant, generating a first draft of each online course page according to topics in a course map, which can then be sent to an academic. This overcomes the "blank page" problem academics often face and is a form of "creative assistance". Pages can then be reviewed, edited and revised by the academic to ensure accuracy, coherence, and alignment with course objectives and learning outcomes.

Scripting and editing educational videos: ChatGPT can be prompted to write draft scripts for videos and podcasts or generate questions for interviewing subject matter experts, which can then be refined by learning designers. Following on from this, AI can bring efficiencies to the post-production process. For example, when producing videos with segments from multiple interviews, it can be used to reduce the time spent on editing. This can be achieved by feeding AI tools the transcriptions and prompting it to create a cohesive narrative from the sentences within. The AI output from this can be used to guide content creators during the editing process.

Not every learning designer, faculty member or student will want to work with AI and we need to accept this. In his 2019 book Should Robots Replace Teachers? Neil Selwyn advocates that integration of AI into teaching and learning must be presented as choice. We need to be able to present a sound educational justification for the use of AI in education and not just for the sake of change.

3. For those who want to use AI, decide what's in and what's out

ssociate professor and academic director at RMIT Online , Anitra Nottingham is head of course design and Marcelo Zerwes is creative media manager, all at RMIT University.
If you found this interesting and want advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the THE Campus newsletter.

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ChatGPT and learning design: what online content creation opportunities does it offer?

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A guide to how ChatGPT and other AI writers can be used to help learning designers and faculty create course content more efficiently

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Understanding and protecting academic integrity

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Understanding and protecting academic integrity

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https://www.timeshighereducation.com/campus/spotlight/understanding-and-protecting-academic-integrity

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Understanding and protecting academic integrity

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Insights on how universities can tackle academic misconduct among students and faculty alike

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https://www.insidehighered.com/views/2023/02/09/chatgpt-plague-upon-education-opinion#.Y-aoT7UU08c.twitter

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In the winter of 2020, while COVID-19 was just beginning to crawl its way across the globe, there was a palpable sense in the United States that the utter catastrophe that COVID has turned out to be, with over one million dead Americans left in its wake (and counting), simply would not happen. Sure, the front pages of newspapers showed Chinese officials in white hazmat suits carrying body bags, but we'd seen such images before with SARS. Swine flu felt like much ado about nothing, and we're always hearing about some sort of bird flu that never quite takes flight. For many, the endless calamities we had forever read about in the news had never actually reached out of our screens and grabbed us by our throats. But then a couple months later, as if a master switch in the center of the Earth was suddenly flipped, we found ourselves in an entirely new world. Years later, we've never quite been the same.

Today we are facing a new sort of plague, one that threatens our minds more than our bodies. ChatGPT, the artificial intelligence chat bot that can write college-level essays, is going viral. The New York Times reports that the hashtag #chatgpt has surpassed well over half a billion views on TikTok. A lecturer at an Australian university found that a fifth of her students had already used ChatGPT on their exams. Scores of Stanford University students reportedly used it on their fall 2022 final exams mere weeks after its release. A critical mass, a superspreader event, is clearly forming. Yet as in the early days of COVID, most educators have yet to fully grasp the stark reality of the tsunami that is about to hit the educational system. While headlines warning about ChatGPT have populated the news cycle daily for more than a month now, most educators have yet to really feel the brunt of this viral sensation directly.

What winter 2020 was for Covid, winter 2023 is for ChatGPT. These are like the early days, when we thought we could stave off a pandemic through an abundance of hand sanitizer and toilet paper. We realize there is potentially a calamity about to wash upon our shores, but we still have our heads in the sand. We think this won't really affect us, that we can avoid having to make any major changes to the way we've always done things. But soon the first crop of assessments will come back, and educators will begin to notice a change, more so with each passing week. Worried that the first cases of GPT may have popped up in their classroom, they will plug their assignments into one of the new GPT detectors that have emerged online. To their shock and dismay, they will find that their classroom has tested positive for GPT.

In these early days of the GPT spread, we are largely defenseless against this novel threat to human intelligence and academic integrity. A return to handwritten and oral in-class assignments-a lockdown response-may be the only immediate effective solution as we wait for more robust protections to arise. But we are assured they are on the way. As with COVID, breakthrough remedies to protect against GPT are being developed at breakneck speed. Several GPT detectors are already available in beta form: we had COVID zero and now we have GPTZero. Turnitin, the main company employed by colleges to detect plagiarism in online student submissions, plans to have its own GPT detector incorporated into its software soon.

But as with those brief glory days when we received our second dose of Pfizer and Moderna shots, breathing a false sigh of relief that the bad dream was finally over, the efficacy rate of these GPT detectors may soon drop as a stronger mutation of the GPT virus emerges just around the corner: GPT-4 -the Delta of GPT. And so the GPT detectors will be updated to protect against GPT-4, and the alphabet of GPT variants coming, but the efficacy may keep weakening, and the mutations will keep occurring. Unlike with COVID, the new variants of GPT will only keep getting stronger, even exponentially so.

Sooner or later, after GPT washes over the world in wave after wave of new and more powerful forms, we will learn to live with this digital plague just as we are learning to live with that physical virus that has so disrupted our lives. But we are learning to live with COVID because we have gained a certain mastery over it. Learning to live with GPT, on the other hand, might mean something quite different. Rather than gaining mastery over the virus, it may be that it gains mastery over us.

Today our students are largely back from the COVID Zoom nightmare and once again interacting with each other in person. But will we ever come back from GPT? Will we develop a resistance to this new plague in our classrooms, or will we simply throw our hands up in defeat, embrace our AI overlords and with it throw out our ability to write, to create and to think critically for ourselves? Will learning to live with GPT mean submission? Only one thing is for sure: like COVID, GPT is not going away.

So what should we do now that ChatGPT has been let loose upon the student body? Rather than succumb to hopelessness, I offer a set of brief concrete suggestions below:

1. If you haven't done so already, try out ChatGPT for yourself. It's free (though currently fairly bogged down). Plug in your take-home exam questions. Plug in your writing prompts. Work with it like an editor would rather than simply accepting the first response it produces. If you haven't yet done this, you're probably not fully comprehending the power of this AI. You have to see it for yourself to grasp what we are facing. Then you have to realize this is only the very beginning and that AI language models will grow in power exponentially.

2. Universities should immediately designate an AI task force, ideally at the level of each college or possibly even each department. While many universities are starting to discuss how to handle the AI revolution, this cannot be a one-off conversation, but rather needs to be an ongoing dialogue, as the AI landscape will be constantly evolving.

3. Place a stronger focus on the classroom and human interactions. Relationship skills and constructive dialogue skills will likely be more important for the future of humanity than perhaps many of the skills students are learning today that will soon be automated away. I'd personally suggest also banning all technology in your classroom (except for justified exceptions) in order to facilitate a relationship-rich educational environment.

4. Incorporate or re-incorporate in-class assignments, both handwritten and possibly oral assignments.

5. Build internet-disabled computer labs on campus so instructors can incorporate in-class written essays and exams without having to rely on handwritten assignments that are often hard for instructors to read and cumbersome for students to write. These computers could be quite cheap to purchase, since all they will need is word processing, so this need not be a huge expenditure.

6. Develop new courses that are specifically designed for working with GPTs and other AI text generators (e.g., Medical Diagnosis With GPT). Students should have familiarity and even expertise with these programs, since such skills will likely be in demand in the job market. Another option would be to have specific AI writing or editing assignments as a portion of one's class. But simply allowing AI text generators to be a standard student resource in every class or for every assignment will ultimately lead to an outsourcing to machines of the ability to think critically or even possess basic knowledge of the subject matter at hand. This would be a failure of a core function of the university-to produce a knowledgeable society capable of critical thought and reflection.

7. Develop standard university protocols for dealing with algorithmically plagiarized work that is flagged by GPT detectors. While GPT detectors will likely be far from a silver bullet and are trivially easy to evade at the moment, they still will likely provide some level of "protection" against GPT. But unlike current plagiarism detectors used by companies like Turnitin, there will be no hard evidence of cheating, as AI generators develop original work rather than directly copying information from prior sources. Rather, the "evidence" of misconduct will be the detector stating that the student's work is "likely" algorithmically generated. It's rather Kafkaesque to penalize a student for cheating because "the machine says you're guilty with a 98.5 percent probability." Much more will need to be done to fairly impugn a student for AI-related academic misconduct. On that note, university academic misconduct policies should be updated to include the misuse of AI and specify what counts as AI misuse.

Now, where there is crisis, there is opportunity. The crisis of COVID, for example, has led to major societal restructuring and has brought about fairly revolutionary changes to the economy and workforce. Some of these changes are for the better and some for the worse. Similarly, while ChatGPT may give us an opportunity to reflect and make substantial changes to how we approach teaching and assessment, we should not pretend that ChatGPT's arrival is not a major crisis or that any of us as educators can avoid it. Rather, we are at a pivotal moment in the history of education. How we respond could have a resounding impact for decades to come. I recommend we do all we can as educators to cultivate the powers of the human mind in the face of this novel threat to our intelligence.

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ChatGPT is a plague upon education (opinion)

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In the winter of 2020, while COVID-19 was just beginning to crawl its way across the globe, there was a palpable sense in the United States that the utter catastrophe that COVID has turned out to be, with over one million dead Americans left in its wake (and counting), simply would not happen.

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If we are setting assessments that a robot can complete what does that say about our assessments?

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If we are setting assessments that a robot can complete what does that say about our assessments?

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https://blog.nomoremarking.com/if-we-are-setting-assessments-that-a-robot-can-complete-what-does-that-say-about-our-assessments-cbc1871f502

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ChatGPT is capable of producing original and high-quality essays with minimal effort.

What does this mean for educational assessment?

Many people argue that it shows the paucity of our current assessment tasks. If we are setting assessments that a robot can complete, surely that shows the assessments are not good enough or hard enough or just plain 'human' enough and that they therefore need reworking.

Last week, I appeared on Good Morning Britain where Dan Fitzpatrick made exactly this argument, and Susannah Reid agreed with what she called a 'profound' point. (Unfortunately the debate ended there and I did not get the chance to respond).

They are not the only ones. In the Financial Times recently, the columnist Camilla Cavendish suggested the following: 'Rather than banning ChatGPT teachers should ask pupils to give it an assignment and critique its response.

Marc Andreesen has weighed in with a similar argumen t on Twitter. '"ChatGPT plagiarism" is a complete non-issue. If you can't out-write a machine, what are you doing writing?'

And even before ChatGPT existed, people were making this argument in the context of other technologies. Here is the economist Daniel Susskind in his book A World Without Work.
Think of the way that we teach and test mathematics, for instance. Many of the problems we set students in secondary school, if not university, can now be solved by apps like PhotoMath and Socratic: take a photo of the problem, printed or handwritten, with a smartphone, and these apps will scan it, interpret it, and give you an instant answer. It is not a good sign that we still teach and test mathematical material in such a routine way that free off-the-shelf systems like these can handle lots of it with ease.
As well as those examples, I have heard similar sentiments expressed to me by many many people over the last month or so, to the extent that it almost seems as though this is the prevailing opinion. ChatGPT can write essays? We will have to set harder / different essays or assessments then!

As popular as this argument is, I disagree with it. There are three big flaws with the 'we should set assessments that computers can't complete' argument. It's an argument that misunderstands basic principles about (1) technology, (2) education and (3) assessment.
One: it's quite hard to find educational assessments computers can't do
Even before ChatGPT, this was true, as Daniel Susskind's point inadvertently makes clear. Now ChatGPT has come along: as well as writing essays, it can get passing marks in a number of prestigious professional qualifications. The solution of 'getting kids to critique a ChatGPT essay' is not going to work either, as ChatGPT is rather good at critiquing its own responses. I suspect it would also be good at critiquing critiques of its responses, and critiquing critiques of those, and so on ad infinitum.

The basic technological principle here is Moravec's paradox, first developed in 1988, which is that computers find the types of academic skills we teach and assess in schools trivially easy. In his words: "It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility."¹

Hans Moravec suggested a biological basis for his paradox: humans as a species have been evolving visual and spatial skills for millions of years, but abstract thought only for about a hundred thousand or so.

OK, you might say. This is all very interesting, but doesn't it just prove the point that we need to change the way we teach and assess? If computers really are so brilliant at these typical academic skills that are taught in schools, maybe we should stop teaching them completely or only teach the particularly advanced, specialist and niche ones that computers can't do?

No. First of all, we will always want to teach academic skills for personal development. It's good to be able to read, write and count even if a computer is faster and quicker. We didn't stop teaching PE because of the invention of the car, or drawing because of the invention of the camera.

It is true that in order to develop, understand and use advanced technologies, humans are going to need advanced skills, and it is true that these more advanced skills should be one of the ultimate aims of education.

But this does not mean that we can forget about the more fundamental skills, because they are what allow us to develop the more advanced skills. This brings us to the second flaw in the 'set more complex assessments' argument.
Two: if we want students to have advanced skills, they cannot leapfrog fundamental skills
Of course we want our students to develop the higher order skills of being able to critique writing produced by AI chatbots and to direct the outputs of new technologies. But those skills depend on more fundamental skills and there is no way we can jump ahead to the more advanced skills without acquiring the more basic skills first. In order for students to successfully grapple with problems computers do, they must work through problems that computers do. If schools could only teach maths using problems that computers cannot solve, we would have to teach six-year-olds maths using problems even top mathematicians find difficult!

So it doesn't matter if we set our students tasks that can be easily solved by computers. It doesn't matter if they produce writing that is weaker than that of ChatGPT. The easy problems and the weak writing are milestones on their journey to mastery which cannot be skipped or outsourced.

The basic educational principle here is to do with the relationship between working memory and long-term memory. We have limited working memories, so we need to make up for that weakness by storing lots of information in long-term memory. You can't outsource that information to Google or ChatGPT: it needs to be in long-term memory so it can be effortlessly and frictionlessly summoned to working memory when needed and combined with information in the environment, where it will produce what we typically call 'skill'. Here's a quotation from Daniel Willingham, Professor of Psychology at the University of Virginia, which expresses this well.
Data from the last 40 years lead to a conclusion that is not scientifically challengeable: thinking well requires knowing facts, and that's true not simply because you need something to think about. The very processes that teachers care about most - critical thinking processes such as reasoning and problem solving - are intimately intertwined with factual knowledge that is stored in long-term memory (not just found in the environment).
There is an analogy here with chess. Chess computers have been better than the very best humans at chess for decades now. What do we do if a child wants to learn chess? Do we say, well, there is no value in teaching or assessing any content that a chess machine can do? Do we say, we need to set them problems that Alpha Go cannot solve? Of course not! We teach them how the pieces move, what the basic openings are, and some of the common patterns to look out for, even though computers find all these tasks trivially easy. Interestingly, we can and do use technology to help students these basics, but at no point do we assume technology means they never have to learn those basics. The same is true of other skills.
Three: the point of an assessment is not the product but the process.
The value of the work students produce in an assessment is not in the work itself but in understanding it represents and the thinking that went into creating it. Imagine two students write an essay. One struggles hard, reads a lot, writes and redrafts it, and produces something that is OK but not great. Another produces something perfect by pasting the prompt into ChatGPT and copying the output into a Word doc. Who has done better? If all we cared about was the product, then it would be the second student. But we don't. We care about the process. The first student's response has led to them learning more and their essay represents greater understanding of the topic than the second student's essay. Fundamentally, it does not matter that a computer can answer this question better than the student. What matters is what the student has learnt from answering it, and what it tells us about their understanding.

The basic assessment principle here is the difference between the sample and the domain. The sample is the test itself. The domain is the student's wider understanding. The sample only matters if it tells us something valuable about the domain - otherwise it is worthless. This might seem like a fairly straightforward distinction, but even before ChatGPT it was widely misunderstood. Here's what Daniel Koretz, Professor of Educational Assessment at Harvard, has to say about it.
This might be called the sampling principle of testing: test scores reflect a small sample of behaviour and are valuable only insofar as they support conclusions about the larger domains of interest. This is perhaps the most fundamental principle of achievement testing. A failure to grasp this principle is at the root of widespread spread misunderstandings of test scores.
The reaction to ChatGPT bears out Koretz's point about how poorly understood this principle is. I also think this misunderstanding could cause real problems with student motivation. If a student struggles for an hour over an extended piece of writing and then finds that a computer has surpassed it in seconds, it is entirely possible they will feel demotivated. What they need to hear from adults is don't worry, your work is of value, you're on a journey and you are developing your own writing skills. What they don't need to hear is well there's no point in even bothering, the computer is so much better than you. Try this assessment which is even harder instead!

So, if we are setting assessments that a robot can complete, what does that say about our assessments? It doesn't tell us very much at all. Maybe it's a good assessment, maybe it's not. Whether a robot can complete it or not is largely irrelevant when judging its quality.

The one way in which the answer to this question will matter is in terms of the conditions that the assessment should be taken in, something I'll consider in future posts.

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If we are setting assessments that a robot can complete, what does that say about our assessments?

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ChatGPT is capable of producing original and high-quality essays with minimal effort. What does this mean for educational assessment? Many people argue that it shows the paucity of our current assessment tasks. If we are setting assessments that a robot can complete, surely that shows the assessments are not good enough or hard enough or just plain 'human' enough and that they therefore need reworking.

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ChatGPT and Text Generators: Should Academia Adapt or Resist?

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ChatGPT and Text Generators: Should Academia Adapt or Resist?

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https://hbsp.harvard.edu/inspiring-minds/chatgpt-and-ai-text-generators-should-academia-adapt-or-resist?cid=email%7Cmarketo%7C2023-02-09-inspiring-minds-digest-new%7C38306463%7Cinspiring-minds%7Ceducator%7Cinspiring-minds-article%7Cfeb2023&acctID=21409695&mkt_tok=ODU1LUFUWi0yOTQAAAGJ1QeY5YeBEWWGeb1u7xUq1p8TQrJ-K3Ge8-2nju1KjPR9Jau4LcmMVNimMbK5unXt74K7Z5UpyE5abc3V8vGNLAkD6zCHbX1q5FXeTDuFl1w

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Those in the education sector have long expressed divergent opinions about technology innovations and their use in classrooms. The debate may be around something as simple as allowing students to use their laptops or smartphones during class, or it may center around an emerging artificial intelligence (AI) technology like ARISTO, a system that can pass (with a score of 90 percent) an eighth-grade-level science exam.

The COVID-19 pandemic forced even the most reluctant academic institutions to embrace the use of technology; most gradually understood that their mission was to educate new generations on the responsible use of technology rather than excluding it completely from the classroom.

Now, academia faces another technological evolution in the form of artificial intelligence text generators (AITGs). At the beginning of this year, we heard about an AITG called ChatGPT that can generate text by asking it questions (like a chatbot). But users quickly discovered that this AI is not just a chatbot; it can generate articles, summaries, essays, or code in seconds. The education-related use cases abound, especially for students, who can use ChatGPT to do their school assignments and examinations.

If used unethically, this technology could diminish their learning process and objectives, some have argued. While talking about its implications in education, academia took distance and started asking, Do we adapt or resist?

Since the pandemic, we have contributed to the technology debate by suggesting the adoption of a constructive and positive approach to pedagogy, in which it is better to concentrate on what innovations could bring to education rather than resisting change.

Here, we'll offer our own recommendations for approaching AITGs. But first, let's look more generally at some of the advantages and concerns surrounding this AI technology.

Opportunities ChatGPT May Bring to Education

The potential of ChatGPT presents intriguing opportunities for education. As most of us would agree, academia must continue to shift from unidirectional knowledge transfer to an active development of competencies, experience, social abilities, and technological agility. Access to information has democratized knowledge acquisition, and ChatGPT may synthesize the use of various sources, albeit not without a potential bias.

On January 21, the Financial Times published an article commenting on a paper published by a Wharton Business School professor that describes how the author, Christian Terwiesch, tested ChatGPT3 on an exam in a core course of his MBA program. The bot ChatGPT earned a solid grade (between B and B-) and outperformed most of the actual students in the course.

Obviously, these kinds of situations represent a challenge for universities and business schools; but instead of taking ChatGPT as a threat, educators and their institutions should nevertheless analyze its advantages and use them in favor of the student to achieve their educational objectives.

Here are five ways AITGs can be used in a participant-centered learning process.

1. Text generators like ChatGPT can boost our collective familiarity with AI and how to use it, a critical competency for our students and their futures. Industries move quickly, and new technologies push them to continue innovating. We must be the first facilitators of new technologies as they emerge and teach our students how to use them appropriately (technically and ethically). If not us, who?

2. AITGs can assist educators in preparing and reviewing sessions by providing them with additional resources or helping them create engaging educational content that can lead to a better learning experience for students. Educators and students already use search engines, citation and research management apps, spellcheckers, and data collection tools; the addition of tech like ChatGPT has the potential to develop written materials previously validated including scripts, examples, tests questions, or even cases analysis to be discussed in class.

3. AITGs can save educators time by automatically grading students' assignments or doing educators' repetitive work-for example, preparing announcements and instructions for assignments or exams; or providing feedback to students when making "recurrent" or common mistakes in solving their exercises; or preparing basic but customized guidelines for activities, such as how to structure a research thesis or how to solve an exercise. AITGs can also be effectively used to give students automatic feedback on their essays and texts.

4. AITGs can be used for training purposes. For example, students can use ChatGPT to emulate conversations and develop their language skills and abilities through conversational interactions with the chatbot.

5. ChatGPT could be used to improve engagement in online learning by increasing students' motivation in asynchronous sessions or activities. Students will find using an innovative tool to be exciting while discovering its potential use in day-to-day tasks, for example to write emails, send text messages, or even prepare a draft of a contract. It can also send automatic but customized feedback and instructions in online courses to help students stay on track (further increasing their engagement in the course).

Early Concerns About Using ChatGPT in Education

Educators understandably have concerns about the risks of this new technology on teaching and evaluating students. Some example concerns include the following:

Plagiarism or fraudulent use of AITGs in written content reflected in papers, essays, tests, and quizzes; identifying and handling unethical behavior in student content; and promoting ethical research with the use of reliable and valid sources of information.

Bias, limitations, and inaccuracy of AITGs in developing responses on certain topics, which could negatively affect the learning process and objectives for students and perpetuate stereotypes and other misinformation.

Students' overreliance on AITGs, causing them to miss out on important learning opportunities such as critical thinking, problem-solving, proper research techniques, and interaction with educators and peers.

Unfair or inaccurate evaluation of students' work using AITGs due to its inability to estimate creativity and originality, among other soft skills.

Should We Adapt or Resist? Recommendations for Academia

In the face of these abundant opportunities and risks, education institutions can respond to this new technology by either resisting or adapting. The resistors will treat AI as an enemy and ban it-as has happened with some educators prohibiting the use of laptops, mobile phones, and other technologies in the classroom.

But taking an adaptive response provides an opportunity to raise awareness among faculty about the importance of knowing and using technology in education. Further, we can step in to more adequately train our future generations of students to use AI and be better prepared for the challenges they will face in their careers.

"Instead of taking ChatGPT as a threat, educators and their institutions should analyze its advantages and use them in favor of the student to achieve their educational objectives."

Here are several recommendations for educators and academic institutions to adapt to this technology rather than resist it.

Help students develop their critical thinking skills by asking them to compare AI-produced content with reliable, valid sources of information.

Invest energy, resources, and creativity into providing an attractive and engaging learning environment for students-either in physical spaces or virtual settings.

Introduce more oral assignments and in-person exams into your curricula. Many Australian universities, for example, have already announced a return to in-person exams to protect the integrity of student assessments.

Increase the importance of in-class activities and participation as a significant element of the evaluation process and a relevant component of the final grade. In other words, shift the object of evaluation from the "product" to the "process" of student learning.

Adopt new or more ways to get students to develop and showcase their knowledge or learning: For example, through debates-in which students take a formalized position on a specific topic and have to defend their idea, perspective, or point of view. Or by introducing Art-Thinking methodology, which highlights the ways artists create to help students develop specific skills they need to enter the workforce and face ever-growing uncertainty.

Emphasize ethical use of AI and the importance of proper research and citation practices. For example, some universities include (or are reintroducing) mandatory courses in academic ethics, research integrity, and critical thinking for all students. Developing in future generations a sensibility on these topics is crucial for all levels of education.

At the institution level, train educators in the use and identification of AI-produced content despite the challenge it represents as an emergent technology.

While we are advocating for a more adaptive approach with these recommendations, some educators are choosing to take a more resistant approach. Examples of these more resistive reactions to ChatGPT include the following:

Trying to render AITG useless by asking more complex questions in examinations and assignments.

Asking for anecdotal or personal information that AI technology cannot provide-at the expense of learning from public knowledge and existing data on a topic.

Using software that blocks students' access to the rest of their system when working on examinations or assignments.

Fighting fire with fire: There is another AI in development called GPTZero that will specialize in finding AI-generated texts.

ChatGPT Can Complement Learning, Not Replace It

ChatGPT has academia buzzing, and many institutions are feeling compelled to "do something" about it quickly, perhaps even implementing policy changes or abrupt bans that may prove disruptive when a term is already in progress. But we're advocating a more adaptive and inclusive approach.

It is important for educators to consider how to address the use of AITGs in the classroom, ensuring that these tools are being used as a complement to, rather than a supplement for, traditional teaching methods. As facilitators of tools that can help students optimize their learning processes, we need to use and understand these new technologies to ensure students have a better learning experience.

It is still too early to know if this AITG is the next tipping point that will disrupt the human-digital interaction, like Google did some years ago or the introduction of the Gutenberg printing press did centuries ago. However, we do know the adaptation process is already unfolding in a few weeks rather than taking decades, while questions around this type of AI remain-like how it may make human life better, as time invested in producing writing content can potentially be significantly reduced.

Educators will need to think creatively about how to apply changes to their content, as well as the skills we give (and we want to give) our students. We can and should embrace AITGs like ChatGPT as a partner that helps us learn more, work smarter, and faster.

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ChatGPT and AI Text Generators: Should Academia Adapt or Resist?

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Those in the education sector have long expressed divergent opinions about technology innovations and their use in classrooms. The debate may be around something as simple as allowing students to use their laptops or smartphones during class, or it may center around an emerging artificial intelligence (AI) technology like ARISTO, a system that can pass (with a score of 90 percent) an eighth-grade-level science exam.

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https://www.independent.co.uk/news/uk/home-news/student-passes-university-exam-chatgpt-b2278218.html

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ChatGPT is a data privacy nightmare. If you've ever posted online you ought to be concerned.

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ChatGPT is a data privacy nightmare. If you've ever posted online you ought to be concerned.

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https://theconversation.com/chatgpt-is-a-data-privacy-nightmare-if-youve-ever-posted-online-you-ought-to-be-concerned-199283

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ChatGPT has taken the world by storm. Within two months of its release it reached 100 million active users, making it the fastest-growing consumer application ever launched. Users are attracted to the tool's advanced capabilities - and concerned by its potential to cause disruption in various sectors.

A much less discussed implication is the privacy risks ChatGPT poses to each and every one of us. Just yesterday, Google unveiled its own conversational AI called Bard, and others will surely follow. Technology companies working on AI have well and truly entered an arms race.

The problem is it's fuelled by our personal data.

Read more: Everyone's having a field day with ChatGPT - but nobody knows how it actually works

300 billion words. How many are yours?

ChatGPT is underpinned by a large language model that requires massive amounts of data to function and improve. The more data the model is trained on, the better it gets at detecting patterns, anticipating what will come next and generating plausible text.

OpenAI, the company behind ChatGPT, fed the tool some 300 billion words systematically scraped from the internet: books, articles, websites and posts - including personal information obtained without consent.

If you've ever written a blog post or product review, or commented on an article online, there's a good chance this information was consumed by ChatGPT.

So why is that an issue?

The data collection used to train ChatGPT is problematic for several reasons.

First, none of us were asked whether OpenAI could use our data. This is a clear violation of privacy, especially when data are sensitive and can be used to identify us, our family members, or our location.

Even when data are publicly available their use can breach what we call contextual integrity. This is a fundamental principle in legal discussions of privacy. It requires that individuals' information is not revealed outside of the context in which it was originally produced.

Also, OpenAI offers no procedures for individuals to check whether the company stores their personal information, or to request it be deleted. This is a guaranteed right in accordance with the European General Data Protection Regulation ( GDPR) - although it's still under debate whether ChatGPT is compliant with GDPR requirements.

This "right to be forgotten" is particularly important in cases where the information is inaccurate or misleading, which seems to be a regular occurrence with ChatGPT.

Moreover, the scraped data ChatGPT was trained on can be proprietary or copyrighted. For instance, when I prompted it, the tool produced the first few passages from Joseph Heller's book Catch-22 - a copyrighted text.

Finally, OpenAI did not pay for the data it scraped from the internet. The individuals, website owners and companies that produced it were not compensated. This is particularly noteworthy considering OpenAI was recently valued at US$29 billion, more than double its value in 2021.

OpenAI has also just announced ChatGPT Plus, a paid subscription plan that will offer customers ongoing access to the tool, faster response times and priority access to new features. This plan will contribute to expected revenue of $1 billion by 2024.

None of this would have been possible without data - our data - collected and used without our permission.

A flimsy privacy policy

Another privacy risk involves the data provided to ChatGPT in the form of user prompts. When we ask the tool to answer questions or perform tasks, we may inadvertently hand over sensitive information and put it in the public domain.

For instance, an attorney may prompt the tool to review a draft divorce agreement, or a programmer may ask it to check a piece of code. The agreement and code, in addition to the outputted essays, are now part of ChatGPT's database. This means they can be used to further train the tool, and be included in responses to other people's prompts.

Beyond this, OpenAI gathers a broad scope of other user information. According to the company's privacy policy, it collects users' IP address, browser type and settings, and data on users' interactions with the site - including the type of content users engage with, features they use and actions they take.

It also collects information about users' browsing activities over time and across websites. Alarmingly, OpenAI states it may share users' personal information with unspecified third parties, without informing them, to meet their business objectives.

Read more: Everyone's having a field day with ChatGPT - but nobody knows how it actually works

Time to rein it in?

Some experts believe ChatGPT is a tipping point for AI - a realisation of technological development that can revolutionise the way we work, learn, write and even think. Its potential benefits notwithstanding, we must remember OpenAI is a private, for-profit company whose interests and commercial imperatives do not necessarily align with greater societal needs.

The privacy risks that come attached to ChatGPT should sound a warning. And as consumers of a growing number of AI technologies, we should be extremely careful about what information we share with such tools.
The Conversation reached out to OpenAI for comment, but they didn't respond by deadline. Correction: in regards to ChatGPT's potential to generate copyrighted texts, this article previously referenced Peter Carey's novel True History of the Kelly Gang, with a ChatGPT screenshot that was not an actual excerpt from the book. This has been changed to an accurate example referencing Joseph Heller's book Catch-22.

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ChatGPT is a data privacy nightmare. If you've ever posted online, you ought to be concerned

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ChatGPT is fuelled by our intimate online histories. It's trained on 300 billion words, yet users have no way of knowing which of their data it contains.

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Generative AI: how will the new era of machine learning affect you?

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Generative AI: how will the new era of machine learning affect you?

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https://www.ft.com/content/1e34f334-4e73-4677-9713-99f85eed7ba0?xnpe_tifc=4I17hkUNx.xX4.xJ4khL49pJVdUZMds_O.ssxFxdOf4NtIVjOF_A4FbDbNJNOk4stIV7hubjh.E.x.YZhMp.h.B.xDY8OF_jhIzdbIU_&utm_source=exponea&utm_campaign=B2B%20%7C%20One-off%20%7C%20Faculty%20-%20UKI%2C%20CEMEA%2C%20US%20%26%20Canada%20%7C%20080222&utm_medium=email

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Just over 10 years ago, three artificial intelligence researchers achieved a breakthrough that changed the field forever.

The "AlexNet" system, trained on 1.2mn images taken from around the web, recognised objects as different as a container ship and a leopard with far greater accuracy than computers had managed before.

That feat helped developers Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton win an arcane annual competition called ImageNet. It also illustrated the potential of machine learning and touched off a race in the tech world to bring AI into the mainstream.

Since then, computing's AI age has been taking shape largely behind the scenes. Machine learning, an underlying technology that involves computers learning from data, has been widely used in jobs such as identifying credit card fraud and making online content and advertising more relevant. If the robots are starting to take all the jobs, it's been happening largely out of sight.

That is, until now. Another breakthrough in AI has just shaken up the tech world. This time, the machines are operating in plain sight - and they could finally be ready to follow through on the threat to replace millions of jobs.

ChatGPT, a query-answering and text-generating system released at the end of November, has burst into the public consciousness in a way seldom seen outside the realm of science fiction. Created by San Francisco-based research firm OpenAI, it is the most visible of a new wave of so-called "generative" AI systems that can produce content to order.

If you type a query into ChatGPT, it will respond with a short paragraph laying out the answer and some context. Ask it who won the 2020 presidential election, for example, and it lays out the results and tells you when Joe Biden was inaugurated.

Simple to use and able in an instant to come up with results that look like they were produced by a human, ChatGPT promises to thrust AI into everyday life. The news that Microsoft has made a multibillion dollar investment in OpenAI - co-founded by AlexNet creator Sutskever - has all but confirmed the central role the technology will play in the next phase of the AI revolution.

ChatGPT is the latest in a line of increasingly dramatic public demonstrations. Another OpenAI system, automatic writing system GPT-3, electrified the tech world when it was unveiled in the middle of 2020. So-called large language models from other companies followed, before the field branched out last year into image generation with systems such as OpenAI's Dall-E 2, the open-source Stable Diffusion from Stability AI, and Midjourney.

These breakthroughs have touched off a scramble to find new applications for the technology. Alexandr Wang, chief executive of data platform Scale AI, calls it "a Cambrian explosion of use cases", comparing it to the prehistoric moment when modern animal life began to flourish.

If computers can write and create images, is there anything, when trained on the right data, that they couldn't produce? Google has already shown off two experimental systems that can generate video from a simple prompt, as well as one that can answer mathematical problems. Companies such as Stability AI have applied the technique to music.

The technology can also be used to suggest new lines of code, or even whole programs, to software developers. Pharmaceutical companies dream of using it to generate ideas for new drugs in a more targeted way. Biotech company Absci said this month it had designed new antibodies using AI, something it said could cut more than two years from the approximately four it takes to get a drug into clinical trials.

But as the tech industry races to foist this new technology on a global audience, there are potentially far-reaching social effects to consider.

Tell ChatGPT to write an essay on the Battle of Waterloo in the style of a 12-year-old, for example, and you've got a schoolchild's homework delivered on demand. More seriously, the AI has the potential to be deliberately used to generate large volumes of misinformation, and it could automate away a large number of jobs that go far beyond the types of creative work that are most obviously in the line of fire.

"These models are going to change the way that people interact with computers," says Eric Boyd, head of AI platforms at Microsoft. They will "understand your intent in a way that hasn't been possible before and translate that to computer actions". As a result, he adds, this will become a foundational technology, "touching almost everything that's out there".

The reliability problem

Generative AI advocates say the systems can make workers more productive and more creative. A code-generating system from Microsoft's GitHub division is already coming up with 40 per cent of the code produced by software developers who use the system, according to the company.

The output of systems like these can be "mind unblocking" for anyone who needs to come up with new ideas in their work, says James Manyika, a senior vice-president at Google who looks at technology's impact on society. Built into everyday software tools, they could suggest ideas, check work or even produce large volumes of content.

Yet for all its ease of use and potential to disrupt large parts of the tech landscape, generative AI presents profound challenges for the companies building it and trying to apply it in practice, as well as for the many people who are likely to come across it before long in their work or personal lives.

Foremost is the reliability problem. The computers may come up with believable-sounding answers, but it's impossible to completely trust anything they say. They make their best guess based on probabilistic assumptions informed by studying mountains of data, with no real understanding of what they produce.

"They don't have any memory outside of a single conversation, they can't get to know you and they don't have any notion of what words signify in the real world," says Melanie Mitchell, a professor at the Santa Fe Institute. Merely churning out persuasive-sounding answers in response to any prompt, they are brilliant but brainless mimics, with no guarantee that their output is anything more than a digital hallucination.

It's a tool that people can use morally or immorally, legally or illegally, ethically or unethically

There have already been graphic demonstrations of how the technology can produce believable-sounding but untrustworthy results.

Late last year, for instance, Facebook parent Meta showed off a generative system called Galactica that was trained on academic papers. The system was quickly found to be spewing out believable-sounding but fake research on request, leading Facebook to withdraw the system days later.

ChatGPT's creators admit the shortcomings. The system sometimes comes up with "nonsensical" answers because, when it comes to training the AI, "there's currently no source of truth", OpenAI said. Using humans to train it directly, rather than letting it learn by itself - a method known as supervised learning - did not work because the system was often better at finding "the ideal answer" than its human teachers, OpenAI added.

One potential solution is to submit the results of generative systems to a sense check before they are released. Google's experimental LaMDA system, which was announced in 2021, comes up with about 20 different responses to each prompt and then assesses each of these for "safety, toxicity and groundedness", says Manyika. "We make a call to search to see, is this even real?"

Yet any system that relies on humans to validate the output of the AI throws up its own problems, says Percy Liang, an associate professor of computer science at Stanford University. It might teach the AI how to "generate deceptive but believable things that actually fool humans," he says. "The fact that truth is so slippery, and humans are not terribly good at it, is potentially concerning."

According to advocates of the technology, there are practical ways to use it without trying to answer these deeper philosophical questions. Like an internet search engine, which can throw up misinformation as well as useful results, people will work out how to get the most out of the systems, says Oren Etzioni, an adviser and board member at AI2, the AI research institute set up by Microsoft co-founder Paul Allen.

"I think consumers will just learn to use these tools to their benefit. I just hope that doesn't involve kids cheating in school," he says.

But leaving it to the humans to second-guess the machines may not always be the answer. The use of machine-learning systems in professional settings has already shown that people "over-trust the predictions that come out of AI systems and models", says Rebecca Finlay, chief executive of the Partnership on AI, a tech industry group that studies uses of AI.

The problem, she adds, is that people have a tendency to "imbue different aspects of what it means to be human when we interact with these models", meaning that they forget the systems have no real "understanding" of what they are saying.

These issues of trust and reliability open up the potential for misuse by bad actors. For anyone deliberately trying to mislead, the machines could become misinformation factories, capable of producing large volumes of content to flood social media and other channels. Trained on the right examples, they might also imitate the writing style or spoken voice of particular people. "It's going to be extremely easy, cheap and broad-based to create fake content," says Etzioni.

Every single major content provider in the world thought they needed a metaverse strategy: they all need a generative media strategy

This is a problem inherent with AI in general, says Emad Mostaque, head of Stability AI. "It's a tool that people can use morally or immorally, legally or illegally, ethically or unethically," he says. "The bad guys already have advanced artificial intelligence." The only defence, he claims, is to spread the technology as widely as possible and make it open to all.

That is a controversial prescription among AI experts, many of whom argue for limiting access to the underlying technology. Microsoft's Boyd says the company "works with our customers to understand their use cases to make sure that the AI really is a responsible use for that scenario."

He adds that the software company also works to prevent people from "trying to trick the model and doing something that we wouldn't really want to see". Microsoft provides its customers with tools to scan the output of the AI systems for offensive content or particular terms they want to block. It learnt the hard way that chatbots can go rogue: its Tay bot had to be hastily withdrawn in 2016 after spouting racism and other inflammatory responses.

To some extent, technology itself may help to control misuse of the new AI systems. Manyika, for instance, says that Google has developed a language system that can detect with 99 per cent accuracy when speech has been produced synthetically. None of its research models will generate the image of a real person, he adds, limiting the potential for the creation of so-called deep fakes.

Jobs under threat

The rise of generative AI has also touched off the latest round in the long-running debate over the impact of AI and automation on jobs. Will the machines replace workers or, by taking over the routine parts of a job, will they make existing workers more productive and increase their sense of fulfilment?

Most obviously, jobs that involve a substantial element of design or writing are at risk. When Stability Diffusion appeared late last summer, its promise of instant imagery to match any prompt sent a shiver through the commercial art and design worlds.

Some tech companies are already trying to apply the technology to advertising, including Scale AI, which has trained an AI model on advertising images. That could make it possible to produce professional-looking images from products sold by "smaller retailers and brands that are priced out of doing photoshoots for their goods," says Wang.

That potentially threatens the livelihoods of anyone who creates content of any kind. "It revolutionises the entire media industry," says Mostaque. "Every single major content provider in the world thought they needed a metaverse strategy: they all need a generative media strategy."

According to some of the humans at risk of being displaced, there is more at stake than just a pay cheque. Presented with songs written by ChatGPT to sound like his own work, singer and songwriter Nick Cave was aghast. "Songs arise out of suffering, by which I mean they are predicated upon the complex, internal human struggle of creation and, well, as far as I know, algorithms don't feel," he wrote online. "Data doesn't suffer."

Techno-optimists believe the technology could amplify, rather than replace, human creativity. Armed with an AI image generator, a designer could become "more ambitious", says Liang at Stanford. "Instead of creating just single images, you could create whole videos or whole new collections."

The copyright system could end up playing an important role. The companies applying the technology claim that they are free to train their systems on all available data thanks to "fair use", the legal exception in the US that allows limited use of copyrighted material.

Others disagree. In the first legal proceedings to challenge the AI companies' profligate use of copyrighted images to train their systems, Getty Images and three artists last week started actions in the US and UK against Stability AI and other companies.

According to a lawyer who represents two AI companies, everyone in the field has been braced for the inevitable lawsuits that will set the ground rules. The battle over the role of data in training AI could become as important to the tech industry as the patent wars at the dawn of the smartphone era.

Ultimately, it will take the courts to set the terms for the new era of AI - or even legislators, if they decide the technology breaks the old assumptions on which existing copyright law is based.

Until then, as the computers race to suck up more of the world's data, it is open season in the world of generative AI.

Letters in response to this article:
AI in the right hands can end the scourge of fake news Showing bots the error of their ways is the easy bit Emerging technologies usually create new roles / Martin De Saulles, Tunbridge Wells, Kent, UK / From Peter Hirsch, Montclair, NJ, US / From Jem Eskenazi, London N3, UK

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Generative AI: how will the new era of machine learning affect you?

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Just over 10 years ago, three artificial intelligence researchers achieved a breakthrough that changed the field forever. The "AlexNet" system, trained on 1.2mn images taken from around the web, recognised objects as different as a container ship and a leopard with far greater accuracy than computers had managed before.

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An important next step on our AI journey

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An important next step on our AI journey

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An important next step on our AI journey

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ChatGPT can answer questions and carry out requests in text form, based on information from the internet as it was in 2021. It can generate speeches, songs, marketing copy, news articles and student essays.

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Google launches ChatGPT rival called Bard

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ChatGPT can answer questions and carry out requests in text form, based on information from the internet as it was in 2021. It can generate speeches, songs, marketing copy, news articles and student essays.

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On January 6, security research group Check Point Research (CPR) published a terrifying article entitled " OpwnAI: Cybercriminals Starting to Use ChatGPT." Although not surprising, I wasn't expecting it so soon.

CPR had previously studied how malicious hackers, scammers, and cybercriminals could exploit ChatGPT. They demonstrated how the chatbot can "create a full infection flow, from spear-phishing to running a reverse shell" and how it can generate scripts to run dynamically, adapting to the environment.

Despite OpenAI's guardrails, which appeared as an orange warning notification when CPR forced ChatGPT to do something against the usage policy, the research group had no problem generating a simple phishing email. "Complicated attack processes can also be automated as well, using the LLMs APIs to generate other malicious artifacts," they concluded.

CPR researchers weren't satisfied with proof that ChatGPT could do this hypothetically (one of the common criticisms skeptics receive is that the potential risks they warn about never materialize into real-world harm). They wanted to find real instances of people misusing it in similar ways. And they found it.

CPR analyzed "several major underground hacker communities" and found at least three concrete examples of cyber criminals using ChatGPT in ways that not only violate the ToS but that could become harmful in a direct and measurable way.

First, an info stealer. In a thread entitled "ChatGPT - Benefits of Malware," a user shared experiments where he "recreated many malware strains." As CPR noted, the OP's other posts revealed that "this individual [aims] to show less technically capable cybercriminals how to utilize ChatGPT for malicious purposes."

"Cybercriminal showing how he created infostealer using ChatGPT." Credit: CPR (with permission)

Second, an encryption tool. A user by the name "USDoD" published a Python script with "encryption and decryption functions." CPR concluded that the "script can easily be modified to encrypt someone's machine completely without any user interaction." While USDoD has "limited technical skills," he is "engaged in a variety of illicit activities."

"Cybercriminal dubbed USDoD posts multi-layer encryption tool." Credit: CPR (with permission)

The last example is fraud activity. The title of the post is quite telling: "Abusing ChatGPT to create Dark Web Marketplaces scripts." CPR writes: "The cybercriminals published a piece of code that uses third-party API to get up-to-date cryptocurrency ... prices as part of the Dark Web market payment system."

"Threat actor using ChatGPT to create DarkWeb Market scripts." Credit: CPR (with permission)

It's clear that ChatGPT being free to use and highly intuitive is an attractor for cybercriminals, including those with low technical skills. As Sergey Shykevich, Threat Intelligence Group Manager at Check Point explains:

"Just as ChatGPT can be used for good to assist developers in writing code, it can also be used for malicious purposes. Although the tools that we analyze in this report are pretty basic, it's only a matter of time until more sophisticated threat actors enhance the way they use AI-based tools."

ChatGPT being a driver of security issues online isn't a hypothesis exacerbated by fearmongerers but a reality that's hard to deny. For those who use the argument that this was possible before ChatGPT, two things: First, ChatGPT can bridge the technical gap. Second, scale matters a lot here - ChatGPT can automatically write a script in seconds.

Cybersecurity, disinformation, plagiarism... Many people have repeatedly warned about the problems ChatGPT-like AIs can cause. Now malicious users start to abound.

Someone could still try to make the case in favor of ChatGPT. Maybe it's not that problematic - the upsides can compensate for the downsides - but maybe it is. And a "maybe" should suffice for us to think twice. OpenAI lowered its guard when GPT-2 turned out to be "harmless" (they saw " no strong evidence of misuse so far "), and they never raised it back again.

I agree with Scott Alexander that " perhaps it is a bad thing that the world's leading AI companies cannot control their AIs." Perhaps reinforcement learning through human feedback isn't good enough. Perhaps companies should find better ways to exert control over their models if they're going to unleash them in the wild. Perhaps GPT-2 wasn't so dangerous but a couple of iterations later we've got something to worry about. And if not, we'll have it in a couple more.

I'm not saying OpenAI hasn't tried - they have (they've even been criticized for being too conservative). What I'm arguing is that, if we perpetuate this mindset of "I've tried to make it right so I now have the green light to release my AI" into the short-term future, we'll encounter more and more downsides that no upside would make up for.

One question has been bothering me for a few weeks: If OpenAI is so worried about doing things right, why didn't they set up the watermarking scheme to identify ChatGPT's outputs before releasing the model to the public? Scott Aaronson is still trying to make it work - a month after the model went completely viral.

I don't think a watermark would've solved the fundamental problems this technology entails, but it'd have helped by giving time. Time for people to adapt, for scientists to find solutions to the most pressing issues, and for regulators to come up with relevant legislation.

Due to OpenAI's inaction, we're left with shy attempts at building GPT detectors that could provide people with a means to avoid AI disinformation, scams, or phishing attacks. Some have tried to repurpose a 3-year-old GPT-2 detector for ChatGPT but it doesn't work. Others, like Edward Tian, a CS and journalism senior at Princeton University, have developed systems from the ground-up, specifically targeted to ChatGPT.

As of now, 10,000+ people have tested GPTZero, me included ( here's the demo. Tian is building a product for which 3K+ teachers have already subscribed). I confess that I've managed to fool it just once (and only because ChatGPT misspelled a word) but haven't tried too hard either.

The detector is quite simple; it evaluates the "perplexity" and "burstiness" of a chunk of text. Perplexity measures how much a sentence "surprises" the detector (i.e. to what degree the distribution of output words doesn't match what it's expected from a language model) and burstiness measures the constancy of perplexity across sentences. Simply put, GPTZero leverages the fact that humans tend to write much more weirdly than AIs - which becomes apparent as soon as you read a page of AI-generated text. It's so dull...

At a <2% false positive rate, GPTZero is the best detector out there. Tian is proud: "Humans deserve to know when the writing isn't human," he told the Daily Beast. I agree - even if ChatGPT doesn't plagiarize, it's morally wrong for people to claim they're authors of something ChatGPT wrote.

But I know it isn't infallible. A few changes to the output (e.g. misspelling a word or interleaving your own) may be enough to trick the system. Asking ChatGPT to avoid repeating words works just fine, as Yennie Jun shows here. And finally, GPTZero may become obsolete soon because new language models appear every few weeks - AnthropicAI has unofficially announced Claude which, as evidenced by Riley Goodside's analyses, is better than ChatGPT.

And GPT-4 is around the corner.

This is a cat-and-mouse game, as some people like to call it - and the mouse is always one step ahead.

If detectors worked just fine, many people would get angry. Most want to use ChatGPT without barriers. Students, for instance, wouldn't be able to cheat in written essays because an AI-savvy professor may be aware of the existence of a detector ( it has already happened). The fact that 3K+ teachers have signed up for Tian's incoming product says it all.

But, because detectors aren't sufficiently reliable, those who don't want to face the uncertainty of having to guess whether some written deliverable is or isn't ChatGPT's product have taken the most conservative solution: Banning ChatGPT.

The Guardian reported on Friday that " New York City schools have banned ChatGPT." Jenna Lyle, a department spokesperson, cites "concerns about negative impacts on student learning, and concerns regarding the safety and accuracy of contents" as the reasons for the decision. Although I understand the teachers' point of view, I don't think this is a wise approach - it may be the easier choice, but it isn't the right one.

Stability.ai's David Ha tweeted this when the news came out:

I acknowledge ( and have done it before) the problems schools face (e.g. widespread undetectable plagiarism) but I have to agree with Ha.

Here's the dilemma: This technology isn't going away. It's a part of the future - a big part, probably - and it's super important that students (and you, me, and everyone else) learn about it. Banning ChatGPT from schools isn't a solution. As Ha's Tweet implies, it could be more harmful to ban it than to allow it.

Yet, students who use it to cheat on exams or to write essays would waste their teachers' time and effort as well as hinder their development without realizing it. As Lyle says, ChatGPT may prevent students from learning "critical-thinking and problem-solving skills."

What's the solution that I (and many others) foresee? The education system will have to adapt. Although harder, this is the better solution. Given how broken the schooling system is, it may very well be a win-win situation for students and teachers. Of course, it goes without saying that until that happens it's better that teachers have access to a reliable detector - but let's not use that as an excuse to avoid adapting education to these changing times.

The education system has a lot of room for improvement. If it hasn't changed in so many years it's because there weren't strong-enough incentives to do so. ChatGPT gives us a reason to reimagine education, the only piece that's missing in this puzzle is the willingness of those who decide.

It really feels like it. Some have compared AI to fire or electricity but those inventions integrated slowly into society and are too far back in time. We don't know how that felt. AI is more like the internet, it's going to transform the world. Very fast.

I've tried to capture in this essay a future that's already more of a present than a future. One thing is that AIs like GPT-3 or DALL-E exist, and a very different thing is that everyone in the world is aware of them. Those hypotheticals (e.g. disinformation, cyber hacking, plagiarism, etc.) are no longer. It's happening here and now and we are going to see more desperate measures to stop them (e.g. building scrappy detectors or banning AI).

We have to assume some things will change forever. But, in some cases, we may have to defend our position (like artists are doing with text-to-image or minorities have done before with classification systems). Regardless of who you are, AI will get to you in one way or the other. You better be ready.

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ChatGPT and the Future (Present) We're Facing

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On January 6, security research group Check Point Research (CPR) published a terrifying article entitled " OpwnAI: Cybercriminals Starting to Use ChatGPT." Although not surprising, I wasn't expecting it so soon. CPR had previously studied how malicious hackers, scammers, and cybercriminals could exploit ChatGPT.

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How has Higher Ed slumbered into an Artificial Intelligence crisis?

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How has Higher Ed slumbered into an Artificial Intelligence crisis?

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https://leefallin.co.uk/2023/02/how-has-higher-ed-slumbered-into-an-artificial-intelligence-crisis/

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It's an honest question. Everywhere I look, there are discussion threads, social media posts and emails from Higher Education professionals obsessing over artificial intelligence. Most of these communications focus on ChatGPT, but some acknowledge other tools exist. These 'new developments' in artificial intelligence have prompted a dramatic response from the education sector. It has been described as a crisis, a moral panic, an ' end to homework' and a threat to higher education. I think my favourite contribution this year comes from The Mail, which announces artificial intelligence could make 'mankind extinct'.

Okay. Some perspective is needed. But my real question is this: How did the Higher Education (HE) sector not see this coming?

Artificial Intelligence has powered your work for years

In the UK, the vast majority of Universities use Microsoft (Office) 365 and the Windows operating system. Microsoft's Outlook powers our emails, SharePoint/OneDrive stores our files, Teams manages our collaboration, and Office keeps us productive. Since 2016, Microsoft's 'Office Intelligent Services ' have seamlessly integrated artificial intelligence-powered features into our everyday working lives. For most HE practitioners, the developments in artificial intelligence have been staring us in the face. Literally. The documents we write, the slides we develop, the emails we read, and the Teams calls we make have all been enhanced by Artificial Intelligence for YEARS.

Artificial intelligence in Microsoft (Office) 365:

Read aloud has turned text-to-speech, enhanced to use tone and inflexion.

Dictate has enabled speech-to-text, allowing people to talk instead of type. This includes

Optical Character Recognition has helped turn image-based text into readable characters.

Presenter Coach has analyzed people's speech, language and body language to deliver real-time presentation feedback in PowerPoint.

Slide Designer has taken draft slides and automatically added design elements and images to make slides more effective.

Accessibility Checker has allowed the automatic generation of ALT text for images, using computer vision.

Microsoft Viva has provided detailed insights: reading your emails to identify unfinished tasks and checking your calendar to provide useful documents for meetings - in real-time.

Subtitles and Transcription have enabled PowerPoint and Teams to provide real-time subtitles for presentations, calls and recordings.

Excel has offered enhanced chart types (i.e. Maps) and real-time, streamed data (i.e. Stocks).

Editor has offered enhanced spelling and grammar advice, and has extended to use text prediction to save time when writing.

Translate has offered real-time translation from text, images and speech across up to 100 languages (and variants).

Scheduler has coordinated meetings between people - and even booked rooms.

Natural language queries in Excel have allowed people to use questions, not formulas.

Search enhanced with AI when using Bing.com

The examples above are just workplace, education and consumer applications. In industry, Microsoft-powered AI has been detecting facing, monitoring crops, enhancing video games, fighting fraud and detecting faults across hundreds of sectors. I can understand people not being aware of some of these applications - but the stuff listed above has been right in front of our eyes.

It was some relief to see ' This shouldn't be a surprise ' published while I write this post!

How can any of these artificial intelligence developments be a surprise?

So. Reflecting on the list above, 'Intelligent Services' have supported reading and writing across the Microsoft (Office) 365 platform for over eight years(!!!). If you've been using Microsoft Office productivity applications like Outlook, Word and PowerPoint - I cannot understand how ChatGPT can be a surprise. Office applications have started:

1. correcting your writing and predicting what you will say

2. reading your emails to manage your diary and tasks,

3. listening to you, so you don't need to write,

4. automatically making things accessible with subtitling and computer vision

The list goes on. If artificial intelligence has been doing all this for years - how is ChatGPT such a leap?

I can understand how ChatGPT feels like a significant step up from previous chatbots. But I don't see how it can be all that surprising when we reflect on those daily developments and how artificial intelligence has slowly become part of the everyday. It isn't just at work or in education. Your last test at the hospital might have been screened by artificial intelligence. Every time you make a purchase, the transactions are scrutinised by artificial intelligence for anomalies. It really is everywhere. I get how the quality of written response is shocking - but given what we've seen happen in Microsoft (Office) 365 over the last few years - I don't think we can call it a surprise.

Why the last-minute response?

I honestly do not know the answer to this question. The radical potential of artificial intelligence has been staring everyone in the face for years. Every email. Every document. Every Teams call. Every PowerPoint. Artificial intelligence has been prompting, pushing, helping and enhancing for years. How can ChatGPT be such a surprise? I am absolutely shocked that schools, colleges, and universities are so late in reacting to the challenge artificial intelligence poses to traditional assessment. This should not be a surprise. Not at all. I cannot understand how future scanning and business planning did not identify this as part of long-term strategies. Emergency planning and task groups should not be necessary! Educational policies should have been prepared years ago.

But they weren't.

As such, the kneejerk reaction has been to ban artificial intelligence in many educational establishments. Given the circumstances - this is probably the right answer. For now. In future, I urge educational leaders to reconsider artificial intelligence and look at how it can be used alongside the curriculum.

Artificial intelligence in the curriculum

Our students will be leaving the educational world to join workplaces that use artificial intelligence. It might not even be labelled as such - but it will be there in the background. The example of Microsoft (Office) 365 in this very post is a great example of this in application. As such, we need to equip our students for this. Not just the practicalities of using artificial intelligence - but the difficult questions around ethics and responsibility of use too. These skills cannot be developed under a ban.

Artificial intelligence reproduces the biases of society. Our students must be equipped to challenge this.

Artificial intelligence uses Machine Learning - but this is often targeted at a range of information- including high-quality, questionable and poor-quality sources. Students need to be able to identify fact from fiction.

Artificial intelligence supports accessibility, it can translate, see and write for anyone who needs support with it. While not perfect, those features cannot be turned off.

It's fair to say - the news isn't all doom and gloom. It's been nice to see some new articles discussing how artificial intelligence won't hinder creativity, will help schools teach skills for university and how it can be integrated into teaching. It is also interesting to see reflections on the OpenAI website (ChatGPT's creator) about how it can support streamlined and personalised teaching - amongst many opportunities and threats.

The last word - The future of Higher Education with ChatGPT

I couldn't help but close this post by handing over the conclusion to ChatGPT. I asked it to tell me the best-case scenario for the inclusion of artificial intelligence in university education.

The best-case scenario

In the best-case scenario, artificial intelligence (AI) will have a positive impact on university education. AI will help to improve the quality and efficiency of education by providing students with personalized learning experiences. AI-driven tutoring systems will be able to analyze a student's performance and give immediate feedback and recommendations for improvement. AI can also be used to create adaptive learning systems that adjust the difficulty of tasks based on a student's progress, allowing students to move at their own pace and make the most of their learning experience.

AI can also be used to automate administrative tasks, such as grading, making it easier for professors to focus on teaching and providing students with the best possible learning experience. AI-enabled analytics can be used to identify students at risk of dropping out, so appropriate interventions can be put in place to increase their chances of success. Finally, AI can be used to offer more engaging learning experiences, such as interactive simulations and augmented reality. All of this will enable universities to provide a higher quality of education while reducing costs and increasing student satisfaction.
(ChatGPT, 2023 - Published by OpenAI)

...and the worst-case scenario?

In the worst-case scenario, artificial intelligence (AI) could have a detrimental impact on university education. AI-driven tutoring systems could be overly rigid, unable to adapt to individual students' needs, and lead to a one-size-fits-all approach to learning. AI-enabled analytics could be used to track student progress and create an environment of surveillance and control, where students feel that their every move is being monitored and judged. In addition, AI-driven automation of administrative tasks could lead to job losses, as universities reduce their staff numbers in favor of automated systems. This could lead to a decrease in the quality of education, as universities focus on cutting costs rather than on providing students with the best possible learning experience. Finally, AI-enabled simulations and augmented reality could lead to a decrease in critical thinking and problem-solving skills, as students become reliant on pre-programmed systems. All of this could lead to a decrease in student satisfaction and an overall decline in the quality of university education.
(ChatGPT, 2023 - Published by OpenAI)

Okay - I had a bit of fun with the conclusion. But ChatGPT makes a valid point... there is a lot of potential disruption - good or bad - coming to HE. The question still remains:

How did most HE practitioners not see this coming?

Answers in the comments 😅

Also see: Using AI to summerise emails: A use case for text-davinci-003

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How has Higher Ed slumbered into an Artificial Intelligence Crisis?

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It's an honest question. Everywhere I look, there are discussion threads, social media posts and emails from Higher Education professionals obsessing over artificial intelligence. Most of these communications focus on ChatGPT, but some acknowledge other tools exist. These 'new developments' in artificial intelligence have prompted a dramatic response from the education sector.

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Contribute your ideas to a collection of 101 creative ideas to use AI in education

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Contribute your ideas to a collection of 101 creative ideas to use AI in education

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https://creativehecommunity.wordpress.com/2023/02/02/creating-a-collection-of-101-creative-ideas-to-use-ai-in-education/

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https://app.steve.ai/video/UKJJ01RP5Q6A5574

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ChatGPT is making universities rethink plagiarism

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ChatGPT is making universities rethink plagiarism

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https://www-wired-com.cdn.ampproject.org/c/s/www.wired.com/story/chatgpt-college-university-plagiarism/amp

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In late December of his sophomore year, Rutgers University student Kai Cobbs came to a conclusion he never thought possible: Artificial intelligence might just be dumber than humans.

After listening to his peers rave about the generative AI tool ChatGPT, Cobbs decided to toy around with the chatbot while writing an essay on the history of capitalism. Best known for its ability to generate long-form written content in response to user input prompts, Cobbs expected the tool to produce a nuanced and thoughtful response to his specific research directions. Instead, his screen produced a generic, poorly written paper he'd never dare to claim as his own.

"The quality of writing was appalling. The phrasing was awkward and it lacked complexity," Cobbs says. "I just logically can't imagine a student using writing that was generated through ChatGPT for a paper or anything when the content is just plain bad."

Not everyone shares Cobbs' disdain. Ever since OpenAI launched the chatbot in November, educators have been struggling with how to handle a new wave of student work produced with the help of artificial intelligence. While some public school systems, like New York City's, have banned the use of ChatGPT on school devices and networks to curb cheating, universities have been reluctant to follow suit. In higher education, the introduction of generative AI has raised thorny questions about the definition of plagiarism and academic integrity on campuses where new digital research tools come into play all the time.

Make no mistake, the birth of ChatGPT does not mark the emergence of concerns relating to the improper use of the internet in academia. When Wikipedia launched in 2001, universities nationwide were scrambling to decipher their own research philosophies and understandings of honest academic work, expanding policy boundaries to match pace with technological innovation. Now, the stakes are a little more complex, as schools figure out how to treat bot-produced work rather than weird attributional logistics. The world of higher education is playing a familiar game of catch-up, adjusting their rules, expectations, and perceptions as other professions adjust, too. The only difference now is that the internet can think for itself.

According to ChatGPT, the definition of plagiarism is the act of using someone else's work or ideas without giving proper credit to the original author. But when the work is generated by some thing rather than some one, this definition is tricky to apply. As Emily Hipchen, a board member of Brown University's Academic Code Committee, puts it, the use of generative AI by students leads to a critical point of contention. "If [plagiarism] is stealing from a person," she says, "then I don't know that we have a person who is being stolen from."

Hipchen is not alone in her speculation. Alison Daily, chair of the Academic Integrity Program at Villanova University, is also grappling with the idea of classifying an algorithm as a person, specifically if the algorithm involves text generation.

Daily believes that eventually professors and students are going to need to understand that digital tools that generate text, rather than just collect facts, are going to need to fall under the umbrella of things that can be plagiarized from.

Although Daily acknowledges that this technological growth incites new concerns in the world of academia, she doesn't find it to be a realm entirely unexplored. "I think we've been in a version of this territory for a while already," Daily says. "Students who commit plagiarism often borrow material from a 'somewhere'-a website, for example, that doesn't have clear authorial attribution. I suspect the definition of plagiarism will expand to include things that produce."

Eventually, Daily believes, a student who uses text from ChatGPT will be seen as no different than one that copies and pastes chunks of text from Wikipedia without attribution.

Students' views on ChatGPT are another issue entirely. There are those, like Cobbs, who can't imagine putting their name on anything bot-generated, but there are others who see it as just another tool, like spellcheck or even a calculator. For Brown University sophomore Jacob Gelman, ChatGPT exists merely as a convenient research assistant and nothing more.

"Calling the use of ChatGPT to pull reliable sources from the internet 'cheating' is absurd. It's like saying using the internet to conduct research is unethical," Gelman says. "To me, ChatGPT is the research equivalent of [typing assistant] Grammarly. I use it out of practicality and that's really all." Cobbs expressed similar sentiment, comparing the AI bot to "an online encyclopedia."

But while students like Gelman use the bot to speed up research, others take advantage of the high-capacity prompt input feature to generate completed works for submission. It might seem obvious what qualifies as cheating here, but different schools across the country offer contrasting takes.

According to Carlee Warfield, chair of Bryn Mawr College's Student Honor Board, the school considers any use of these AI platforms as plagiarism. The tool's popularization just calls for greater focus in evaluating the intent behind students' violations. Warfield explains that students who turn in essays entirely produced by AI are categorically different from those who borrow from online tools without knowledge of standard citations. Because the ChatGPT phenomenon is still new, students' confusion surrounding the ethics is understandable. And it's unclear what policies will remain in place once the dust settles-at any school.

In the midst of fundamental change in both the academic and technological spheres, universities are forced to reconsider their definitions of academic integrity to reasonably reflect the circumstances of society. The only problem is, society shows no stagnance.

"Villanova's current academic integrity code will be updated to include language that prohibits the use of these tools to generate text that then students represent as text they generated independently," Daily explained. "But I think it's an evolving thing. And what it can do and what we will then need in order to keep an eye on will also be kind of a moving target."

In addition to increasingly complex questions about whether ChatGPT is a research tool or a plagiarism engine, there's also the possibility that it can be used for learning. In other educational settings, teachers see it as a way to show students the shortcomings of AI. Some instructors are already modifying how they teach by giving students assignments bots couldn't complete, like those that require personal details or anecdotes. There's also the matter of detecting AI use in students' work, which is a burgeoning cottage industry all its own.

Ultimately, Daily says, schools may need rules that reflect a range of variables.

"My guess is that there will be the development of some broad blanket policies that essentially say, unless you have permission from a professor to use AI tools, using them will be considered a violation of the academic integrity code," Daily says. "That then gives faculty broad latitude to use it in their teaching or in their assignments, as long as they are stipulating explicitly that they are allowing it."

As for ChatGTP, the program agrees. "Advances in fields such as artificial intelligence are expected to drive significant innovation in the coming years," it says, when asked how schools can combat academic dishonesty. "Schools should constantly review and update their academic honor codes as technology evolves to ensure they are addressing the current ways in which technology is being used in academic settings."

But, a bot would say that.

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ChatGPT Is Making Universities Rethink Plagiarism

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In late December of his sophomore year, Rutgers University student Kai Cobbs came to a conclusion he never thought possible: Artificial intelligence might just be dumber than humans. After listening to his peers rave about the generative AI tool ChatGPT, Cobbs decided to toy around with the chatbot while writing an essay on the history of capitalism.

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ChatGPT sparks debate on how to design student assessments now

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ChatGPT sparks debate on how to design student assessments now

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https://www.insidehighered.com/news/2023/01/31/chatgpt-sparks-debate-how-design-student-assignments-now

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Is an ice cream sandwich a sandwich? How about a sushi roll, chicken wrap or sloppy joe? These were some of the prompts included in a classification and model-building assignment in the fall 2022 Knowledge-Based AI course that David Joyner taught at the Georgia Institute of Technology.

But when Joyner, executive director of online education and the online master of science in computer science and senior research associate, was scheduled to teach the course again in the spring 2023 semester, he reconsidered the assignment in the presence of ChatGPT -the OpenAI chat bot that burst onto the global stage in late 2022 and sent shock waves across academe. The bot interacts with users in a conversational way, including by answering questions, admitting its mistakes, challenging falsehoods and rejecting inappropriate requests.

"I'd used the questions for five years because they were fun questions," Joyner said. "But ChatGPT's answer was so precise that I'm pretty sure it was learning from my own best students," whom he suspected had posted their work online. Joyner replaced several of the sandwich options with avocado toast, shawarma, pigs in a blanket, Klondike bar and Monte Cristo. He also updated the academic misconduct statement on his syllabus to "basically say that copying from ChatGPT isn't different from copying from other people." Such efforts, Joyner acknowledges, may be a temporary fix.

As faculty members ponder academe's new ChatGPT-infused reality, may are scrambling to redesign assignments. Some seek to craft assignments that guide students in surpassing what AI can do. Others see that as a fool's errand-one that lends too much agency to the software.

Either way, in creating assignments now, many seek to exploit ChatGPT's weaknesses. But answers to questions concerning how to design and scale assessments, as well as how to help students learn to mitigate the tool's inherent risks are, at best, works in progress.

"I was all ready to not stress about the open AI shit in terms of student papers, because my assignments are always hyper specific to our readings and require the integration of news articles to defend claims etc. ... BUT THEN I TRIED IT ..." Danna Goldthwaite Young, professor of communication at the University of Delaware, wrote this week in introducing a thread on Twitter.

Students Should Surpass AI-or Not

When Boris Steipe, associate professor of molecular genetics at the University of Toronto, first asked ChatGPT questions from his bioinformatics course, it produced detailed, high-level answers that he deemed as good as his own. He still encourages his students to use the chat bot. But he also created The Sentient Syllabus Project, an initiative driven by three principles: AI should not be able to pass a course, AI contributions must be attributed and true, and the use of AI should be open and documented.

"When I say AI cannot pass the course, it means we have to surpass the AI," Steipe said. "But we also must realize that we cannot do that without the AI. We surpass the AI by standing on its shoulders."

Steipe, for example, encourages students to engage in a Socratic debate with ChatGPT as a way of thinking through a question and articulating an argument.

"You will get the plain vanilla answer-what everybody thinks-from ChatGPT," Steipe said, adding that the tool is a knowledgeable, infinitely patient and nonjudgmental debate partner. "That's where you need to start to think. That's where you need to ask, 'How is it possibly incomplete?'"

But not every faculty member is convinced that students should begin with ChatGPT's outputs.

"Even when the outputs are decent, they're shortcutting the students' process of thinking through the issue," said Anna Mills, English instructor at the College of Marin. "They might be taking the student in a different direction than they would have gone if they were following the germ of their own thought."

Some faculty members also challenge the suggestion that students should compete with AI, as such framing appears to assign the software agency or intelligence.

"I do not see value in framing AI as anything other than a tool," Marc Watkins, lecturer in composition and rhetoric at the University of Mississippi, wrote in an email. Watkins, his department colleagues and his students are experimenting with ChatGPT to better understand its limitations and benefits. "Our students are not John Henry, and AI is not a steam-powered drilling machine that will replace them. We don't need to exhaust ourselves trying to surpass technology."

Still, others question the suggestion that AI-proofing a course is difficult.

"Creating a course that AI cannot pass? Shouldn't take very long at all," Robert Cummings, associate professor of writing and rhetoric at the University of Mississippi, wrote in an email. "Most AI writing generators are, at this stage, laughably inaccurate ... Testing AI interactions with components of a course might make more sense."

But Steipe is pondering a possible future in which descendants of today's AI-writing tools raise existential questions.

"This is not just about upholding academic quality," Steipe said. "This is channeling our survival instincts. If we can't do that, we are losing our justification for a contribution to society. That's the level we have to achieve."

How Faculty Can Exploit ChatGPT's (Current) Weaknesses

In the future, faculty members may get formal advice about how to craft assignments in a ChatGPT world, according to James Hendler, director of the Future of Computing Institute and professor of computer, web and cognitive sciences at Rensselaer Polytechnic Institute.

In the meantime, faculty are innovating on their own.

In computer science, for example, many professors have observed that AI writing tools can write codes that work, though not necessarily of the kind that humans find easy to edit, Hendler said. That observation can be exploited to create assignments that distinguish between content and creative content.

"We try to teach our students how to write code that other people will understand, with comments, mnemonic variable names and breaking code up into meaningful pieces," Hendler said. "That's not what's happening with these systems yet."

Also, since ChatGPT's ability to craft logical arguments can underwhelm, assignments that require critical thinking can work well in the presence of ChatGPT.

"It's not very good at introspecting," Steipe said. "It just generates. You often find non sequiturs or arguments that don't hold water. When you point it out to the to the AI, it says, 'Oh, I got something wrong. I apologize for the confusion.'"

Several faculty members contacted for this article mentioned that lessons learned from the earlier emergence of Wikipedia hint at a path forward. That is, both the online encyclopedia and OpenAI's chat bot offer coherent prose that is prone to errors. They adapted assignments to mix use of the tech tools with fact-checking.

Moving forward, professors can expect students to use ChatGPT to produce first drafts that warrant review for accuracy, voice, audience and integration to the purpose of the writing project, Cummings wrote. As the tools improve, students will need to develop more nuanced skills in these areas, he added.

An Unsolved Problem

Big tech plans to mainstream AI writing tools in its products. For example, Microsoft, which recently invested in ChatGPT, will integrate the tool into its popular office software and sell access to the tool to other businesses. That has applied pressure to Google and Meta to speed up their AI-approval processes.

"My classes now require AI, and if I didn't require AI use, it wouldn't matter, everyone is using AI anyway," Ethan Mollick, associate professor of management and academic director of the Wharton Interactive at the University of Pennsylvania, wrote on his blog that translates academic research into useful insights.

But big tech's speed in delivering AI products to market has not always been accomplished with care. Social media platforms, for example, were once naïvely celebrated for bringing together those with shared interests, not realizing at the time that the platforms also brought together supporters of terror, extremism and hate.

Meta's release of a ChatGPT-like chat bot several months before OpenAI's product received a tepid response, which Meta's chief artificial intelligence scientist, Yan LeCun, blamed on Meta being " overly careful about content moderation," according to The Washington Post.(LeCun spoke with Inside Higher Ed about challenges in computer science in September.) Faculty members may need to help students learn to mitigate and address inherent, real-world harm new tech tools may pose.

"The gloves are off," Steipe said of the huge monetary driver of the emergence of sophisticated chat bots. In higher education, this may mean that the ways in which professors assess students may change. "We've heavily been basing assessment on proxy measures, and that may no longer work."

Professors may assess their students directly, but that level of personal interaction generally does not scale. Still, some are encouraged to find themselves on the same side, so to speak, as their students.

"Our students want to learn and are not in a rush to cede their voices to an algorithm," Watkins wrote.

Such alignment, when present, may offer comfort to the heady disruption academics have experienced since ChatGPT's release, especially as bigger questions-beyond how to assign grades-loom.

"The difference between the AI and the human mind is sentience," Steipe said. "If we want to teach as an academy in the future that is going to be dominated by digital 'thought,' we have to understand the added value of sentience-not just what sentience is and what it does, but how we justify that it is important and important in the way that we're going to get paid for it."

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ChatGPT sparks debate on how to design student assignments now

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Is an ice cream sandwich a sandwich? How about a sushi roll, chicken wrap or sloppy joe? These were some of the prompts included in a classification and model-building assignment in the fall 2022 Knowledge-Based AI course that David Joyner taught at the Georgia Institute of Technology.

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Educator Considerations for ChatGPT - OpenAI API

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Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's platform.

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QAA briefing paper: The rise of artificial intelligence and potential risks for academic integrity

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QAA briefing paper: The rise of artificial intelligence and potential risks for academic integrity

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https://www.qaa.ac.uk/docs/qaa/members/the-rise-of-artificial-intelligence-software-and-potential-risks-for-academic-integrity.pdf?sfvrsn=ebb0a981_6

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https://blog.nomoremarking.com/can-chatgpt-provide-useful-feedback-df3575733d38

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In our last post, we looked at the way ChatGPT's written feedback is superficially impressive but not that helpful pedagogically. In this post, we'll look at ways it can provide something more useful.

Part of the challenge here is that providing good feedback on writing is hard for humans to do, let alone AI. Ideally, you want something that a student has to act on, not something that they just passively read. So you want to produce something that is fairly specific.

But you don't want to produce something that is too specific. The aim, as Dylan Wiliam says, is to improve the student, not the work. This is why I sometimes get uneasy about whole-class feedback that essentially turns into editing or redrafting the work. You don't really want to improve the essay; you want to improve the students' thinking so that their next essay doesn't have the same mistakes.

So how does ChatGPT fare with this challenge? The rest of this blog post will discuss a student response to the following GCSE question: 'It is the people who have extraordinary skill, courage and determination who deserve to be famous, not those who have good looks or lots of money or behave badly.' Write a letter to the editor of a newspaper in which you argue your point of view in response to this statement.

We saw in the previous blog that if you ask it to grade an essay and explain why, it provides a fairly generic paragraph that is based on the mark scheme.

Technically impressive, but not actually that useful.

What about if you ask it how the writer could improve the essay?

This is definitely better, but it falls into the trap I talked about above, of being too specific and of seeking to improve the work, not the student. If this student went off and did more research on Charli Damelio and Kim Kardashian it is unlikely that would help them in their next essay.

So what happens when I change the prompt and ask it to focus on the student, not the essay? I think it comes up with something pretty good!

It thinks run-on sentences and comma splices are important! It brought a tear to my eye! Who isn't going to love an AI that agrees with them!

But I am still not completely happy. Yes, I agree that the writer should practice writing short concise and well-structured sentences - but that's still too vague for me! I want actual examples of what the student should do. So I tried a prompt that asked for an actual activity.

This is so close to being great, but ultimately it is wrong. None of the options are correct, and the option it says is correct has actually just replaced the run-on with a comma splice. The correct option should be: Take Kim Kardashian as an example. She didn't deserve her fame at first, but she grew and learnt.

Still, it is capable in principle of providing specific and actionable feedback that will help a student to improve their thinking. And it would be very easy to edit that question so that it was correct - far easier than writing it out from scratch.

Generally speaking, I find that it is a bit hit and miss when constructing multiple-choice questions. Sometimes it provides questions where all the options are wrong. Sometimes it provides ones where it says there is one right answer but actually there's more than one right answer. Sometimes it just comes up with gobbledegook. In practise I have found it works best with the work of a) younger students or b) older students who have made relatively few errors. It is weakest at providing feedback for older students who have made a lot of errors, as these errors tend to be more complex and harder to unpick.

However, I am inclined to cut it a little slack because I am asking it to do hard tasks that would be incredibly time-consuming for a teacher, and it is still producing some quite useful outputs. You can also follow up by saying 'please give me four more questions like this one'. Not even the most Stakhanovite senior manager would expect their teachers to provide five personalised multiple choice questions in response to every piece of writing.

Obviously, in the examples above I have asked lots of follow-up questions. This isn't particularly efficient. What we really want is to be able to ask one question that will generate everything we want, and ideally for it to be easily editable too so we can correct any errors that creep in. We have built a system like this and integrated it with our No More Marking Comparative Judgement dashboard.

Your students can type their responses into our system, and you can choose what percentage of the judging you would like completed by humans, and what percentage by AI. Once all the judging is complete, each student will get a grade and a specific comment. You can easily review all the scripts and edit the comments. You can also print off the scripts with the comments so the students have a record of them. You could still do whole-class feedback the next lesson using some screenshots of the scripts, and you could potentially use ChatGPT separately to create some good MCQs, if you wanted. And depending on how much judging you chose to do, you'd still be saving time compared to any kind of traditional marking.

Here are some screenshots of how our system works.

Judging dashboard, including human & AI judges

The feedback page - the student essay is at the bottom & editable AI comments on right

The results PDF - it combines all the essays with their score & edited AI feedback

We'll go through how our system works with subscribers in our webinar next Wednesday at 4pm. Sign up here.

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In our last post, we looked at the way ChatGPT's written feedback is superficially impressive but not that helpful pedagogically. In this post, we'll look at ways it can provide something more useful. Part of the challenge here is that providing good feedback on writing is hard for humans to do, let alone AI.

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https://www.youtube.com/live/UJoHK13OPd0?feature=share

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Workshopping AI and Writing with Anna Mills and Maha Bali

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Link to webpage link to this conference:

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ChatGPT: How I learned to stop worrying and ignore the bot

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ChatGPT: How I learned to stop worrying and ignore the bot

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https://www.peterbryant.org/chatgpt-idgaf-or-how-i-learned-to-stop-worrying-and-ignore-the-bot/

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If you weren't critical readers of blogs like this, you would have thought there was no bigger story in the world than ChatGPT. Littered with hyperbole, barely evidenced case studies and the kind of end-is-nighism we have not seen since, well since MOOCs, the education press has been extremely quick to declare ChatGPT to be the future-executioner-calculator-fire-wheel of the modern university as we know it. Advocates have unpacked and dusted off the claims that anyone who doesn't embrace the generative AI change is a luddite who is doomed to spend their lives in the Middle Ages (let's ignore the historical contradictions). Fear mongers are amping up the disaster rhetoric (one-third of universities will go broke in five years because of ChatGPT, claims Jordan Peterson). On the other hand, the more rational voices in higher education are left to navigate a way through the middle and argue it's the chicken not the egg that's important.

We shouldn't be talking about what is effectively vendor spam (it is no coincidence that most of the press releases have focused on education - a very lucrative market and IMHO often a very gullible one when it comes to technology). We should be talking about assessment and how it facilitates learning. Changing assessments to 'defeat' or 'detect' generative AI is both counter-intuitive to good learning design and effectively feeding the beast. Each time we go to the bot to try it out, test its powers and fuel our fears that the machines will take over, we feed it. When it doesn't know an answer to a question it gets asked a few times it goes searching for information to formulate a response. When we use ChatGPT we are doing the beta testing for the company that owns the bot. OpenAI became a capped profit entity in 2019 (despite the name) and has received funding and/or governance from Elon Musk, Peter Thiel co-founder of PayPal, Sam Altman from LinkedIn and Microsoft amongst others (see the Wikipedia page). Whilst their intentions are to feed the open-source market, Reuters estimates that ChatGPT will earn US$200 million in 2023 and US$1billion in 2024 for OpenAI. This is bolstered by an estimated US$10 billion funding provided by Microsoft.

So why don't I give a fuck about ChatGPT? Well, let me posit four reasons.

The people advocating for widespread disruptive change to education because of ChatGPT and other generative AI platforms are often the same people (or use the same tropes) as those who claimed MOOCs would change the future landscape of higher education. And guess what, they didn't. Neither did Pokémon Go, Second Life or any other technological 'disruption'. Not even an experience of unprecedented potency like the pandemic led to long-term change in higher education, with the snapback discussed in this blog in full swing and online learning returned by institutions to its apparent 'natural home' on the fringes and declared a second-class experience when compared to the 'perfection' of our face-to-face teaching model. It is too early to make a prediction about the future impacts of generative AI or whether it be the next ubiquitous technology like the computer or the calculator as a tool for learning. But that doesn't stop advocates wanting to be first to the media well to say they know better and that knew it first.

Assessment in higher education has been a problem for decades. Our students have told us this in successive student satisfaction surveys at a local and national level, that put satisfaction with assessment and feedback at significantly lower levels than anything else (including car parking and university food). Yet, we are still wedded to the exam as mode of assessment (returning to it like a recently found lost favourite sweater after the pandemic). We have deeply inculcated a small suite of assessment modes and practices into our LMS, our technology suite, our integrity detection and our measures of quality, achievement, and performance. This rusts these practices onto our curriculum and quality assurance and enhancement processes. We have been talking ad debating authentic assessment for well over a decade now with little evidence of a widely accepted definition, frameworks and new types of assessment modes, question types and feedback. As universities have marketised and increased cohorts and breadth of program offerings, the imperative for assessment has become scale. Auto-marking, online exams, AI generated feedback and other efficiency interventions are fed by more simplistic, dichotomous, memory-based, or standardised questions. The problem that AI apparently exposes has been there for decades. In general, we design inauthentic assessments with bad questions that don't assess or facilitate learning. They can be searched through Google, replicated by contract cheating sites, and aggregated from unattributed sources before we even get to AI.

Of course, if we keep assessing in inauthentic ways and we keep asking students to repeat knowledge back to us or to show how much they remember using modes of communication they will never be asked to use again then ChatGPT makes what they can already do simpler, easier, and more convenient. And because we are simply asking for words to be fed back us, then a generator of words like ChatGPT is perfect. Generative AI does not produce knowledge. It does not know who you are and what learning is doing to you as a person. It does not understand meaning. What is does is write words, without meaning or context. It is the perfection of the infinite number of monkeys writing Shakespeare argument (it is a thing, it is called the infinite monkey theorem). Does text generated by ChatGPT work when words without meaning demonstrate a definition of competence? Yes. But is that learning? The meaning of the words in a Shakespearean play come from the reading, the performance and what emotions, feelings, actions they engender, and further how those words reside within a unique set of lived and yet to be experienced crises, challenges, relationships, and moments. Learning resides in the same.

ChatGPT doesn't learn. It just becomes more efficient

What ChatGPT is, in this instance, is replication as travesty. ChatGPT may be able to write a speech or an essay or a sermon or an obituary, but it cannot create a genuine song. It could perhaps in time create a song that is, on the surface, indistinguishable from an original, but it will always be a replication, a kind of burlesque. Songs arise out of suffering, by which I mean they are predicated upon the complex, internal human struggle of creation and, well, as far as I know, algorithms don't feel. Data doesn't suffer. ChatGPT has no inner being, it has been nowhere, it has endured nothing, it has not had the audacity to reach beyond its limitations, and hence it doesn't have the capacity for a shared transcendent experience, as it has no limitations from which to transcend. Nick Cave from the Red Right Hand Files

If we assume that assessment is there to assure that knowledge has been remembered and can be applied to abstracted, standardised contexts such as case studies then ChatGPT makes the process of evidencing that outcome more efficient. The more we feed it questions, the more it improves its responses. The more data it consumes, the more it reinforces its answers. If it doesn't know or have enough data, it either tells you or it makes it up. But if we ask it what have I learnt, or how I have used the knowledge to further my understanding of the unique phenomena and experiences around me? It can consume information about you, and make assumptions, but it cannot get to know you. It cannot understand you nor can it experience what you have experienced to make you the person or leader or professional you are. Learning in the way it is constructed is a human trait built on, as Nick Cave so eloquently puts it, emotions, and experiences such as suffering and grief, but also joy, satisfaction, confidence, sociality, ego, and ambition amongst so many others. Learning is not a procedure, it is a sometimes traumatic, sometimes joyous journey of transition from not knowing to knowing, from incompetence to competence and from personal to collective. No generative AI can replicate that.

A general question asked to ChatGPT, a generic but OK answer (with US spelling mind)

The same question but with a specific subject added (also good to know the bot who I am. Yet)

Generative AI exists in a challenging and grey ethical area. First, there is the use of humans to help reduce the propensity for the bot to be abused to generate toxic content. There are allegations that workers in the global south were used to manually train the bot to not react to prompts and provocations for generate pro-fascist content (for example). Secondly, there is the issue of where the data has been scraped from and whether that has been attributed, and thirdly the thorny copyright issue of who owns the output of generative AI. This is a legal quagmire that has yet to be even vaguely tested in court. For higher education, who owns the intellectual data and more importantly, how that data is recognised and cited is critical. This will invariably be rigorously fought in courts around the world, especially with the source data that the bots use to generate natural language responses and then as the monetisation happens, how the 'copyright' of the generated text is enforced. Like many tools, ChatGPT is built on the labour of others. It assumes the precepts of the Internet as the wild west, without legislative or copyright boundaries, which has been proven time and again to not be true. Academia has struggled with the greyness of Internet knowledge, beating itself up over Wikipedia and open access often at the behest of publishers.

Higher education needs to look at this data in the same way it applies deeply structured and transferable standards of the ethical management of data. Who owns it? How was it collected? Did the parties give consent? Is it managed to ensure privacy, attribution, and agency for the sources of data? Universities must apply these standards, why not ChatGPT?

So, what do I give a fuck about then?
CTFD - and do as the Hitchhiker's Guide to the Galaxy says...
We don't panic. We stop jumping about like beans trying to defeat, embrace or outflank the bots. Each time we do that in beta we are simply giving the developers more test data, even more than they can scrape.

We don't panic! We don't need to change assessment tomorrow, mainly because the moment we do, we feed it again to find ways around what we do. Universities are huge opportunities to monetise this platform. The fact that so much traffic talks about how ChatGPT creates assignments (at credit level whatever that means) shows that the media forces behind ChatGPT are very willing to poke the bear and get a response (and its working a treat).

We don't panic. Writing better questions and setting more authentic tasks will always encourage a learner to demonstrate their learning. They will generate more effective and useful feedback and feed-forward. Assessments that require more than memory, regurgitation or replication engage learners in higher-order, transdisciplinary skills. They prompt and catalyse journeys through transitional spaces and can create deeper learning through real or simulated experiences. This isn't theoretical. Academics have been making assessments like this for decades. Simulations, reflections, presentations, portfolios...the list is endless, its just that scale and systems have made them seem niche.

We need to think deeply about assessment. Change the damned questions and stop asking students to regurgitate our curated version of knowing. That is not learning. Stop asking students to apply and recontextualise knowledge in the standardised images we create but to use knowledge and skills within their own unique ecosystems of lived experiences, work, life, play and learning. No bot can make that real for any person. All the bots can do is create another fake person, with a fake picture and fake life. If that is good enough for us as teachers and for you as students, then we have failed as academics and the prophecies of the doomsayers deserve to be right. We are better than that, and our assessment and teaching must reflect that. ChatGPT is just another warning sign that we have to think about, but it is not the first and it won't be the last. Make assessment that are epistemologically, educationally, and experientially authentic. By that I mean, they have value and meaning to both the educator and the student. They catalyse learning through action, application, and connection. They use knowledge and skills, but don't require students to repeat or replicate it. They don't have to take longer to mark, they don't have trick the AI and they don't require the dismantling of everything we know and love in the academy. I will have some more to say in the next few weeks on designing and deploying authentic assessment.

Thirdly, let's open the door. Let's treat any generative AI like a Google search or a journal article or a book, or even Wikipedia. Go ahead students, use ChatGPT to write your assignments. BUT REFERENCE IT. We ask this of references drawn from the Internet, we ask this of 'traditional' literature, we ask this of any writing that is NOT YOUR OWN. So sure, use ChatGPT to write part of your essay. But get graded on its quality and the admission you did not write it. And if you choose to not acknowledge the source of the information then you are plagiarising, you are engaging in academic misconduct, because all sources deserve to be acknowledged. That is a human trait of respect. You may wish to go further and consider the veracity of the words generated by ChatGPT. Are the references real? Are the sources of information that it scrapes (and does not acknowledge) trustworthy and reliable? Be critical. Finally, you may do as my friends Professor Lawrie Phipps and Dr Donna Lanclos suggest and consider the ethics of using words generated by ChatGPT, and acknowledge that:
This presentation/paper/work was prepared using ChatGPT, an "AI Chatbot". We acknowledge that ChatGPT does not respect the individual rights of authors and artists, and ignores concerns over copyright and intellectual property in the training of the system; additionally, we acknowledge that the system was trained in part through the exploitation of precarious workers in the global south. In this work I specifically used ChatGPT to ....
DigitalisPeople - Lawrie Phipps and Donna Lanclos, with the help of Autumn Caines

Just like there was at each of the 'watershed' moments in higher education, any opportunity to rethink assessment should be welcomed. Any opportunity to de-rust some of the assessment architecture and separate the quality of assessment from the UX of the technology and IT infrastructure should be grasped. Any shift in the mindset that assessment weeds out the weak and rewards those who follow the narrow pathways of learning that have defined disciplinary knowledge for decades should be celebrated. Maybe we can start giving a fuck about assessment. That would be nice.

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ChatGPT: IDGAF (Or: How I Learned to Stop Worrying and Ignore the Bot)

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If you weren't critical readers of blogs like this, you would have thought there was no bigger story in the world than ChatGPT. Littered with hyperbole, barely evidenced case studies and the kind of end-is-nighism we have not seen since, well since MOOCs, the education press has been extremely quick to declare ChatGPT to be the future-executioner-calculator-fire-wheel of the modern university as we know it.