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No question if they’re too fancy for you to handle. If you always want to go the extra mile to get what you want. Ensure your experience onsite is smooth sailing. https://j4l.com works its magic to get you what you want, if you really want it, you get it. Your special lady could be just a click away. Stellar customer support is at your fingertips. The site typically offers video streaming featuring amazing creatures live. You feel the king, and you’re the king. Send a few free messages and see how it goes. A well-written letter is a grand leap towards great relationships.

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all machine learning suffers from the same training bias. ChatGPT is nice as it gives an alternative pathway to generate information but concerns about results remain

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AI and Higher Education: Is it time to re-think teaching and assessment? – Learning Technologies

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Over the past few weeks, social media has lit up with talk of a new AI technology that would put the credibility of students’ assessments in doubt going forward. So what is this new AI technology, and what are the potential implications regarding its impact on Education? This blog examines these qu…

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Writing already involves AI, in the form of predictive text for example. We are familiar with this on our phones and in our emails. Humans have been collaborating with technology for writing since sticks were used for drawing in sand or on cave walls.

Ingenuity and creativity mean that this technology is constantly changing. Most recently, the advent of AI writers (software that uses artificial intelligence to generate text) has created a lot of excitement and concern.

How do AI writers work? You simply enter what you'd like to write about in an onscreen box and an AI writer will produce almost human-quality text in moments, related to your topic. AI writers can also take an existing piece of text and "spin" it, to produce multiple different versions.

In higher education, worries about academic integrity have clouded exploration of the potentials of AI writing. Yet for people in many careers already, working effectively with AI writers is an essential part of their everyday responsibilities.

Students today need to be prepared for a future in which writing with AI is already becoming essential. Just as word processor functions such as spelling and grammar checks have become accepted and integrated into writing practices, so too will text generators.

Journalists, administrators, content creators, academics, poets and many others are using AI writing in many creative and efficient ways. The problems start when AI writers are not attributed for their input. When students submit AI-authored work as their own, this is a breach of academic integrity.

Yet the lines between human and machine have become blurry. We readily use strings of predictive text in texts and emails, without citing the program or algorithm that produced the words.

So how can students use AI writers in higher education? The following suggestions allow for academic integrity and for the development of higher-level thinking skills.

1. Use AI writers as researchers. They can research a topic exhaustively in seconds and compile text for review, along with references for students to follow up. This material can then inform original and carefully referenced student writing.

2. Use AI writers to produce text on a given topic for critique. Design assessment tasks that involve this efficient use of AI writers, then critical annotation of the text that is produced.
3. Use different AI writers to produce different versions of text on the same topic, to compare and evaluate.
4. Use and attribute AI writers for routine text, for example blog content. Use discrimination to work out where and why AI text, human text or hybrid text are appropriate, and give accounts of this thinking.

5. Use and attribute AI writers for creative text, for example poetry. Google's Verse by Verse requires the user to input a first line, then writes the rest of the poem or provides suggestions based on the work of famous poet muses. This is just one of countless ways that AI can make interventions in creative processes. Students can research the multiple programs and algorithms on offer.
6. Explore and evaluate the different kinds of AI-based content creators that are appropriate for your discipline.
7. Research and establish the specific affordances of AI-based content generators for your discipline. For example, how might it be useful to be able to produce text in multiple languages, in seconds? Or create text optimised for search engines?

8. Explore different ways AI writers and their input can be acknowledged and attributed ethically and appropriately in your discipline. Model effective note-making and record-keeping. Use formative assessment that explicitly involves discussion of the role of AI in given tasks. Discuss how AI could lead to various forms of plagiarism, and how to avoid this.

All the above require specific skills in search optimisation, evaluation and editing. Then there's speculative work: students can create design documentation for AI text and content generators of the future for their own disciplines. What kind of innovative AI could support a landscape gardener? A user experience designer? An accountant? A doctor?

All the suggestions above demand the development of a critical awareness of the normative and excluding potentials of algorithmic content generation. This is a part of the reason why these tools must be acknowledged and, indeed, embraced in higher education. Pretending that they do not exist or banning them outright will not prevent or enhance their use. Some of the key critical questions to ask about any AI text generators are:

What was the body of material on which this AI was trained? In other words, what has this AI read and absorbed, to make its "assumptions" of what strings of words make "sense"?

Who, and what, has been excluded from this body of material, and therefore, potentially, the text generated?

What assumptions, biases and injustices are embedded in this material, and therefore, potentially, in the text generated?

Socially responsible engagement with AI text generators needs to be both creative and critical. The opportunity to experiment with AI - while always being aware that users are themselves training AI in the process - is a vital component of contemporary higher education.
Lucinda McKnight is a senior lecturer in pedagogy and curriculum at Deakin University and an Australian Research Council DECRA Fellow. 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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Eight ways to engage with AI writers in higher education

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Just as spelling checks and predictive text have become accepted, so too will AI writers, so educators should help students responsibly engage with and understand the potential and limitations of these text generators, writes Lucinda McKnight

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ChatGPT and the Future of Assessment in Higher Education

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Bias and explainability in artificial intelligence

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Encourages lectures to use more innovative and interesting forms of assessment

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People will potentially pass degrees without adequate understanding

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Could support structure moreso than content

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It states 'wrong' things with a lot of confidence

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It states 'wrong' things with a lot of confidence

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For any major writing, gives you a start... everyone hates staring at a white page when beginning!

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For any major writing, gives you a start... everyone hates staring at a white page when beginning!

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Possibility of improving objectivity (AI in general) in marking

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Possibility of improving objectivity (AI in general) in marking

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A threat to our roles as academic writing support staff

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From examples I've done myself and others, the ai lies sometimes and doesn't know it is

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This is a round table event that includes Rob Howe and Rhona Sharpe to discuss the impact of this technology on the student learning experience and how we might research it. It is difficult to escape conversations about the recent release of ChatGPT. This sudden jump in awareness and capabilities of AI raises questions about the student learning experience and how we might understand its impact. We would like to provide a space to talk about how and what we might learn about the student use and perceptions of AI. Is there a need for focused look at this or does this fit in to existing frameworks or questions about the students' experience? If there are opportunities what are the broad areas this affects? Where do we think it is priority, and are there any past lessons from past digital experience evaluations that we should think about now? If you have some grant money and the time what would be your focus? Come along and share your ideas. Rob Howe.
https://pure.northampton.ac.uk/en/persons/rob-howe Rhona Sharpe https://www.ctl.ox.ac.uk/ourteam/rhona-sharpe

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ELESIG: ChatGPT and the student digital learning experience

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This is a round table event that includes Rob Howe and Rhona Sharpe to discuss the impact of this technology on the student learning experience and how we might research it. It is difficult to escape conversations about the recent release of ChatGPT.

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Fundamental challenge to traditional assessment pardigm

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Fundamental challenge to traditional assessment pardigm

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Demonstrate to students and show the pitfalls of using the technology

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Demonstrate to students and show the pitfalls of using the technology

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Language students using ChatGPT to write their work and therefore assessment not showing students' true language level.

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Ethical concerns about how the text indexing is undertaken and people's working conditions in this industry

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Ethical concerns about how the text indexing is undertaken and people's working conditions in this industry

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Kenyan workers work for less than $2 per hour to filter harmful content on ChatGPT

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TIME claimed that the AI development company OpenAI hired mainly Kenyan workers for 2 dollars an hour to filter harmful content on ChatGPT.

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Ingrained bias in the information. Returned. Lack of awareness around IG from the user. Insufficient training leads to people blindly thinking the information is correct. 

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Ingrained bias in the information. Returned. Lack of awareness around IG from the user. Insufficient training leads to people blindly thinking the information is correct. 

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open discussions about uses, pitfalls and academic integrity - why are we at university?

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Inability to supervise its use or see and test where it's being used inappropriately e.g. plagiarism

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Inability to supervise its use or see and test where it's being used inappropriately e.g. plagiarism

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Lack of agreement on impact within academic teams

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Lack of agreement on impact within academic teams

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It keeps academic conduct people in jobs...

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What does embracing ChatGPT in education look like?

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What does embracing ChatGPT in education look like?

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Generating ideas for examples and finding new perspectives

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How can we offer guidance to your academic staff in terms of students using ChatGPT?

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Showcase good examples, promote self-learning

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Showcase good examples, promote self-learning

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Use of language translation to allow novices to write code

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Use of language translation to allow novices to write code

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Both broad: acknowledgement of sourses, training and data cleaning issues,

and narrow: academic integrity, equity of opportunity, accessibility

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Embrace ChatGPT within assessment approach, eg review and critique how well Chat GPT output aligns with LO and what improvements could be made

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Use it to challenge students to analyse the responses/ results

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Use it to challenge students to analyse the responses/ results

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Have students learn about it directly and understand positives/negatives of using it & appropriate uses

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Have students learn about it directly and understand positives/negatives of using it & appropriate uses

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Time saving when writing and having to paraphrase ideas.

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The possibility that students will use ChatGPT to generate answers/solutions

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Integrity and lack of fairness. Students passing when they know nothing of the subject. Existential crisis for some subjects.

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Editor's Note. Today's post is by Curtis L. Kendrick. Curtis currently serves as Binghamton University Libraries Faculty and Staff mentor, having most recently served as dean of libraries for the University. Prior to his positions at Binghamton, Kendrick served as university dean for libraries and information resources at the City University of New York (CUNY), and has held library administrative positions at Columbia University, Harvard University, Stony Brook University and Oberlin College.
On November 30, 2022, OpenAI released ChatGPT ("Generative Pre-trained Transformer") to the public. OpenAI is a research company that works in the area of artificial intelligence and is a hybrid for-profit/non-profit organization. ChatGPT is an artificial intelligence language model that can generate human-like text based on a given prompt. It is trained on huge data sets consisting of a massive amount of text data. The Harvard Business Review notes, "while versions of GPT have been around for a while, this model has crossed a threshold: It's genuinely useful for a wide range of tasks, from creating software to generating business ideas to writing a wedding toast."

In preparation for a presentation about race and academic libraries I tried ChatGPT (Jan 9 version) to see what it (they?) had to say. I was curious about how it worked and how accurately it responded to queries. The system is not quite ready for prime time, as even OpenAI notes, "While we have safeguards in place, the system may occasionally generate incorrect or misleading information and produce offensive or biased content. It is not intended to give advice." OpenAI's philosophy is to "release these models into the wild before all the guardrails are in place, in the hopes that feedback from users will help the company find and address harms based on interaction in the real world."

For this assessment I posed queries to ChatGPT about racism and whiteness in academic libraries, requested more information in a follow-up ask, and then requested the system provide me with citations on the topic. (See Appendix I: Transcript for a transcript of the interaction with ChatGPT.)

Analysis

In a series of interactions, ChatGPT was asked to provide information about racism and whiteness in academic libraries. The responses generated were credible, clearly written and the program can provide nuance in its responses. The quality of the writing is about on par with a good Wikipedia entry. There were times when re-phrasing the question was necessary so the program would understand what was being asked of it. For example, the program was asked to find articles that discuss whiteness as a racial characteristic in academic libraries, and it responded with "I'm sorry, I am unable to find articles in academic libraries," as if it thought it was being asked to deliver the articles.

Where ChatGPT failed miserably was in the citations it provided. Half of the 29 citations checked were from just two publications, Journal of Library Administration and Library and Information Science Research Journal. While the citations are adequately formed, typically they were incomplete, generally lacking volume or issue numbers.

The main problem found with ChatGPT is that the citations refer to articles that don't exist (see Appendix II: Citations). They are phantom citations leading nowhere. Each citation was searched on Proquest's Library and Information Science Collection, and the most common response was: "Your search for "article title" found 0 results." Of the 29 citations checked, only one was accurate, one was correct but had the title transposed, and one was to a real article, but the source journal provided by ChatGPT was incorrect. When questioned as to the accuracy of the citations it provides, ChatGPT grew indignant, claiming, "The articles and studies I listed were published in reputable academic journals and written by experts in their field." Well, maybe not so much. When pressed on this point, ChatGPT vacillated somewhat, offering "there may be instances where the information I provide may not be completely accurate or up-to-date." Agreed. The system did give a shout-out to librarians, noting, "It is also recommended to consult with librarians and other subject matter experts, to ensure the accuracy of the information and to get the latest information."

To provide extra verification the journal title/year combination was checked to verify the non-existence of the article. Each issue of a journal for the specified year was checked, and we found no trace of the items cited.

Phil Davis notes, "ChatGPT works in the same way your phone's autocomplete function works while texting; it simply puts words together that are statistically likely to follow one other." AI programs are initially exposed to training data to provide the program with a knowledge base from which to make its inferences. The solution to the citation problem is to expose ChatGPT to training data from the academic realm, perhaps the JSTOR corpus, or information from ScienceDirect, or one of the citation tracking sources. This would enable ChatGPT to provide citations to actual articles and other works, rather than making them up like a first-year student with a paper due the next morning.

ChatTGPT was asked if it was trained on the academic literature. The program was not, rather, used a diverse array of sources from the public Internet and social media sites. Thinking about the work of incorporating academic literature into its training base, the program described a rational process for how to engage with large academic data sets, although it was vague when pressed for a deliverables timetable for such a project. (See Appendix I: Transcript)

As far as the librarians, I think we better stick around for a while longer.

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Guest Post - The Efficacy of ChatGPT: Is it Time for the Librarians to Go Home? - The Scholarly Kitchen

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Editor's Note. Today's post is by Curtis L. Kendrick. Curtis currently serves as Binghamton University Libraries Faculty and Staff mentor, having most recently served as dean of libraries for the University.

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About chatgpt

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Artificial Intelligence in accounting education: what are the implications?

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Artificial Intelligence in accounting education: what are the implications?

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Artificial Intelligence in accounting education: what are the implications?

Martin Compton and Susan Smith UCL

Thu, Feb 16, 2023, 3:00 PM - 4:00 PM (UK time)

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Welcome! You are invited to join a meeting: Artificial Intelligence and accounting education: what are the implications?. After registering, you will receive a confirmation email about joining the meeting.

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In this Accounting Cafe seminar Dr Martin Compton and Professor Susan Smith seek to separate hype from reality and explain how educators can review their assessment practices in an AI world.

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Makes a good starting point for doing research, by compiling information from various sources

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Makes a good starting point for doing research, by compiling information from various sources

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Summarise journal articles. Analyse texts and generate hypothesis. 

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Summarise journal articles. Analyse texts and generate hypothesis. 

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We need to embrace the world our graduates will be entering - and help them understand the limitations and ethical issues of its use.

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We need to embrace the world our graduates will be entering - and help them understand the limitations and ethical issues of its use.

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The genie is out of the box, and we need to work with ChatGPT and prepare our students for the word of work where AI of this nature will be increasingly used

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Whether plagiarism/mitigation checks used in education can keep up with the speed of how AI is being used by students for assessments 

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Whether plagiarism/mitigation checks used in education can keep up with the speed of how AI is being used by students for assessments 

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Support research, plan course outlines, generate new assessment methods

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Support research, plan course outlines, generate new assessment methods

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I think this has the potential to create a shift in what we need to assess, e.g. is writing an essay an authentic assessment?

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I think this has the potential to create a shift in what we need to assess, e.g. is writing an essay an authentic assessment?

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Sandpit of examples of ways you could experiment with chatgpt to do things beyond generating pure prose

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Sandpit of examples of ways you could experiment with chatgpt to do things beyond generating pure prose

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ChatGPT could be considered an “assistive technology” if it assists people with communication disability to get their message across more efficiently.

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If we don't change assessment approaches

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If we don't change assessment approaches

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If we do nothing, and keep to the same assessment approaches....

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Include discussion around ChatGPT in the curriculum.  This is so relevant for Data Analytics/Data Science/equivalent degrees

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Include discussion around ChatGPT in the curriculum.  This is so relevant for Data Analytics/Data Science/equivalent degrees

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Already started - open and honest, talking about professional values and integrity

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Already started - open and honest, talking about professional values and integrity

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As a language model created by OpenAI, I do not have personal opinions. However, some advantages of ChatGPT include:

1. Large Scale: ChatGPT is trained on a very large dataset, allowing it to have knowledge on a wide range of topics and generate human-like text.

2. Language Generation Capability: ChatGPT can generate text that is grammatically correct and semantically coherent.

3. Multitasking: ChatGPT can handle multiple tasks, such as question answering, text completion, and language translation, making it versatile and useful in a variety of applications.

4. Human-like Communication: With its training data consisting of human language, ChatGPT can generate human-like text and engage in human-like conversation, making it ideal for use in customer service, virtual assistants, and other human-computer interaction scenarios.

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How will students and teachers explore the boundaries of useful use, appropriate use and inappropriate use

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Include students in co-creation of curriculum

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Include students in co-creation of curriculum

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As a language model, ChatGPT reflects the biases present in the data it was trained on, which may result in it generating text that is not inclusive or respectful to certain groups of people. This is a common issue with AI models that are trained on large amounts of text data, which can contain harmful stereotypes and biases.

OpenAI is aware of this issue and is actively working to address it through various measures, such as fine-tuning the model with more diverse and inclusive training data and implementing fairness and bias mitigation techniques. However, it's important to note that there is still work to be done to ensure that language models like ChatGPT are fully inclusive and produce text that is respectful and fair to all groups of people.

As a user, it's important to be mindful of the potential biases and limitations of language models like ChatGPT and to use them in an ethical and responsible manner.

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From an HE library: Align with institutional strategy to improve or tweak existing plagiarism awareness teaching across all courses. Use this as an opportunity for advocacy for the library's remit in terms of teaching ethical use of information.

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From an HE library: Align with institutional strategy to improve or tweak existing plagiarism awareness teaching across all courses. Use this as an opportunity for advocacy for the library's remit in terms of teaching ethical use of information.

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https://ai-uk.turing.ac.uk/

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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.

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Critical thinking opportunity if AI-generated text is framed within pedagogy, rather than banned.

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Critical thinking opportunity if AI-generated text is framed within pedagogy, rather than banned.

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ChatGPT can be a useful tool for education in the following ways:

1. Providing quick answers to students' questions: ChatGPT can be used as an AI tutor to answer students' questions about a wide range of subjects.

2. Enhancing writing and language skills: ChatGPT can assist students in improving their writing and language skills by generating text completions, summarizing long documents, and correcting grammar and spelling errors.

3. Encouraging creativity: ChatGPT can be used as a creative writing tool, generating ideas and suggestions for students' writing projects.

4. Personalizing learning: ChatGPT can be integrated into personalized learning programs, adapting to the individual needs and interests of each student.

It's important to note that while ChatGPT can be a useful educational tool, it should not be solely relied upon as a substitute for human interaction and interaction with traditional educational materials.

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Students being given misinformation unintentionally by the ai or the risk of it being manipulated to spread propaganda and misinformation (e.g. Chatgpt has been used to write polymorphic malware)

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As an AI language model developed by OpenAI, I do not have personal beliefs, opinions, or biases. My training data includes text from a wide range of sources, which includes text written by people from diverse cultural and socioeconomic backgrounds.

However, it's important to note that the data used to train me reflects the biases and limitations of the internet and can perpetuate harmful stereotypes and misinformation. OpenAI is actively working to mitigate these issues by continuing to improve and diversify our training data and to build inclusive language models.

It's also important for users to critically evaluate the information generated by language models like me and to verify the information with reliable sources.

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It's a great way to get an introduction, particularly if you ask it for its sources.

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can it be used for transcription?

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If you push it it becomes inaccurate, or even misleading

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My concern is how to help Educational leaders to support teachers in primary & secondary education (k-12) to embrace using a tool for education rather than fighting to lock it down.  How can we support schools to be more proactive and thereby undermine the nefarious uses.

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My concern is how to help Educational leaders to support teachers in primary & secondary education (k-12) to embrace using a tool for education rather than fighting to lock it down.  How can we support schools to be more proactive and thereby undermine the nefarious uses.

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Open Book Exams with no access to computer though is what may be needed for most exams but in some CHATGPT could be allowed and critiquing skills be assessed - Here is a guide on open book exams I produced: https://researchportal.port.ac.uk/en/publications/designing-open-book-exams

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Open Book Exams with no access to computer though is what may be needed for most exams but in some CHATGPT could be allowed and critiquing skills be assessed - Here is a guide on open book exams I produced: https://researchportal.port.ac.uk/en/publications/designing-open-book-exams

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As it has been programmed by (bias) humans, then the quality of the "information" or responses is questionable.

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QAA briefs members on artificial intelligence threat to academic integrity

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QAA has published a briefing note to support members in tackling challenges to academic integrity which have been brought about by the rise of artificial intelligence (AI) tools.

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One of the skills we are often trying to teach is how to evaluate work, chatGPT offers a good opportunity for this (have them look at a chatGPT output and pick out the mistakes).

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Unlocking the Potential of Language Generation Tools in Higher Education: A Progressive Approach to Assessment Reform

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Lisa Gray, Senior Consultant, Learning, Teaching and Assessment. PebblePad The proliferation of AI tools such as ChatGPT in Higher Education has prompted a discussion on their potential implications- particularly on assessment practices. While some may view these tools as a disruption to traditional pedagogical paradigms, it's important to consider them as opportunities for rethinking and advancing assessment methods.

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Could be helpful to students in developing their learning through critical reading and reflection

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Support students to critically evaluate digital tools - advantages/disadvantages.

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Automated Essay Writing: An AIED Opinion - International Journal of Artificial Intelligence in Education

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Automated Essay Writing: An AIED Opinion - International Journal of Artificial Intelligence in Education

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Essay Writing with Transformer AI Systems

Essays have formed part of academic assessment since the early 19th century, when students in some European universities were required to write scholarly papers for discussion in seminars (Kruse, 2006). Academic essays continue to be a mainstay of assessment in schools, colleges and universities because they are easy to set, assess depth of understanding, and train students in how to express an argument. In recent years, the scope of written assignments has expanded to include narrative, argumentative, reflective, expressive, responsive and analytical papers.

The widespread practice of setting written assignments as coursework has been criticised as laborious and unfair (Race, 2018). It is also subject to contract cheating through "essay mills" that sell written assignments to order, at fees of up to £400 for writing a 20-page essay1. A study by Newton ( 2018) found that 15.7% of students surveyed admitted to paying someone else to write an assignment. Now, an unintended consequence of generative "Transformer" AI systems such as GPT-3 is that they democratise cheating. A student can generate an entire essay in seconds, at a cost of around 50 US cents2. Equally worrying, would-be academic researchers can call on these systems to generate articles for submission to conferences and journals.

How do such Transformer AI systems work? How can we detect whether an academic essay has been written by an AI? Could any good come from widespread use of such technology? What does this mean for the AIED community?

How Does a Transformer AI Work?

Take time to read this brief undergraduate student essay on "A Critique of Learning Styles". If this were your student, how would you assess the written work? What mark would you give? What are its strengths and weaknesses?

The construct of "learning styles" is problematic because it fails to account for the processes through which learning styles are shaped. Some students might develop a particular learning style because they have had particular experiences. Others might develop a particular learning style by trying to accommodate to a learning environment that was not well suited to their learning needs. Ultimately, we need to understand the interactions among learning styles and environmental and personal factors, and how these shape how we learn and the kinds of learning we experience.
Learning Styles and Learning Modes
In the 1970s, learning styles became an important feature of educational practice. In the 1990s, "learning modalities" became a major focus of learning style research. This is the idea that we process information in different ways. For example, some individuals may be auditory learners, benefiting from listening to lectures and using recorded materials. Others may be visual learners, who gain from using visual materials such as textbooks, handouts, and computer graphics. Still others may be kinesthetic learners, who benefit from learning by movement and doing. Numerous experiments have been conducted to test this idea, and a number of different classifications of learning modalities have been proposed.

In a review of this research, Dunn and Dunn (1997) found that the evidence does not support the idea that people have a particular learning mode that is dominant or that they process information in a particular way. In their study, the authors found that when students were given a particular instructional treatment, they could be classified in a particular learning modality category. But when they were given a different instructional treatment, they were classified in a different category. In a follow-up study in which they gave students a number of instructional treatments, Dunn and Dunn found that students could be placed in a particular category in some treatments, but not others.

Dunn and Dunn also found that the results varied by age. Older students generally fit the results of more than one modality, whereas younger students were more likely to fit the results of just one modality. This led them to conclude that style preferences are learned over the course of development. This is consistent with the idea that people are flexible in responding to the way they are taught.
References
Dunn, R., & Dunn, K. (1997). Learning styles: Research and practice. Journal of Research in Education, 7(2), 139-151.

The entire essay was written by GPT-3, a Transformer AI program. It was given the prompt "The construct of 'learning styles' is problematic because" and it generated the rest, including heading and citation. The text shown here is the first attempt. The reference at the end was generated by adding the prompt "References" to the end of the text and resubmitting.

Gaining access to GPT-3 is straightforward. Anyone with internet can sign up to the OpenAI website3, gain an account, click the "Playground" tab, type a prompt such as the title of an essay, set the maximum length of output (up to 4000 language "tokens", or approximately 3000 words) and click the Submit button. A few seconds later, the system produces a typed and formatted text. Some companies are already promoting AI-based essay writing services4.

GPT-3 works like a highly trained text completer of the kind found on mobile phones and email interfaces. Instead of looking back at the last few characters and using these to predict the next word or two, it can attend to the previous 750 words it has written, to continue an entire short story, blog, or student essay. The same program can also summarise a scientific article in simpler language, write a review, translate languages, and answer general questions. In short, a Transformer AI is a general-purpose language machine.

How Can We Detect Whether An Essay is Written by a Transformer AI System?

Plagiarism software will not detect essays written by Transformers, because the text is generated, not copied. A Google search shows that each sentence in the essay above is original. Two plagiarism detectors each indicated over 95% original text5.

What better way to discover whether the essay is written by human or machine than to ask GPT-3? Here is its response when presented with the essay above and the prompt "Was that essay written by a human or a computer?".

It is difficult to say for certain, but it seems more likely that it was written by a computer. The language is very technical and does not seem to flow smoothly. In addition, the ideas are presented in a very concise, bullet-point style, which is often seen in computer-generated texts.

Impressive, yet it gave a similar response when presented with the introduction to a highly-cited research paper written by Sharples (Sharples, 2010), claiming it "does exhibit some characteristics that are often associated with AI-generated text, such as a lack of clear authorial voice and a somewhat stilted, awkward style of writing". Rather than attempt further embarrassing comparisons, we note that any sufficiently powerful program to determine whether a piece is written by human or machine could frequently be outwitted by an equally powerful AI text generator, in a futile computational arms race.

Humans fare no better than machines at detecting AI-generated essays. In a small study by EduRef.net6, college professors were asked to grade essays produced by human writers and by GPT-3, without being informed that any piece was generated by machine. For a Research Methods topic, the machine-written essay was given a grade of C, while the human essays were graded B and D. For US History, machine and human were given similar grades. For a Law essay, GPT-3 was graded B-, while the human essays ranged from A- to F. For one topic, Creative Writing, the machine essay was failed, while human-written essays were graded from A- to D+. The professors gave similar written feedback to the machine productions as to human writers.

A comprehensive study of state-of-the-art methods to determine if a piece of extended text is human-written or machine-generated concludes that "humans detect machine-generated texts at chance level" and that for AI-based detection "overall, the community needs to research and develop better solutions for mission-critical applications" (Uchendu et al., 2021). Students employ AI to write assignments. Teachers use AI to assess and review them (Lu & Cutumisu, 2021; Lagakis & Demetriadis, 2021). Nobody learns, nobody gains.

On the surface, our sample text appears to be a mediocre to good (though very short) student essay. It is correctly spelled, with good sentence construction. It begins with an appropriate claim and presents a coherent argument in support, backed up by evidence of a cited research study. The essay ends with a re-statement of the claim that learning styles are flexible and change with environment.

But look more closely and the paper falls apart. It references "Dunn, R., & Dunn, K. (1997). Learning styles: Research and practice. Journal of Research in Education, 7(2), 139-151." There is a journal named Research in Education, but no issue 7(2) in 1997. Dunn & Dunn did publish research on learning styles, but not in that journal. GPT-3 has fashioned a plausible-looking but fake reference. The program also appears to have invented the research study it cites. We can find no research study by Dunn and Dunn which claims that learning styles are flexible, not fixed.

To understand why a Transformer AI program should write plausible text, yet invent references and research studies, we turn to the seminal paper written by the developers of GPT-3. In a discussion of its limitations, the authors write: "large pretrained language models are not grounded in other domains of experience, such as video or real-world physical interaction, and thus lack a large amount of context about the world" (Brown, et al. 2020, p.34). Transformers are models of language not experiential knowledge. They are not designed to be scholarly - to check academic references and ensure that evidence is grounded in fact. In human terms, they are essentially inexperienced, unthinking and amoral. They have no ability to reflect on what they have written, to judge whether it is accurate and decent.

OpenAI has provided an add-on to GPT-3 that filters bad language. However, it is unlikely that the company will produce tools to check for accuracy. Its focus is on artificial general intelligence not education. Other companies could, in the future, provide tools to check generated references for accuracy or add genuine references to an article. But these would not overcome the fundamental limitation of Transformer language models such as GPT-3: that they have no internal inspectable model of how the world works to provide a basis for the system to reflect on the accuracy and scholarship of its generated work. Research is in progress to develop explainable neural AI (Gunning et al., 2019) and hybrid neural/symbolic AI systems (Garcez & Lamb, 2020) that might address this problem.

Could Any Good Come from Widespread Use of Such Technology?

Transformer AI systems belong to an alternative history of educational technology, where students have appropriated emerging devices - pocket calculators, mobile phones, machine translation software, and now AI essay generators - to make their lives easier. The response from teachers and institutions is a predictable sequence of ignore, resist, then belatedly accommodate.

It will be hard to ignore the growing number of students who submit assignments written by AI. Turnitin, the leading plagiarism checking company, admits that "we're already seeing the beginnings of the oncoming AI wave ... when students can push a button and the computer writes their paper" (Turnitin, 2020). As we have already indicated, resisting AI-generated assignments by deploying software to detect which ones are written by machine is likely to be a futile exercise. How, then, can we accommodate these new tools?

Teachers could restrict essay assignments to invigilated exams, but these are formal and time consuming. Alternatively, they could set reflective and contextualised written assignments that could not be generated by AI. For example, a teacher could set each student an independent research project, then ask for a written report on that specific project, give the student feedback on the report, then ask for the student to write a critical reflection on the feedback and issues raised by the project.

An imaginative way to incorporate AI-generated text into teaching could be for the teacher to employ Transformer AI to generate a set of alternative essays on a topic, then ask students to critique these and write their own better versions. Or set a complex question then ask each student to generate AI responses to the question and for the student to evaluate these responses in relation to the marking criteria. The student would then write an integrative essay drawing on the AI answers to address the original question.7

Transformer AI can be a tool for creative writing. For example, the student writes a first paragraph, the AI continues with the second paragraph, and so on. The AI writing partner helps maintain a flow of words and also takes the story in unexpected directions, to which the student must respond. Generating a few alternative continuations to a story may help a student writer see creative writing not as a linear progression, but an exploration of a space of possibilities.

AI assisted writing exercises could focus on skills of critical reading, accuracy, argumentation and structure. Assignments where AI is not allowed could be assessed for style, expression, voice and personal reflection.

Additionally, teachers could explore with students the ethics and limits of generative AI. How does it feel to interact with an expert wordsmith that has no intrinsic morals and no experience of the world? Is writing with AI tantamount to plagiarism (Fyfe, 2022).

What Does This Mean for the AIED Community?

Reviewers for IJAIED will not be able to avoid the challenge of assessing whether a submitted article has been written with the aid of an AI system. As an exercise, we generated an entire short research paper. First, we chose at random the title of a real paper published in IJAIED: "Domain-Specific Modeling Languages in Computer-Based Learning Environments: a Systematic Approach to Support Science Learning through Computational Modeling" (Hutchins et al., 2020). Then we used GPT-3 to generate three alternative Abstracts, just from the title. We chose a generated abstract for a "review paper". Then, we added to the abstract the heading "Introduction" and requested GPT-3 to generate the paper. We followed with a prompt of "Discussion" and then "References", which GPT-3 added in neat APA format. Finally, we presented GPT-3 with just the newly generated Abstract and requested it to generate a new title for the paper. It responded with: "Using Domain-Specific Modeling Languages to Support Science Learning: A Review of the Literature".

The result is an original 2,200 word "academic paper" produced in under five minutes. The output can be read at https://t.co/RTxogLRlJT. It will probably not pass a first Editor's review but is a harbinger of a flood of papers generated with the aid of AI. IJAIED will not be the only journal to receive papers produced wholly or partly by AI, but we are well placed to lead a debate on how to detect and deal with them.

A related issue is how to respond to genuine papers in the general area of AI Transformer systems in education. Should we publish papers on new tools to automate essay writing, or software to detect AI-generated essays? Where is the dividing line between promoting competition between AI generators and detectors, and enabling new forms of academic writing assisted by generative AI?

This could be a pivotal time for education, as students equip themselves with powerful new AI tools that substitute for what some perceive as the drudgery of assessment. These will not just write essays for students but answer complex questions and generate computer code (Bombazine et al., 2021). An education system that depends on summative written assessment to grade student abilities may have reached its apotheosis.

Every new educational technology arrives with affordances and limitations. AI Transformer technology is a powerful general-purpose language model that is already becoming embedded in education through chatbots, text summarisers, language translators, and now essay generators and tools for creative writing. The AIED community is well placed not only to debate the application of these systems to education, but to design new generative AI tools for writing, reasoning and conversation for learning.

Acknowledgements

I wish to thank Judy Kay for valuable comments on a draft of this article.

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Institute of Educational Technology, The Open University, Milton Keynes, UK

Mike Sharples

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Correspondence to Mike Sharples.

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Sharples, M. Automated Essay Writing: An AIED Opinion. Int J Artif Intell Educ 32, 1119-1126 (2022). https://doi.org/10.1007/s40593-022-00300-7
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Keywords

Transformer AI

Plagiarism

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Text generation

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Automated Essay Writing: An AIED Opinion - International Journal of Artificial Intelligence in Education

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This opinion piece emerged from research for the book, Story Machines: How Computers Have Become Creative Writers, by Mike Sharples and Rafael Pérez y Pérez, published by Routledge. While thinking of ways to promote the book, I realised that students could employ AI story generators to write essays.

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Discuss broader purpose of learning and academic integrity

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Discuss broader purpose of learning and academic integrity

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Develop with students shared understanding of purpose of programme/module, and how ChatGPT/AI can contribute to learning

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Whether students who use AI/ChatGPT will bypass learning, shortcutting the time required to think deeply, reflect and write to develop understanding. Or whether AI/ChatGPT can be integrated to support the learning process

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ChatGPT - Rethinking And Advancing Assessment Methods - March 2023

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Join this webinar for a discussion on the challenges presented by AI and the opportunity to tackle issues such as plagiarism with authentic assessment.

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Timnit Gebru discusses the misleading framings of artificial intelligence, her experience of getting fired by Google in a very public way, and why we need to avoid getting distracted by all the hype around ChatGPT and AI image tools.

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Timnit Gebru discusses the misleading framings of artificial intelligence, her experience of getting fired by Google in a very public way, and why we need to avoid getting distracted by all the hype around ChatGPT and AI image tools.

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Paris Marx is joined by Timnit Gebru to discuss the misleading framings of artificial intelligence, her experience of getting fired by Google in a very public way, and why we need to avoid getting distracted by all the hype around ChatGPT and AI image tools.

Timnit Gebru is the founder and executive director of the Distributed AI Research Institute and former co-lead of the Ethical AI research team at Google. You can follow her on Twitter at @timnitGebru.

Tech Won't Save Us offers a critical perspective on tech, its worldview, and wider society with the goal of inspiring people to demand better tech and a better world. Follow the podcast (@techwontsaveus) and host Paris Marx (@parismarx) on Twitter, and support the show on Patreon.

The podcast is produced by Eric Wickham and part of the Harbinger Media Network.

Also mentioned in this episode:
Please participate in our listener survey this month to give us a better idea of what you think of the show: https://forms.gle/xayiT7DQJn56p62x7Timnit wrote about the exploited labor behind AI tools and how effective altruism is pushing a harmful idea of AI ethics.Karen Hao broke down the details of the paper that got Timnit fired from Google.Emily Tucker wrote an article called "Artifice and Intelligence."In 2016, ProPublica published an article about technology being used to "predict" future criminals that was biased against black people.In 2015, Google Photos classified black women as "gorillas." In 2018, it still hadn't really been fixed.Artists have been protesting AI-generated images that train themselves on their work and threaten their livelihoods.OpenAI used Kenyan workers paid less than $2 an hour to try to make ChatGPT less toxic.Zachary Loeb described ELIZA in his article about Joseph Weizenbaum's work and legacy.Support the show

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don’t fall for the ai hype w/ timnit gebru

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‎Tech Won't Save Us: Don't Fall for the AI Hype w/ Timnit Gebru on Apple Podcasts

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Paris Marx is joined by Timnit Gebru to discuss the misleading framings of artificial intelligence, her experience of getting fired by Google in a very public way, and why we need to avoid getting distracted by all the hype around ChatGPT and AI image tools. Timnit Gebru is the founder and executiv...

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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

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The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.

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On the Dangers of Stochastic Parrots | Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

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The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size.

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Think piece for curriculum teams

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Innovative approach to ML online assessment needed!

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Innovative approach to ML online assessment needed!

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On Modern Languages degrees, we don't expect original thinking in assessment tasks, but accurate and effective use of the target language. I have no doubt that with the current assessment diet chatbots could graduate in Modern Languages in the UK with a 2:1:(

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‘There is no standard’: investigation finds AI algorithms objectify women’s bodies

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Guardian exclusive: AI tools rate photos of women as more sexually suggestive than those of men, especially if nipples, pregnant bellies or exercise is involved

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Focus more on process of learning (and failing) and not just the products of learning.

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Focus more on process of learning (and failing) and not just the products of learning.

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Controversial - what about lecturers using ChatGPT to create content? 

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Controversial - what about lecturers using ChatGPT to create content? 

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Do you think GPT or AI may replace teachers some day ?

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Where does the incentive to think for oneself go? It makes it so easy to just reproduce information without having to think about it and coast along through a course. 

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Where does the incentive to think for oneself go? It makes it so easy to just reproduce information without having to think about it and coast along through a course. 

# data[].attributes.headline
It's a calculator that sometimes provides apparently accurate answers.

# data[].attributes.subject
It's a calculator that sometimes provides apparently accurate answers.