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Dalle2 and Midjourney aren't great at handling text with images, yet....

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Generative AI in a Nutshell - how to survive and thrive in the age of AI

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40 Proven AI Prompts for Educators [THIRD EDITION]

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Read 40 Proven AI Prompts for Educators [THIRD EDITION] by Dan Fitzpatrick on Issuu and browse thousands of other publications on our platform. Sta...

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Use existing curriculum, or generate standards-aligned content with real, cited sources.

Choose a grade level and language, and watch Diffit create complete, differentiated resources. ✅

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Use existing curriculum, or generate standards-aligned content with real, cited sources. Choose a grade level and language, and watch Diffit create complete, differentiated resources. ✅

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Ethical Considerations for AI – Digital Education

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bit.ly/3P0TM27

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A Template Artificial Intelligence Policy For Your Organization.pdf

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Welcome to the AI Readiness Online Course

Do you want to understand how to use AI in your teaching, or leverage your data in your education or training business?

New module on Generative AI now available!

In Autumn 2022, OpenAI released ChatGPT, a generative AI tool. It's caused something of a stir. In this course, we'll look at what tools like ChatGPT can do and what their implications for education are. But we need to start by putting generative AI and tools like ChatGPT and Dall-E in their place, because AI is not new, and much of this generative AI is not that new either.

Take this course if you are an educator or working in an educational businesses. If you're a parent, student, researcher, or just curious about AI, you'll find it helpful too. Build your knowledge and skills so that you can work ethically and effectively with AI and data, and ensure that when it arrives on your doorstep, you see a familiar face.

Instructors ​

Your instructors are Professor Rose Luckin, EVR's Founder & CEO, and Rowland Wells, Creative Producer.

All the material in the online course has been adapted from the AI Readiness Framework in 'AI for School Teachers', published by Routledge, co-authored by Professor Rose Luckin, Professor Mutlu Cukurova, and Karine George, Headteacher. You can read about the authors here.

​

'AI for School Teachers' can be found here.

What is the AI Readiness Course about?

​

Professor Rose Luckin's AI Readiness Online Course is an 8-step programme to help familiarise you with AI, and teach you what it can do with data. Each step contains a set of modules to build up your knowledge and skills so that you may work ethically and effectively with AI and data.

Who is our course for?

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The AI Readiness Online C ourse is for educators and leaders in schools, and educational businesses, colleges and universities, and for parents, students, researchers, training organisations, entrepreneurs and EdTech developers, and anyone curious about AI and what it can do.

​

​

What is AI Readiness?

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AI Readiness is when you and your organisation are ready to leverage the transformational power of AI in your educational setting. There are 7 steps to get you to that point, and you can learn more about the framework in a series of webinars we've produced for educators and for educational businesses.

Can I take the course even if I don't know anything about AI?

​

The course is an introduction to AI and data and can be taken even if that is not your background. It doesn't get very technical, and you won't need to be working with AI or data currently in order to understand how they and the course could help you in the future .

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The AI Readiness Online Course | Professor Rose Luckin's EDUCATE Ventures Research | AI for Education

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AI is here, right now, but most of us know little about it. If you ask the average person, they probably have no idea how it affects their work, play, or learning. This course tells you what AI is, what it isn't, and what it can and cannot do, and why understanding human intelligence is key to understanding artificial intelligence.

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I shared on Twitter the other day that "When it comes to learning and teaching in an AI era, strong literacy skills have never been so important.". The question often arises though when you are unsure about what to do when you're not quite sure about how to do something, particularly around technology, and how to ensure that you, your school and most importantly, your students, are protected and know how and what to do with technology?

I've long championed the importance of a strong PLN and to that end, someone who has been a strong part of that network for well over a decade has been Laura Knight.

In recent conversations and through our work with schools, we both commented to each other that there is currently somewhat of a vacuum for schools regarding what to do about Artificial Intelligence (AI). W ith many schools not even having a digital strategy ( check here if you're looking to create one in the guide to creating a digital strategy I co-authored with Al Kingsley), let alone a specific policy for AI, Laura and I thought we would have a go at creating a template that schools could use to help them start to shape their policies around the use of AI in their school.

As you'll see from the template, there's plenty of scope and space for you to include things relating to your context and we've included an Appendix where we've shared some of our related recommendations and assumptions too.

In the footnote of each page of the policy, you'll also notice the Creative Commons licence we've put onto the document. For clarity, the licence means that you can:

You cannot however

Finally, any use you make of it or adaptations you make to it, must still credit us and must also carry the same Creative Commons licence. For more information on that licence, please visit this link .

Please access the policy template here . Feedback is welcomed. We'd also love to hear stories about how it has helped you at all.

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Free resource: Use of Artificial Intelligence (AI) in Education school policy template

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I shared on Twitter the other day that "When it comes to learning and teaching in an AI era, strong literacy skills have never been so important.". The question often arises though when you are uns...

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Machine Learning & Human Intelligence eBook, by Professor Rose Luckin

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Machine Learning & Human Intelligence eBook, by Professor Rose Luckin

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The future of education for the 21st Century, available for free in the EVR Resource Bank at the link above.

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​

Looking for something in AI for education? EDUCATE Ventures Research 's expert AI and data scientists, researchers, EdTech mentors and academics have produced a set of quality resources for educators, business founders and developers, and the AI-curious. Browse our store to find eBooks on machine learning, AI guides for teachers and heads, and our AI Readiness Online Course with its new module on using Generative AI for the classroom.

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Resources | Professor Rose Luckin's EDUCATE Ventures Research (EVR) | AI & EdTech for Education

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The EDUCATE Ventures Research (EVR) resource store: where teachers, trainers, learners, leaders, workers and the AI-curious come to find out more about the ethical application of technology in AI and EdTech.

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The Skinny on AI for Education Newsletter

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The Skinny on AI for Education Newsletter

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Discover the latest insights at the intersection of AI and education from Professor Rose Luckin and the EVR Team. From personalized learning to smart classrooms, we decode AI's impact on education. We analyse the news, track developments in AI technology, watch what is happening with regulation and policy and discuss what all of it means for Education. Stay informed, navigate responsibly, and shape the future of learning with The Skinny

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THE SKINNY
on AI for Education

Welcome to The Skinny on AI for Education newsletter.

Discover the latest insights at the intersection of AI and education from Professor Rose Luckin and the EVR Team. From personalized learning to smart classrooms, we decode AI's impact on education. We analyse the news, track developments in AI technology, watch what is happening with regulation and policy and discuss what all of it means for Education. Stay informed, navigate responsibly, and shape the future of learning with The Skinny.

Latest Issue:

Issue #4: Will the current era of AI hype be AI's transformative moment? Where can you get clarity on next steps? Recognise that you are not alone, and can leverage your own remarkable human intelligence to distinguish between the immediate, tactical actions needed to address current AI challenges, and the strategic responses required to position yourself for a future where every moment isn't an AI-driven crisis​

Past Issues:

Issue #3: Get to grips with AI in education and training, find out what kind of innovations different EdTech companies are implementing with AI, read about Microsoft and OpenAI's latest developments and get the skinny on regulation, ethics and safety​

Issue #2: Discover why we must now futureproof our education systems for an AI world, what more AI is coming our way, including new developments in ChatGPT, and what the consensus is behind the real risks of AI for humanity​

Issue #1: Discover why ChatGPT matters so much to education, what battles and approaches exist for AI regulation, the debate over evidence and the value of AI expertise in the classroom​

Welcome to "The Skinny on AI for Education" newsletter, your go-to source for the latest insights, trends, and developments at the intersection of artificial intelligence (AI) and education. In today's rapidly evolving world, AI has emerged as a powerful tool with immense potential to revolutionize the field of education. From personalized learning experiences to advanced analytics, AI is reshaping the way we teach, learn, and engage with educational content.

In this newsletter, we aim to bring you a concise and informative overview of the applications, benefits, and challenges of AI in education. Whether you're an educator, administrator, student, or simply curious about the future of education, this newsletter will serve as your trusted companion, decoding the complexities of AI and its impact on learning environments.

Our team of experts will delve into a wide range of topics, including adaptive learning algorithms, virtual tutors, smart classrooms, AI-driven assessment tools, and more. We will explore how AI can empower educators to deliver personalized instruction, identify learning gaps, and provide targeted interventions to support every student's unique needs. Furthermore, we'll discuss the ethical considerations and potential pitfalls associated with integrating AI into educational systems, ensuring that we approach this transformative technology responsibly. We will strive to provide you with actionable insights that can be applied in real-world scenarios, empowering you to navigate the AI landscape with confidence and make informed decisions for the betterment of education.

As AI continues to evolve and reshape our world, it is crucial to stay informed and engaged. By subscribing to "The Skinny on AI for Education," you will become part of a vibrant community of educators, researchers, and enthusiasts dedicated to exploring the potential of AI and driving positive change in education.

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The Skinny on AI for Education | Professor Rose Luckin's EDUCATE | England

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Discover the latest insights at the intersection of AI and education. From personalized learning to smart classrooms, we decode AI's impact on education. We analyse the news, track developments in AI technology, watch what is happening with regulation and policy and discuss what all of it means for Education.

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5 training modules from Professor Rose Luckin and the EVR Team to get you up to speed on what generative AI tools like ChatGPT can do, and their implications for education: https://www.educateventures.com/airc-generative-ai-primer

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AI for School Teachers (Byte-Sized EdTech Research), by Professors Rose Luckin & Mutlu Cukurova, and Headteacher Karine George

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AI for School Teachers (Byte-Sized EdTech Research), by Professors Rose Luckin & Mutlu Cukurova, and Headteacher Karine George

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How will we help people learn how to learn and work ethically and effectively alongside smarter digital tools driven by AI? Public campaigns will surely play a part, but education: school-age education, national curriculums, and initiatives for lifelong learning, will be critical channels for engagement. Focusing on teachers, the front line, those with the toughest jobs, those preparing people to build and operate in our increasingly AI-supported societies, will be invaluable: https://www.educateventures.com/ai-for-school-teachers

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With all the talk in HE of AI and plagiarism, little attention has been given to teaching, learning or research. I have outlined an idea for an AI University, with a relentless focus on AI to optimise teaching and learning, to increase access and reduce costs.

As a corollary, let us look at 'research'. Can an AI University be a focus to optimise research? It has already happened over the last two decades. The real question is not whether AI can generate research, it is whether it can halt the paper mill it has become.

Future of research

Reports are tumbling out of institutions telling us that AI simply augments human effort. This is simplistic, so obviously defensive that it is in danger of turning into a pearl clutching meme.

AI has been accelerating research for decades

AI will automate many researc h tasks

AI will eliminate some human research

AI will create new research opportunities

AI will produce big wins, leapfrogging existing research efforts

With cheap intelligence-on-demand, AI has already sped up the process of research across a broad front and promises a lot more.

Wave 1: Existing impact

We have already seen this happen in the first wave, with search, citation search and access to Journals and knowledge at our fingertips, in seconds. The impact of AI on 'research' may be much greater than it is on teaching and learning. It has gone under the radar but it the impact has already been astonishing. Google Search and Google Scholar accelerated research, as the whole business of access to Journals, knowledge and ready to use citations, slashed time to completion. I remember waking mile after mile along library shelves to find Journals and books, hour and hours labouring over desk research and citations (the bane of any researcher back then), even the typing of dissertations and theses with zero cut and paste! All have been accelerated or automated by AI. One could argue that every PhD period should have been slashed by several months!

Wave 2: Automating tasks
A second wave is now hitting research, as tasks are being augmented and automated, with a slew of research papers showing that this is not only possible by happening.
Let's look at tasks the literature has already identified as being possible, productively, by AI:

*Abstracts

*Introductions

*Literature reviews

*Research gaps

*Hypotheses

*Scoring/Open text surveys

*Research plans

*Data analysis

*New concepts

*Idea generation

*Full paper outlines

*Full papers

*Faster peer review

Wave 3: Optimise research

A third wave may emerge where research is being eliminated on scale, especially text based speculation, meta studies and data-driven research. There is every reason to believe that AI will create new research opportunities and eliminate entire areas of current research endeavour. One would also hope that is speeds up peer critiques and reviews.

One would hope that, rather than making the current paper mill go faster, we can optimise research by eliminating bad or weak research. The system is in need of a rest on quality and quantity. Far too much research is being published and not used or even read. AI could help solve this problem.

Wave 4: Big wins

A fourth wave will leapfrog research to produce enormous wins. This has already happened and will accelerate. In some specific areas the impact of AI has leapfrogged the existing paradigm.

*Alphafold: 1 billion years of research saved

*MIT: Super-antibiotic Halicin discovered

*Materials discovery of millions of new structures by Deepmind

Demis Hassabis says, one protein was taking 4-5 years to identify its 3D structure. We now know all 200 million, saving 1 billion YEARS of research. Read that again - it is an astonishing figure. This was only the start as we've also seen a similar breakthrough by Google on materials science, and a groundbreaking drug discovery by MIT.

Do we think this will stop? I don't think so.

AI University and research

One radical idea is to break the link between research and teaching, to recognise that this is part of the problem, the old Humboldtian idea that researchers make good teachers and that it is a necessary condition for success. I have argued that breaking this old Prussian bond could be a good thing, on the basis of research showing that researchers as teachers often hinder rather than help teaching in Universities.

What is needed is a University that grabs the bull by the horns and becomes a centre of research itself, or how research can be accelerated to release the bounty that AI offers or engages in research that uses AAI to accelerate progress. Research is, after all, a means to an end, not an end in itself. There is always a purpose or goal, even in blue sky physics, we have a general understanding that understanding the nature of reality has benefits for us as a species and often lays the ground work for new research.

There has to be a recognition that we are returning to a successful model that emerged in the Post-War period, with some notable examples before, (Bell, Eddison, Xerox, Tesla etc) , where large tech companies are doing their own research and leaping ahead of academia. This is not lone wolf activity, as they rely on academic output in terms of talent. However, their speed, ability to take risks, access to data, ability to deliver on scale and competitive mindset means that things happen quicker with more substantial breakthroughs.

Post-war success in the US in particular was engineered by Vannevar bush to co-ordinate academia, Government and business, paying to the strengths of all three. This is what ended the war with the Manhattan project and got them to the moon. All three need an element of humility to co-operate and make human advances to solve big ticket problems, such as climate change and the energy crisis.

It is no accident that almost all the breakthroughs are taking pace in the US, perhaps the Anglosphere of one includes Deepmind. There seems to be something in this idea that the optimal way forward is collaboration between state, academia and commerce, rather than anti-corporate, anti-government or anti-academic sentiment being allowed to hinder progress, as that is clearly sub-optimal.

Bibliography

Aydın Ö., Karaarslan E. OpenAI ChatGPT generated literature review: Digital twin in healthcare (2022)

Chen Y., Eger S. Generating (humourous) titles from scientific abstracts end-to-end (2022)

Wenzlaff K., Spaeth S. Smarter than humans? (2022)

Dowling M ChatGPT for (Finance) research: The Bananarama Conjecture (2023)

Zhai X. ChatGPT user experience: Implications for education (2022)

Adesso G. GPT4: The ultimate brain. Authorea Preprints (2022)

Valavanidis, A., SCIENTIFIC REVIEWS Artificial Intelligence Application withMachine-learning Algorithm Identified a Powerful Broad-Spectrum Antibiotic.

Merchant, A., Batzner, S., Schoenholz, S.S., Aykol, M., Cheon, G. and Cubuk, E.D., 2023. Scaling deep learning for materials discovery. Nature, pp.1-6.

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Free resources so you can leverage AI in education with effective strategies, polices and ethical guidelines. Helping you create the future today in your school. AI for Education is yours to take and run with.

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AI Unplugged: Dan Fitzpatrick Explains All (TIC S2EO1)

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The presentation will include a demo of the latest iteration of Bolton College's FirstPass platform.

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Please spend a moment feeding back to us...Thank you for coming.

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Please spend a moment feeding back to us...Thank you for coming.

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Check out this Presentation designed by Tim Cook.

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We understand that AI is bringing lots of change for leaders.

We also understand that change doesn't happen in a day.

We want to offer a 5-month programme, 2hrs per month, to support education leaders, and we'd love your input to help us shape it, in return for a discounted place on the first cohorts. Please drop by this very short survey:

https://docs.google.com/forms/d/e/1FAIpQLSdMtiLoi98QF8tF32xHB9U8dw-Gar3IkRkE70JW0ppDsw_TvQ/viewform?usp=sf_link

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AI in your School Community workshop output: Know; Feel; Do

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AI in your School Community workshop output: Know; Feel; Do

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Centaurs and Cyborgs on the jagged frontier - Blog on the future of work

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Centaurs and Cyborgs on the jagged frontier - Blog on the future of work

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I promised to share this link during my talk, well worth a read...

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A lot of people have been asking if AI is really a big deal for the future of work. We have a new paper that strongly suggests the answer is YES.

For the last several months, I been part of a team of social scientists working with Boston Consulting Group, turning their offices into the largest pre-registered experiment on the future of professional work in our AI-haunted age. Our first working paper is out today. There is a ton of important and useful nuance in the paper but let me tell you the headline first: for 18 different tasks selected to be realistic samples of the kinds of work done at an elite consulting company, consultants using ChatGPT-4 outperformed those who did not, by a lot. On every dimension. Every way we measured performance.

Consultants using AI finished 12.2% more tasks on average, completed tasks 25.1% more quickly, and produced 40% higher quality results than those without. Those are some very big impacts. Now, let's add in the nuance.

First, it is important to know that this effort was multidisciplinary, involving multiple types of experiments and hundreds of interviews, conducted by a great team, including the Harvard social scientists Fabrizio Dell'Acqua, Edward McFowland III, and Karim Lakhani; Hila Lifshitz-Assaf from Warwick Business School and Katherine Kellogg of MIT (plus myself). Saran Rajendran, Lisa Krayer, and François Candelon ran the experiment on the BCG side, using a full 7% of its consulting force (758 consultants). They all did a lot of very careful work that goes far, far beyond the post. So, please look at the paper to make sure you get all the details - especially if you have questions about numbers or methods. I need to simplify a lot to fit 58 pages of findings into a post, and any mistakes are mine, not my co-authors. Also, while we pre-registered these experiments, this is still a new working paper, so there might be errors or mistakes, and the paper is not yet peer-reviewed. With that in mind, let's get to the details...

AI is weird. No one actually knows the full range of capabilities of the most advanced Large Language Models, like GPT-4. No one really knows the best ways to use them, or the conditions under which they fail. There is no instruction manual. On some tasks AI is immensely powerful, and on others it fails completely or subtly. And, unless you use AI a lot, you won't know which is which.

The result is what we call the "Jagged Frontier" of AI. Imagine a fortress wall, with some towers and battlements jutting out into the countryside, while others fold back towards the center of the castle. That wall is the capability of AI, and the further from the center, the harder the task. Everything inside the wall can be done by the AI, everything outside is hard for the AI to do. The problem is that the wall is invisible, so some tasks that might logically seem to be the same distance away from the center, and therefore equally difficult - say, writing a sonnet and an exactly 50 word poem - are actually on different sides of the wall. The AI is great at the sonnet, but, because of how it conceptualizes the world in tokens, rather than words, it consistently produces poems of more or less than 50 words. Similarly, some unexpected tasks ( like idea generation) are easy for AIs while other tasks that seem to be easy for machines to do (like basic math) are challenges for LLMs.

I asked the ChatGPT with Code Interpreter to visualize this for you:

To test the true impact of AI on knowledge work, we took hundreds of consultants and randomized whether they were allowed to use AI. We gave those who were allowed to use AI access to GPT-4, the same model everyone in 169 countries can access for free with Bing, or by paying $20 a month to OpenAI. No special fine-tuning or prompting, just GPT-4 through the API.

We then did a lot of pre-testing and surveying to establish baselines, and asked consultants to do a wide variety of work for a fictional shoe company, work that the BCG team had selected to accurately represent what consultants do. There were creative tasks ("Propose at least 10 ideas for a new shoe targeting an underserved market or sport."), analytical tasks ("Segment the footwear industry market based on users."), writing and marketing tasks ("Draft a press release marketing copy for your product."), and persuasiveness tasks ("Pen an inspirational memo to employees detailing why your product would outshine competitors."). We even checked with a shoe company executive to ensure that this work was realistic - they were. And, knowing AI, these are tasks that we might expect to be inside the frontier.

In line with our theories, and as we have discussed, we found that the consultants with AI access did significantly better, whether we briefly introduced them to AI first (the "overview" group in the diagram) or did not. This was true for every measurement, whether the time it took to complete tasks, the number of tasks completed overall (we gave them an overall time limit) or the quality of the outputs. We rated that quality using both human and AI graders, who agreed with each other (itself an interesting finding).

We also found something else interesting, an effect that is increasingly apparent in other studies of AI: it works as a skill leveler. The consultants who scored the worst when we assessed them at the start of the experiment had the biggest jump in their performance, 43%, when they got to use AI. The top consultants still got a boost, but less of one. Looking at these results, I do not think enough people are considering what it means when a technology raises all workers to the top tiers of performance. It may be like how it used to matter whether miners were good or bad at digging through rock... until the steam shovel was invented and now differences in digging ability do not matter anymore. AI is not quite at that level of change, but skill levelling is going to have a big impact.

But there is more to the story. BCG designed one more task, this one carefully selected to ensure that the AI couldn't come to a correct answer. This wasn't easy. As we say in the paper "since AI proved surprisingly capable, it was difficult to design a task in this experiment outside the AI's frontier where humans with high human capital doing their job would consistently outperform AI." But we identified a task that used the blind spots of AI to ensure it would give a wrong, but convincing, answer to a problem that humans would be able to solve. Indeed, human consultants got the problem right 84% of the time without AI help, but when consultants used the AI, they did worse - only getting it right 60-70% of the time. What happened?

In a different paper than the one we worked on together, Fabrizio Dell'Acqua shows why relying too much on AI can backfire. In an experiment, he found that recruiters who used high-quality AI became lazy, careless, and less skilled in their own judgment. They missed out on some brilliant applicants and made worse decisions than recruiters who used low-quality AI or no AI at all. When the AI is very good, humans have no reason to work hard and pay attention. They let the AI take over, instead of using it as a tool. He called this "falling asleep at the wheel", and it can hurt human learning, skill development, and productivity.

In our experiment, we also found that the consultants fell asleep at the wheel - those using AI actually had less accurate answers than those who were not allowed to use AI (but they still did a better job writing up the results than consultants who did not use AI). The authoritativeness of AI can be deceptive if you don't know where the frontier lies.

But a lot of consultants did get both inside and outside the frontier tasks right, gaining the benefits of AI without the disadvantages. The key seemed to be following one of two approaches: becoming a Centaur or becoming a Cyborg. Fortunately, this does not involve any actual grafting of electronic gizmos to your body or getting cursed to turn into the half-human/half-horse of Greek myth. They are rather two approaches to navigating the jagged frontier of AI that integrates the work of person and machine.

Centaur work has a clear line between person and machine, like the clear line between the human torso and horse body of the mythical centaur. Centaurs have a strategic division of labor, switching between AI and human tasks, allocating responsibilities based on the strengths and capabilities of each entity. When I am doing an analysis with the help of AI, I often approach it as a Centaur. I will decide on what statistical techniques to do, but then let the AI handle producing graphs. In our study at BCG, centaurs would do the work they were strongest at themselves, and then hand off tasks inside the jagged frontier to the AI.

On the other hand, Cyborgs blend machine and person, integrating the two deeply. Cyborgs don't just delegate tasks; they intertwine their efforts with AI, moving back and forth over the jagged frontier. Bits of tasks get handed to the AI, such as initiating a sentence for the AI to complete, so that Cyborgs find themselves working in tandem with the AI. This is how I suggest approaching using AI for writing, for example. It is also how I generated two of the illustrations in the paper (the Jagged Frontier image and the 54 line graph, both of which were built by ChatGPT, with my initial direction and guidance)

Our paper, along with a stream of excellent work by other scholars, suggests that, regardless of the philosophic and technical debates over the nature and future of AI, it is already a powerful disrupter to how we actually work. And this is not a hyped new technology that will change the world in five years, or that requires a lot of investment and the resources of huge companies - it is here, NOW. The tools the elite consultants used to supercharge their work are the exact same as the ones available to everyone reading this post. And the tools the consultants used will soon be much worse than what is available to you. Because the technological frontier is not just jagged, it is expanding. I am very confident that in the next year, at least two companies will release models more powerful than GPT-4. The Jagged Frontier advances, and we have to be ready for that.

Even aside from any anxiety that statement might cause, it is also worth noting the other downsides of AI. People really can go on autopilot when using AI, falling asleep at the wheel and failing to notice AI mistakes. And, like other research, we also found that AI outputs, while of higher quality than that of humans, were also a bit homogenous and same-y in aggregate. Which is why Cyborgs and Centaurs are important - they allow humans to work with AI to produce more varied, more correct, and better results than either humans or AI can do alone. And becoming one is not hard. Just use AI enough for work tasks and you will start to see the shape of the jagged frontier, and start to understand where AI is scarily good... and where it falls short.

In my mind, the question is no longer about whether AI is going to reshape work, but what we want that to mean. We get to make choices about how we want to use AI help to make work more productive, interesting, and meaningful. But we have to make those choices soon, so that we can begin to actively use AI in ethical and valuable ways, as Cyborgs and Centaurs, rather than merely reacting to technological change. Meanwhile, the Jagged Frontier advances.

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Centaurs and Cyborgs on the Jagged Frontier

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I think we have an answer on whether AIs will reshape work....

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JISC-  A Framework for Digital Transformation

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JISC-  A Framework for Digital Transformation

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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. This blog post will be updated as needed, and we have also produced a version as a more formal report.
Version 1.3 - 2nd Jan 2024. First version published in April 2023.

Table of Contents
2. An Introduction to the Generative AI Technology 2.2. Microsoft Copilot 2.3. Google Bard 2.4. Other Models 2.5. A summary of key capabilities, limitations, and concerns around ChatGPT and other Large Language Models 2.6 Image Generation 2.7 Beyond Chatbots 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 pose 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 updated in January 2024, 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 GPTs, 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 January 2022. Users paying for the ChatGPT Plus service have access to a version that can access the internet, plus a later cut-off date of April 2023 for pre-trained information.

ChatGPT Plus customers also have access to GPTs which extend ChatGPT's functionality. This is replacing an earlier tool called 'Plugins', and allows any user to create a custom version of ChatGPT, focused on a particular task.

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's Copilot tools

Although ChatGPT has got most of the hype, there are other players in this space, and this number is likely to increase. Microsoft have several products, all based on OpenAI's technology, and variations of the name 'Copilot'

Of most interest to education at the moment is Microsoft Copilot (which was called Bing Chat), which is based on Open AI's GPT-4. It's a chat tool 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.

There is also an enterprise edition - this version is likely to be highly attractive to universities and colleges, as it ensures data and information included in the chats doesn't leak outside your institution.

Microsoft has also introduced Microsoft Copilot for 365 - a set of generative AI tools for Microsoft 365 (i.e. Word, Excel, PowerPoint etc). These are currently only available to institutions wishing to sign up a minimum of 300 users at $30 a month.

These tools, along with a similar set from Google, move generative AI from a separate concern to part of 'everyday AI' - tools our staff and students use every day.

2.3 Google's Bard

Bard is Google's equivalent of Microsoft Copilot, and is powered by Google's own AI models. Like Copilot, it can access the internet, but unlike Copilot, it doesn't provide references for the sites it used to give its answers, at least in its initial response. It does however have an interesting 'fact-checking' feature, where it will search the web for similar content to back up its answers and show you where it found the information.

2.4 Other models

There are quite a few other models under development - Claude and Llama being two of the more notable.

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

Meta's Llama is slightly different in that it has been made available as an open-source model, meaning that you can run it yourself. Open-source AI models often differ from open-source software though, and it's not possible to fully understand how the Llama model works, or modify it yourself, from this release.

2.5. 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 January 2022 (but those with the paid ChatGPT Plus service have access to a version that can access the internet, and has a slightly later training date)

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

2.6 Image Generation

It's not all about text - image generation tools have made huge progress too, particularly with Midjourney, DALL-E 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 Microsoft Copilot, for example, can also generate images, using OpenAI's DALL-E.

2.7 Beyond Chatbots

Whilst the focus has been on chatbots and image generation, generative AI is finding 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. One of the first outputs of this work - Assessment Menu: Designing assessment in an AI enabled world - is now available.

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 guidance to students being published by many institutions. 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.

We will shortly be publishing example guidance, particularly for colleges.

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 uses 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, and another gives our latest recommendations.

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 produced a report on the findings of student panels sponsored and hosted by the University of Manchester.

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 who pay for ChatGPT Plus get faster, more reliable access, as well as access to the latest model, and those who 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.

The Department for Education has recently published the results of a call for evidence on the use of generative AI in education. which shows ways in which respondents are already benefitting from generative AI, alongside their concerns.

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 EU AI act classifies the use of AI in education as high risk, putting additional responsibilities on providers. 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 aims to give a broad introduction to generative AI. Much more has been written on the topic, and for those who 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)

Generative AI in education Call for Evidence: summary of responses Department For Education (Noc 2023)

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

Reconsidering assessment for the ChatGPT era - QAA (Jul 2023)

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

Guidance for AI in Education and Research - Unesco (Sep 2023)

Artificial Intelligence (AI) Use in Assessments: Protecting the Integrity of Qualifications - JCQ (March 2023)
Selected relevant Jisc Blog Posts:
Staff and student use:

Generative AI: Lifeline for students or threat to traditional assessment? (April 2023)

Means. Motive, Opportunity: A Composite Narrative about Academic Misconduct (March 2023)

Generative AI and How Students Are Using It (June 2023)

Institutional Policy

Considerations on wording when creating advice or policy on AI use - (Feb 2023)

Navigating Terms and Conditions of Generative AI (Sept 2023)

Licensing Options for Generative AI (Dec 2023)

AI Detection:

AI Detection - Latest Recommendations (Sept 2023)

A short experiment in defeating a ChatGPT detector (Jan 2023)

Bias and other ethical considerations:

Hidden Workers powering AI (March 2023)

Exploring the potential for bias in ChatGPT (Jan 2023)

Using Generative AI:

Getting started with ChatGPT Part 1 - Understanding ChatGPT - (March 2023)

How to Explore AI Image Generation (Nov 2022)

Debunking Myths and Harnessing Solutions: Navigating generative AI in Education (Dec 2023)

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

V1.2 - 16 Oct 2023

References to DALLE-2 changes to just DALL-E with the release of DALL-E 3.

Section 2.2 updated to reference Llama and Bing Chat Enterprise.

Section 2.2 Reference to Claude and Bard being behind ChatGPT removed as they have progressed signficantly.

Section 3 added linked to Assessment Menu

Section 3.1.2 - added link to latest Jisc AI detection blog.

Section 3.2.1 - added link to report on student forums.

Section 4 - updated to reflect AIEd classed as high risk in EU Act.

Further reading - added recent documents from QAA and Unesco.

Blogs list - date of articles added

V1.3 - 2 Jan 2024

References to Bing Chat changed to Microsoft Copilot as Micrsoft have renamed the product.

Section 2.1 - Replaced text on ChatGPT plugins with text on GPTs.

Section 2.2 Merged all content about Copilot into its own section to reflect Microsoft's latest branding

Section 2.2 Updated content on Microsoft Copilot to reflect pricing of Copilot 365.

Section 2.3 Moved Google Bard into its own section 2.3 as it continues to evolve. Updated the screenshot and text to show the fact-checking feature.

Section 2.4 Moved Claude and Llama into a section '2.4 Other Models' - this was previously section 2.2.

Section 3.2.2 Added reference to DfE call for evidence report.

Further reading - added recent document DfE

Blogs list updated

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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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AI webinar session 2 - How AI will shape education now & in the future, with Rose Luckin

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It was billed as a "celebration of chocolate in all its delightful forms" but ended up a tragic tale worthy of an Oompa-Loompa song.

Police were called to a venue in Glasgow last weekend after furious families who had spent hundreds of pounds on the Willy's Chocolate Experience complained about the "awful" event that left children in tears and was abruptly cancelled midway through.

The event organiser, House of Illuminati, which charged up to £35 for tickets, promised an "immersive experince" based on the Warner Bros film Wonka, which stars Timothée Chalamet as the young chocolate entrepreneur and was an instant hit with children and grownups over the festive period.

The event publicity promised giant mushrooms, candy canes and chocolate fountains, along with special audio and visual effects, all narrated by dancing Oompa-Loompas - the tiny, orange men who power Wonka's chocolate factory in the Roald Dahl book which inspired the prequel film.

But instead, when eager families turned up to the address in Whiteinch, an industrial area of Glasgow, they discovered a sparsely decorated warehouse with a scattering of plastic props, a small bouncy castle and some backdrops pinned against the walls.

After an immediate and angry response from early arrivals who demanded their money back, organisers called a halt to the event only hours after opening, but failed to inform later attenders, some of whom had travelled substantial distances and are now demanding their train costs be refunded too.

AFacebook group has been set up by furious families, who described their children crying with disappointment at the event which was scheduled to run on Saturday and Sunday and lambasted the event as a "farce" and the organisers as "cowboys".

Police Scotland confirmed that officers had been called to the venue after complaints.

Paul Connell, an actor who had been hired by the company to perform at the event, told STV News of his shock when he arrived at the warehouse and realised that the rubric he had been given was impossible to fulfil because none of the promised props or special effects were there.

"My heart sank looking around ... I just felt sad because I was aware of how many kids were going to be coming through.

"We were told to hand the kids a couple of jelly beans and a quarter cup of lemonade at the end."

Connell said that when he asked the organisers what he should do with the script he was told to improvise.

In a statement, House of Illuminati apologised to customers for the "very stressful and frustrating day".

"Unfortunately, last minute we were let down in many areas of our event and tried our best to continue on and push through and now realise we probably should have cancelled first thing this morning instead," it said.

The organisers confirmed that full refunds would be given to customers, which could take up to 10 days.

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Glasgow Willy Wonka experience called a 'farce' as tickets refunded

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Event billed as immersive 'celebration of chocolate' cancelled after children left in tears at sparsely decorated warehouse

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Teachers are using AI to grade essays. Students are using AI to write them | CNN Business

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teaching ChatGPT best practices in her writing workshop class at the University of Lynchburg in Virginia, said she sees the advantages for teachers using AI tools but takes issue with how it can be used to create feedback for students.

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When you live in a research/social media bubble like I do, it's easy to take certain things for granted. For example, I always overestimate the number of people who are using generative AI regularly in their day to day work.

The reality, as of April 2024, is the majority of people within and outside of education haven't had the time, the interest, or the inclination to use much generative AI beyond free tools like the unpaid version of ChatGPT.

Something else I take for granted is the fact that AI detection tools do not work. Since the release of ChatGPT in November 2022, universities have been confronted with a number of products for detecting generative artificial intelligence. Largely, these tools have been born out of fear that large language model based technologies like ChatGPT will be used by students to cheat on assessment tasks.

It's an understandable and entirely valid concern, especially given statistics on how many students engage in academic misconduct (and the fact that considering many of these studies are self reported, means those percentages are probably much higher). But companies developing generative AI detection tools often prey on education providers in a way which is predatory and largely driven by commercial and not academic interest.

There are already dozens of AI detection tools on the market. To avoid giving any of them any free publicity, I'm not going to mention any of them directly in this article. Suffice it to say that since I started working with generative artificial intelligence two years ago, I have yet to see a detection tool that is reliable or accurate.

Outside of my bubble, where I have easy access to novel research, and the ability and inclination to test these tools myself, many education providers are still in the dark when it comes to detection tools and they can be lulled into a false sense of security by the companies selling them.

In this post, I'll discuss some of my personal objections to AI detection tools, and explore a new piece of research that once again proves AI detection tools don't work.

How Do AI Detection Tools Work?

Unlike traditional plagiarism checkers which compare texts to a large database of existing text (and don't get me started on the amount of students' intellectual property being hoarded by these companies for profits), AI detection tools use pattern matching to identify generated text.

Language models operate by processing huge amounts of text data and learning probabilistic rules about how language works. They then use these rules to create novel text.

However, language models often have tells which can be more predictable than human writing. For example:

Lack of variation in sentence structure

Overuse of certain words such as conjunctions (e.g. "however", "furthermore", "in addition", "in conclusion")

Overuse of particular vocabulary (e.g. "delves", "navigates complexities")

Predictable sentence length, paragraph length

Predictable grammatical constructions

Detection tools work on pattern matching these features and also, in some cases, use traditional plagiarism detection methods to look for text which may be recreated verbatim from a language model's training data set.

Why It Doesn't Work

Although AI detection tools can successfully identify some generated content, there are several points at which the tools break down, making them unsuitable as an academic integrity checkpoint.

First of all, large language models continue to develop at an incredibly rapid pace. A powerful model like Claude 3 Opus from Anthropic produces much more varied and less predictable text than GPT-4, which itself provides more sophisticated text than the free version of ChatGPT or other models which are more limited in capacity, such as the free version of Google Gemini or Microsoft Copilot when it is using GPT-3.5.

This means that using a more powerful model reduces the efficacy of detection tools until the detection tools are tweaked and improved based on the new model. Essentially it's an arms race between generation and detection, and one which, given the resources of developers like Microsoft, Google, and OpenAI, detection tool companies cannot hope to win.

It's also easy to circumvent or break detection tools using adversarial techniques. These are deliberate prompting tactics designed to work around the detection tools. Some examples of adversarial techniques include:

Instructions in the prompt to vary sentence structure

Instructions to incorporate deliberate errors and make the outputs more human-like

Ping-ponging from one model to another, laundering the outputs

Using more powerful models when the limitations of a specific tool are known

Creating system prompts designed to circumvent as many points of detection/prediction as possible

AI Detection as an Equity Issue

Having explored a few of the reasons why AI detection tools can fail, it's important to now consider why they shouldn't be used at all as a point in an academic integrity conversation.

To do so, I'm going to illustrate the point using a scenario. Imagine four students complete the same assessment task. The conditions of the assessment task specify that no generative artificial intelligence tools may be used. Detection tools will be employed as an integrity measure after submission. The students must complete this assessment task in their own time, outside of the school/university.

Ashley is a regional student with limited access to digital technologies at home. They are therefore reliant on their institution's computers and network. The institution has blocked direct access to generative AI tools.

Ashley checks GPT, Gemini, and Co-pilot, but since they're blocked ends up having to use the free credits of a third party application built on top of GPT-3.5. They're also limited to completing this task during the time they have on campus at lunchtime or immediately after classes before returning home.

Bob is an English as an additional language (EAL) student from a migrant family where English is not spoken in the home. Bob uses the free version of ChatGPT because he has heard from fellow students that it is a good translation tool. He uses ChatGPT to translate both the assignment questions and his answers.

Alice comes from a low socio-economic background with low levels of literacy in the home and limited digital literacy. Alice uses Microsoft Copilot at home on her phone as a way to understand the requirements of the task and to help make her ideas seem more academically written.

Marie is an English first language speaker from a wealthy household. Her mother is a software engineer and her father is an intellectual property lawyer. Marie writes her response using her father's access to Claude (Opus), requiring a $20 a month USD subscription. She inputs the assignment questions and generates her entire response verbatim.

Just for good measure, and because she knows how these tools work, she pastes the response into GPT-4 (another subscription-based model) and then back again into Claude with the instruction to make it a little bit more sophisticated, a little bit more varied, and to incorporate some direct quotes from the materials from class that she uploads as a PDF (a capability only available in paid models). Marie's final response is comprehensive, accurate, and sophisticated. It is also entirely fabricated by GenAI.

The four students submit their work independently. The detection tool flags:

Ashley's work as 90% AI-generated

Bob's as 100% AI-generated

Alice's as 85% AI-generated

Marie's as 20% AI-generated

Of the students, you could argue that Bob and Alice attempted to use generative AI as an assistive technology to help understand the task and to form their answers. Alice's use was perhaps a little bit more heavy-handed. And all four students have certainly breached the requirements of the task by using generative AI in the first place.

The fact is, the student who used the generative AI tools with the most deliberate, nefarious intent was Marie, who was also the least likely to get caught. Marie is the student who was already advantaged by the education system, advantaged by her socio-economic status, and now advantaged by a heavy-handed approach to policing the technology.

This is the equity issue of generative AI detection:

GenAI detection tools privilege students who are English first language, have access to paid large language models/applications, and are more digitally literate.

AI Detection is a Workload Issue

Now let's shift our attention to look at an issue which is close to my heart. In 2016, I completed my Master's in Education which culminated in an action research project exploring how professional learning can mitigate the risk of teacher burnout. During that research, it became very clear that the factors contributing to teacher burnout are many and varied. Amongst those factors is the workload imposed by assessment and reporting practices.

In both K-12 and higher education, assessment is big business, and at the end of most assessment work, educators spend hours marking, moderating, and reporting. Assessment is an important but time-consuming part of the job.

Checking for and monitoring cases of academic misconduct is unfortunately part of this task. In many senior secondary and higher education institutions, this includes processes such as automatic plagiarism checking, and the responsibility generally falls to the teacher or lecturer in charge of the class.

Typically, the process goes something like this: For assessment tasks that are completed outside of examination conditions, in electronic format, students are required to submit their work through a plagiarism detection platform, often built into the learning management system (LMS). Either students upload to this platform directly or their teachers upload a collection of assignments in bulk.

The assessments are processed by the plagiarism checking system and reports are generated. Having used these tools myself for senior secondary English and for undergraduate teacher training courses, I can attest that whilst they're not hugely time consuming, this process does add a layer on to the assessment and reporting process. If a student's work is reported beyond a particular threshold (say, 20% to allow for genuine quotes and citations), then the assessor has to go in, manually identify the areas which have been flagged as plagiarism, and then report back to the student. In extreme cases of plagiarism, this will then kick along to whatever the next stage of the institution's academic integrity policies are, for example resubmission, zeros, and so on.

Whilst this is a brief imposition on the educators, the use of similar approaches with generative AI is much more burdensome on educators. This is because, unlike plagiarism tools, generative AI tools do not give a clear cut result. The percentage likelihood of AI generated content is less accurate than plagiarism detection, more open to interpretation, and therefore requires more consideration on the educator's part. It requires more nuanced and potentially more stressful conversations between the educator and the student, and the potential for much more kickback from the students and many more appeals. In many contexts, both students and parents are aware that detection tools are not as accurate as plagiarism tools.

The added time and stress of using generative AI detection tools is a burden on educators who are already in an industry with a high risk of burnout and attrition.

New Research

Last year and early this year, I had the privilege of working on papers on an AI assessment scale with Dr. Mike Perkins, Dr. Jasper Roe and Associate Professor Jason MacVaugh. I've detailed the AI Assessment Scale elsewhere and you're welcome to download a free ebook of activities aligned to the scales which allow for generative assessment.

Mike and Jasper, along with other authors, have just published a preprint of their latest research testing over 800 samples of writing against various detection tools. Mike shared the research on LinkedIn with this comment:

Our latest preprint shows the results of 805 tests of human samples, initial GenAI output, and GenAI output after we applied adversarial techniques designed to evade detection by AI text detectors. We saw a non manipulated mean accuracy rate of 39.5%, dropping to 22.1% after the application of the adversarial techniques The preprint can be found on arXiv here: https://arxiv.org/abs/2403.19148

The researchers also found concerning rates of false accusations (15%) where the tools incorrectly flagged human-written samples as AI-generated. At the same time, a high percentage of AI-generated texts went undetected, and a lower rate of false positives appears to come with an increased rate of undetected content. This points to major risks for both students being unfairly accused and dishonest usage of AI going unnoticed.

Interestingly, the outputs from different AI models had varying levels of detectability, with text from Google's Bard being the easiest to identify compared to GPT-4 and Anthropic's Claude. However, Bard-generated text also saw the biggest drop in detectability after applying adversarial techniques.

Several conclusions emerge, but alongside my other comments in this article the key is that not only are AI detection tools largely ineffective, they are also a short-term, ill-advised, and possibly unethical approach to academic integrity in light of generative AI. The current limitations of these tools underscore the need for a critical, nuanced approach if implementing them in higher education, and highlight the importance of exploring alternative AI-aware assessment strategies.

Over the next few months, I'll be writing extensively about approaches that K-12 and tertiary organisations can take to update their assessment strategies in ways which don't rely on ineffective technologies.

Over the last few years, I've worked with dozens of schools and universities and served on the boards of several not-for-profits, and have been involved in strategic planning, teaching and learning, assessment, and of course generative artificial intelligence.
If you'd like to discuss any of these areas with your organisation, please get in touch via the following form:

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When you live in a research/social media bubble like I do, it's easy to take certain things for granted. For example, I always overestimate the number of people who are using generative AI regularly in their day to day work. The reality, as of April 2024, is the majority of people within and outside of...

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Generative AI in academia: Friend or foe?

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Generative AI in academia: Friend or foe?

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Sam Illingworth, Dr

Associate Professor at Edinburgh Napier University

Dr Sam Illingworth is an associate professor at Edinburgh Napier University, whose research and practice revolve around the use of creative pedagogies to explore staff and student belonging in higher education. You can find out more about his work via his website www.samillingworth.com, and connect with him on X/Twitter @samillingworth.

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Generative AI in academia: Friend or foe?

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The launch of ChatGPT in November 2022 marked a potential turning point in higher education. As a prime example of generative AI, capable of creating content and solving problems in previously...

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Retrieval-Augmented Generation: what R.A.G can do

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Retrieval-Augmented Generation: what R.A.G can do

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Create your own R.A.G? https://huggingface.co/facebook/rag-token-nq

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For instant knowledge. Try it for youself.

Private ChatGPT

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Imagine you could chat with your organisation's entire digital knowledgebase, including MIS data, admission policies, HR policies, Departmental reports, marketing material, and curriculum information. Imagine how powerful that would be for you and your staff to ask complex questions and get instant answers. To save time getting AI to write emails, summarise complex information, generate reports, and make quick, better-informed decisions. All from AI solutions that don't hallucinate and are secure in a Private ChatGPT hosted in your Azure tenant. Now you don't have to imagine. You can try it for yourself with a free trial of a Private Chat GPT Assistant trained on your information. Find out more about the free trial here.

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See for yourself

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Disclaimer: All our Assistants are trained on publicly available information. AI tools can sometimes return incomplete or incorrect information. While we have done everything we can to resolve this problem, you should always check the reference sources and information for yourself.

One solution

Private ChatGPT enables you to query, search, enquire and analyse exactly the same as the publicly available versions, but without the security and privacy problems.

It is the basis for our highly effective productivity "Assistants". Assistants turn your extremely valuable information in old unstructured documents into new, organised, and instantly accessible intelligence. A process that currently takes significant time and effort can now be done cheaply and easily in minutes by anyone with the right access.

Assistants use Retrieval-Augmented Generation (RAG) AI so you can use natural language querying on your enterprise data. And becauses that data is the single source of truth, it tackles the issues of hallucination, and addresses all your security and privacy concerns.

Below are several example use cases that we've explored with other organisations.
You'll no doubt come up with your own.

We recognise that you're probably more bothered about what we can do to solve your problems and generate opportunities for you than you are about who we are, but just in case...

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About us

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Private ChatGPT AIs for SMEs and Public Sector

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Imagine you could chat with your organisation's entire digital knowledgebase, including MIS data, admission policies, HR policies, Departmental reports, marketing material, and curriculum information. Imagine how powerful that would be for you and your staff to ask complex questions and get instant answers.

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Employees with AI skills are in demand—97% of £100k+ IT jobs require AI skills, leading to a 21% pay premium. 66% of VPs won't hire senior candidates without generative AI knowledge. Khan Academy and Microsoft stress urgency in AI education. Ethical and transparent AI development is crucial.

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THE SKINNY
on AI for Education

Welcome to The Skinny on AI for Education newsletter. Discover the latest insights at the intersection of AI and education from Professor Rose Luckin and the EVR Team. From personalized learning to smart classrooms, we decode AI's impact on education. We analyse the news, track developments in AI technology, watch what is happening with regulation and policy and discuss what all of it means for Education. Stay informed, navigate responsibly, and shape the future of learning with The Skinny.

But first, calling all educational leaders, please complete our EVR
AI Benchmarking Self-Evaluation exercise - it's important that we hear from as many UK schools and colleges as possible, to ensure our analysis gives a representative perspective about what is happening with AI in UK education at the moment.

How are you using AI? Compare your progress to other Schools and Colleges across the UK

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Professor Rose Luckin and the EVR Team would like to invite you to participate in a national benchmarking exercise to evaluate current trends in the use of AI in education. Your perspective counts and all the team will need is for you to complete a simple 10-minute self-evaluation.

Headlines

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I hope this message finds you well. Please accept my sincere apologies for the long gap since the last Skinny issue. As many of you will understand, my attention has been wholly dedicated to my family and the joyous arrival of my fourth grandchild - an incredible moment that undoubtedly warranted my complete presence and focus.

Now, I find myself grappling with the amount that has happened since the last issue - it is hard to get to grips with it all and to make sense of the various developments and their implications for education. I do my best below to navigate the latest technology developments, the continuing slew of large AI investments, the regulatory landscape and the increasing pressures on the education and training sector to get to grips with what AI is enabling students to achieve and businesses to leverage.

However, prompted by the recent OpenAI voice debacle, first please indulge me as I reflect that I once had the pleasure of being upgraded on a flight from New York to London and to add to the thrill of sipping chilled champagne and eating warm snacks in first class, I found myself sitting behind actress Scarlett Johansson. I knew she was on the flight way before I got on the plane and took my seat, I knew as soon as I heard the words "ooh now we can have a romantic dinner together" as the she entered the lounge at JFK airport with her friend. Her voice is so distinctive, and let's face it so sexy too! I knew this was no fake, because I had the benefit of seeing as well as hearing. There was no question - this was no imposter, but the real, living, breathing Ms Johansson. No wonder that OpenAI want to use her voice for their AI. Not only did she entice Joaquin Phoenix in the film 'Her' but most of the population of filmgoers too. How sad then that OpenAI have been so dishonest in the way they dealt with their misuse of her identity. A warning to all of us that they need to earn our trust and that we should not give it to them without evidence that they deserve it! The alarm bells should be ringing, and we should definitely worry that OpenAI is eliminating its "super alignment team" focused on safety and be concerned about the rapid development and deployment of AI technologies without sufficient oversight and consideration of potential risks and ethical implications - especially in education.

Oh, and just in case you are wondering, Ms. Johansson was the epitome of grace and charm, her petite frame and natural beauty undiminished by the absence of makeup. As we landed, she slipped on a baseball cap and discreetly disappeared into a waiting car, leaving her fellow passengers none the wiser to her presence as they begun to take their luggage from the overhead lockers!

But on to the newsletter now and what has been happening with AI in education...

what's been happening with ai

I was disheartened to see that the number of adult learners in England declined by 47% between 2010-11 and 2022-23. This decline is most pronounced in the most deprived areas of the country, exacerbating the existing skills divide. At the same time, business schools recognise the need for AI upskilling and are responding by developing new offerings to meet the evolving needs of executives and organisations and whilst there is a shortage of experts to teach the courses, it seems clear that those who can afford it will be able to get the skills they need. Sadly, the majority risk being left behind unless a concerted effort is made to increase adult training.

There are also questions about job satisfaction as AI completes more of the mundane work tasks to boost efficiency, the rise of AI also raises questions about the role of human judgment, creativity, and interpersonal relationships. Organisations that thoughtfully navigate these issues and find ways to leverage AI while empowering their employees will undoubtedly thrive in the AI era.

And employees with skills will likely do very well - 97% of IT jobs that pay over £100,000 now require AI skills as a core requirement and in general workers with AI skills are well-positioned to command an average skill premium of 21% compared to their peers without such expertise. 66% of executives at the vice president level or above state that they would not hire an applicant for a senior position who lacked knowledge of using basic generative AI tools. However, the ability to build applications using AI opens up even more opportunities, positioning job seekers as highly sought-after candidates in various industries.

Khan Academy announced that it is collaborating with Microsoft on AI in education and just to put the pressure on, OpenAI co-founder Greg Brockman and Khan Academy founder Sal Khan suggested that the window of opportunity for starting AI initiatives in education is closing rapidly. I find that hard to believe. I can understand why they would want us to believe that time is tight, but the truth is that we are only at the start of what will inevitably be a considerable a long journey for AI in education. It is also the case that building AI tutors that are truly helpful and natural to use is extremely hard and without advancing our understanding of a learner's context and evidence of delivering consistently helpful results, we should maintain some healthy scepticism.

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Meanwhile, Google published an intriguing paper, "Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach." The paper reports research that underscores the potential for sector-specific data to refine and optimise AI models for particular applications, such as education. The authors call for collaborative development of shared pedagogical benchmarks in the AI and education sector to drive evidence-based progress. They also acknowledge the technical limitations of their LearnLM-Tutor. For example, it is text-only, lacks full understanding of context, cannot leverage non-verbal cues, and may hallucinate information. Sociotechnical limitations include less personalisation and rapport-building compared to human tutors. They stress that more research is needed on effective human-AI collaboration in education. A finding that will be recognised by all those of us who have been working as part of the AI and education community for the past few decades.

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The relentless march of technological progress continues, from OpenAI's GPT-4o launch and Google's enhanced Gemini 1.5 Pro and Gemini Live voice chat app, to the development of smaller language models by Microsoft, Google, Meta, and Apple to address concerns over the costs and resource demands of large language models (LLMs). Chinese AI companies offer "AI-in-a-box" products for businesses to maintain data and operations in-house, while Apple's OpenELM family of open-source, smaller LLMs prioritizes user privacy by running on Apple devices. Amidst this progress, big tech's disingenuity persists, from OpenAI's 'Sky' voice controversy to Meta's simultaneous release of the open-source Llama 3 LLM and expressions of their own scepticism regarding the safety and human-like intelligence of large LLMs. Microsoft's "Windows Recall" also raises questions, granting their AI co-pilot a "photographic memory" of user activity without clear justification.

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As Abba famously sang, "Money, money, money." The unabated flow of substantial investments in AI continues, exemplified by Saudi Arabia's $100 billion commitment to become a global AI hub and diversify its economy. With investors demanding swift returns, the pressure to innovate may overshadow considerations of safety and ethics. Regrettably, this climate could exacerbate the existing lack of transparency from AI companies at a time when public trust and collaboration between companies and regulators are paramount for the responsible development and deployment of AI technologies. Whilst on the subject of money, Nvidia, the leading AI chip manufacturer, reported a staggering 262% revenue increase in the past quarter, underscoring the immense financial potential in this field.

As we all try to safely navigate this complex and rapidly evolving landscape, let's remain committed to the thoughtful and ethical advancement of AI in education. Collaboratively, I continue to believe we can work towards a future where the benefits of AI are harnessed for the greater good, while vigilantly safeguarding against potential pitfalls.

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1. The number of adult learners in England has declined by 47% between 2010-11 and 2022-23, leading to a loss of 7 million qualifications. The decline has been most pronounced in the most deprived areas of the country, exacerbating the existing skills divide. This trend is attributed to 28% real terms cut in per capita funding for adult skills by the government and a 20% reduction in investment per employee by companies over the same period, hindering economic growth and social mobility as the demand for skilled workers continues to grow.

2. The growing interest among executives in the commercial applications of generative AI is driving business schools to offer a wide range of new courses focused on digital understanding and skills. A recent survey found that nearly half of the respondents anticipate learning about AI in the next five years, along with other digital subjects such as cybersecurity, digital marketing, e-commerce, and data analytics. Business schools are responding by revamping their course portfolios and developing new offerings to meet the evolving needs of executives and organizations.

3. Training providers face the challenge of finding enough experts to teach courses on the latest AI technologies. MIT Sloan School of Management collaborates with professors from the MIT Computer Science and Artificial Intelligence Laboratory to deliver its AI courses, highlighting the need for interdisciplinary collaboration and innovative approaches to meet the growing demand for AI education.

4. As AI-generated content becomes increasingly prevalent in the workplace, organizations must reflect on the meaning and value of work for their employees. While AI can automate mundane tasks and potentially boost efficiency, it also raises questions about the role of human judgment, creativity, and interpersonal relationships. Organizations that thoughtfully navigate these issues and find ways to leverage AI while empowering their employees are more likely to thrive in the AI era, but few companies seem to be actively grappling with these challenges as AI adoption expands.

5. George Siemens announced the launch of a Global Data Consortium, an initiative to encourage universities to become producers, not just consumers, in the AI arena. The project aims to help universities get involved in AI development by reviewing and commenting on a concept/technical paper. Siemens argues that higher education is not sufficiently engaged in AI and proposes the consortium as an on-ramp for universities to start building with AI, inviting feedback on the concept paper.

6. Khan Academy is collaborating with Microsoft on AI in education. OpenAI co-founder Greg Brockman and Khan Academy founder Sal Khan emphasized that the window of opportunity for starting AI initiatives in education is closing rapidly, and the education system has done little internally to address this pressing need, stressing the importance of swift action and collaboration to harness the power of AI for improving educational outcomes and experiences.

7. Google has published a paper titled "Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach," which demonstrates the value of educational data in shaping the effectiveness of large language models (LLMs) in specific sectors. Key Takeaways - Google DeepMind and Google have developed LearnLM-Tutor, a generative AI model optimised for 1-on-1 conversational tutoring. The development process involved participatory research with learners and educators to identify key pedagogical principles and capabilities to prioritize. This included workshops, interviews, and co-design activities. Supervised fine-tuning of Gemini 1.0 (base language model) on pedagogically rich datasets to create LearnLM-Tutor. Comprehensive evaluation of LearnLM-Tutor compared to the base model using 7 benchmarks spanning quantitative metrics, qualitative feedback, human judgments, and automated methods. LearnLM-Tutor outperformed Gemini 1.0 on most pedagogical dimensions. Real-world testing of LearnLM-Tutor with students in Arizona State University's online courses. Interviews revealed students found the AI tutor helpful, always available, and a safe space to ask questions. Responsible development practices including impact assessments, new educational policies, mitigations for risks like misinformation, and iterative human and automated evaluations.

1. An analysis of over 5,300 live tech jobs in the UK revealed that 97% of IT jobs paying over £100,000 now require AI skills as a core requirement, either explicitly in the job title or within the role's primary requirements. The study highlights the rapid integration of AI into high-paying tech jobs and the growing demand for professionals with AI expertise, emphasizing the need for IT professionals to acquire and continuously develop AI skills to remain competitive in the job market.

2. Workers with AI skills are well-positioned to command higher salaries, enjoying an average skill premium of 21% compared to their peers without such expertise. As AI continues to reshape the way workers use information and deliver results, sectors heavily exposed to AI are experiencing nearly five times higher growth in labour productivity compared to industries with lower AI adoption, highlighting the potential for AI to drive significant efficiency gains and create new opportunities for growth and innovation across various sectors.

3. AI is increasingly affecting the job market, with 66% of executives at the vice president level or above stating that they would not hire an applicant who lacked knowledge of using basic generative AI tools. Junior and less-experienced candidates are more likely to secure employment and receive increased responsibility if they possess AI skills, highlighting the growing importance of AI competency across various levels of the workforce as companies recognize the value of employees who can effectively leverage AI tools to enhance productivity and innovation.

4. Proficiency in using AI tools has become a valuable asset in the current job market, providing individuals with a competitive edge in securing employment and advancing their careers. However, the ability to build applications using AI opens up even more opportunities, positioning job seekers as highly sought-after candidates in various industries. As companies increasingly recognize the potential of AI to transform their operations and drive innovation, the demand for professionals who can design, develop, and implement AI-powered solutions continues to grow, making AI application development an increasingly valuable and in-demand skill set.

1. OpenAI's launch of GPT-4o, a multimodal AI model, was overshadowed by a dispute with actress Scarlett Johansson. The AI assistant, named "Sky," had a voice that Johansson claimed was too similar to her own, used without her permission. After receiving legal notices from Johansson's team, OpenAI's CEO Sam Altman removed the voice and apologized, stating that it was not intended to resemble hers. The incident highlights growing concerns in Hollywood about AI's potential to disrupt the entertainment industry and infringe upon actors' rights.

2. Major tech companies, including Microsoft, Google, Meta, and Apple, are developing small language models to encourage the adoption of AI by businesses. These models have fewer parameters but still possess powerful capabilities, making them more cost-effective and requiring less computing power. The move aims to address concerns from enterprise customers about the high costs and resources needed to run large language models and make AI more accessible to a broader range of businesses.

3. Chinese AI companies are introducing "AI-in-a-box" products that allow businesses to run AI on their own premises, posing a threat to the AI cloud computing services offered by major Chinese tech firms like Alibaba, Baidu, and Tencent. These boxed solutions cater to companies that prefer to keep their data and operations in-house rather than relying on cloud-based services, potentially disrupting the market for AI cloud computing in China, where on-premises and private cloud setups account for about half of the market.

4. Nvidia reported record sales of AI chips, leading to a 262% increase in revenue for the past quarter, surpassing analysts' expectations. The company's CEO, Jensen Huang, expects this growth to continue throughout the year, driven by the launch of their new Blackwell chips. Huang also announced that Nvidia would maintain an annual pace of introducing newer, more powerful chips, highlighting the increasing demand for advanced AI hardware and Nvidia's dominant position in the market.

5. Meta's chief AI scientist, Yann LeCun, expressed scepticism about the ability of large language models (LLMs) to achieve human-like reasoning and planning capabilities. LeCun argued that LLMs have limited understanding of logic, the physical world, and lack persistent memory, making them "intrinsically unsafe." Instead, he advocates for an alternative approach to create "superintelligence" in machines, contrasting with the current focus on advancing LLMs and suggesting that a different path may be necessary to develop truly intelligent AI systems.

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6. Meta AI has released Llama 3, an open-source large language model with 8 billion and 70 billion parameter versions, and a 405 billion parameter model in training. When running on Grok chips, the results are highly impressive, highlighting Meta's AI capabilities and commitment to open-source development.

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7. Vector databases are becoming increasingly important for LLMs and AI, as they enable the creation of vectors that help AI systems understand and process information more effectively, improving the performance and efficiency of AI applications.

8. Microsoft has announced new AI features for Windows, including "Windows Recall," which gives the AI assistant a "photographic memory" of a user's virtual activity while promising to protect privacy by allowing users to filter what is tracked, raising both excitement and concerns about the potential implications of such technology.

9. Apple's OpenELM is a family of open-source, smaller large language models designed to prioritise user privacy by running on Apple devices. The models range from 270 million to 3 billion parameters and can process 2,048 tokens of context. OpenELM's architecture differs from current state-of-the-art transformer models, and in evaluations, it outperformed several other open-source models trained solely on publicly available data, although it fell short on the MMLU benchmark.

10. Google's Gemini 1.5 Pro has received significant updates, most notably the doubling of its input context window to 2 million tokens of text, audio, and/or video, enabling developers to apply generative AI to multimedia files and archives beyond the capacity of other currently available models. Google also introduced Gemini 1.5 Flash, Veo, and expanded the Gemma family of open models, as well as launching Gemini Live, a smartphone app for real-time voice chat, as part of Project Astra, a DeepMind initiative to create real-time, multimodal digital assistants.

11. Amazon is removing its AI-driven Just Walk Out checkout service from most of its Amazon Fresh grocery stores and replacing it with Dash Cart, a smart shopping cart that enables customers to scan purchases as they shop. This decision suggests that the Just Walk Out technology may be less well-suited to larger store environments, facing challenges such as reliance on remote employees, difficulty in improving accuracy, high installation costs, and the need for extensive remodelling in some stores.

1. Nvidia's record sales of AI chips led to a 262% increase in revenue for the past quarter, exceeding analysts' expectations. The company's CEO, Jensen Huang, anticipates continued growth, driven by the launch of their new Blackwell chips and a commitment to introducing newer, more powerful chips annually, demonstrating the increasing demand for advanced AI hardware and Nvidia's leading position in the market.

2. While Asia's largest AI and chipmaker stocks have continued to gain, companies further down the semiconductor supply chain, such as those involved in chipmaking equipment, materials, and chemicals, have been lagging behind their global peers in recent months. Shares of Tokyo Electron, Towa, Advantest, and Screen have experienced declines, indicating room for upside in these sectors and suggesting that investors are primarily focused on leading AI and chipmaker stocks, while the broader semiconductor industry is yet to fully benefit from the AI boom.

3. Salesforce shares plummeted by 20% after the company reported weaker than expected revenues and bookings, erasing approximately $50 billion from its market value. The customer relationship software giant provided a rare single-digit growth outlook for the second quarter, indicating challenges in maintaining growth momentum despite the increasing demand for AI and digital transformation solutions.

1. OpenAI is facing increased scrutiny due to its handling of various issues, including employee investment plans, the elimination of its "super alignment team" focused on safety, and the voice used in its Omni model. There are calls for more "adult supervision" of the company, highlighting concerns about the rapid development and deployment of AI technologies without sufficient oversight and consideration of potential risks and ethical implications.

2. Saudi Arabia is investing $100 billion to become a global AI hub, aiming to diversify its economy by channelling its wealth into more sustainable industries. The country has established funds and partnerships to invest in AI start-ups, research, and infrastructure, attracting investments from both AI giants and start-ups. However, some investments have faced delays and criticisms, and U.S. partners have drawn scrutiny for working with Saudi Arabia due to human rights concerns.

Welcome to "The Skinny on AI for Education" newsletter, your go-to source for the latest insights, trends, and developments at the intersection of artificial intelligence (AI) and education. In today's rapidly evolving world, AI has emerged as a powerful tool with immense potential to revolutionize the field of education. From personalized learning experiences to advanced analytics, AI is reshaping the way we teach, learn, and engage with educational content.

In this newsletter, we aim to bring you a concise and informative overview of the applications, benefits, and challenges of AI in education. Whether you're an educator, administrator, student, or simply curious about the future of education, this newsletter will serve as your trusted companion, decoding the complexities of AI and its impact on learning environments.

Our team of experts will delve into a wide range of topics, including adaptive learning algorithms, virtual tutors, smart classrooms, AI-driven assessment tools, and more. We will explore how AI can empower educators to deliver personalized instruction, identify learning gaps, and provide targeted interventions to support every student's unique needs. Furthermore, we'll discuss the ethical considerations and potential pitfalls associated with integrating AI into educational systems, ensuring that we approach this transformative technology responsibly. We will strive to provide you with actionable insights that can be applied in real-world scenarios, empowering you to navigate the AI landscape with confidence and make informed decisions for the betterment of education.

As AI continues to evolve and reshape our world, it is crucial to stay informed and engaged. By subscribing to "The Skinny on AI for Education," you will become part of a vibrant community of educators, researchers, and enthusiasts dedicated to exploring the potential of AI and driving positive change in education.

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The Skinny on AI for Education #7 | Professor Rose Luckin's EDUCATE Ventures Research | AI for Education

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Growing AI capabilities spur major investments but raise ethical concerns over rapid, unchecked development. Education sector eyes AI's potential while grappling with skills gaps and integration challenges. Job market demands AI skills as businesses weigh workforce impacts. The responsible path requires oversight and evidence-based optimisation.

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"Postcreation" harnesses all human knowledge and achievements, leveraging AI and multimodal LLMs to create a new future. This isn't mere remixing or sampling; it's reimagining and redefining culture. A communal effort transcending past limitations, fostering collaborative innovation.

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With all the talk in HE of AI and plagiarism, little attention has been given to teaching, learning or research. I have outlined an idea for an AI University, with a relentless focus on AI to optimise teaching and learning, to increase access and reduce costs.

As a corollary, let us look at 'research'. Can an AI University be a focus to optimise research? It has already happened over the last two decades. The real question is not whether AI can generate research, it is whether it can halt the paper mill it has become.

Future of research

Reports are tumbling out of institutions telling us that AI simply augments human effort. This is simplistic, so obviously defensive that it is in danger of turning into a pearl clutching meme.

AI has been accelerating research for decades

AI will automate many researc h tasks

AI will eliminate some human research

AI will create new research opportunities

AI will produce big wins, leapfrogging existing research efforts

With cheap intelligence-on-demand, AI has already sped up the process of research across a broad front and promises a lot more.

Wave 1: Existing impact

We have already seen this happen in the first wave, with search, citation search and access to Journals and knowledge at our fingertips, in seconds. The impact of AI on 'research' may be much greater than it is on teaching and learning. It has gone under the radar but it the impact has already been astonishing. Google Search and Google Scholar accelerated research, as the whole business of access to Journals, knowledge and ready to use citations, slashed time to completion. I remember waking mile after mile along library shelves to find Journals and books, hour and hours labouring over desk research and citations (the bane of any researcher back then), even the typing of dissertations and theses with zero cut and paste! All have been accelerated or automated by AI. One could argue that every PhD period should have been slashed by several months!

Wave 2: Automating tasks
A second wave is now hitting research, as tasks are being augmented and automated, with a slew of research papers showing that this is not only possible by happening.
Let's look at tasks the literature has already identified as being possible, productively, by AI:

*Abstracts

*Introductions

*Literature reviews

*Research gaps

*Hypotheses

*Scoring/Open text surveys

*Research plans

*Data analysis

*New concepts

*Idea generation

*Full paper outlines

*Full papers

*Faster peer review

Wave 3: Optimise research

A third wave may emerge where research is being eliminated on scale, especially text based speculation, meta studies and data-driven research. There is every reason to believe that AI will create new research opportunities and eliminate entire areas of current research endeavour. One would also hope that is speeds up peer critiques and reviews.

One would hope that, rather than making the current paper mill go faster, we can optimise research by eliminating bad or weak research. The system is in need of a rest on quality and quantity. Far too much research is being published and not used or even read. AI could help solve this problem.

Wave 4: Big wins

A fourth wave will leapfrog research to produce enormous wins. This has already happened and will accelerate. In some specific areas the impact of AI has leapfrogged the existing paradigm.

*Alphafold: 1 billion years of research saved

*MIT: Super-antibiotic Halicin discovered

*Materials discovery of millions of new structures by Deepmind

Demis Hassabis says, one protein was taking 4-5 years to identify its 3D structure. We now know all 200 million, saving 1 billion YEARS of research. Read that again - it is an astonishing figure. This was only the start as we've also seen a similar breakthrough by Google on materials science, and a groundbreaking drug discovery by MIT.

Do we think this will stop? I don't think so.

AI University and research

One radical idea is to break the link between research and teaching, to recognise that this is part of the problem, the old Humboldtian idea that researchers make good teachers and that it is a necessary condition for success. I have argued that breaking this old Prussian bond could be a good thing, on the basis of research showing that researchers as teachers often hinder rather than help teaching in Universities.

What is needed is a University that grabs the bull by the horns and becomes a centre of research itself, or how research can be accelerated to release the bounty that AI offers or engages in research that uses AAI to accelerate progress. Research is, after all, a means to an end, not an end in itself. There is always a purpose or goal, even in blue sky physics, we have a general understanding that understanding the nature of reality has benefits for us as a species and often lays the ground work for new research.

There has to be a recognition that we are returning to a successful model that emerged in the Post-War period, with some notable examples before, (Bell, Eddison, Xerox, Tesla etc) , where large tech companies are doing their own research and leaping ahead of academia. This is not lone wolf activity, as they rely on academic output in terms of talent. However, their speed, ability to take risks, access to data, ability to deliver on scale and competitive mindset means that things happen quicker with more substantial breakthroughs.

Post-war success in the US in particular was engineered by Vannevar bush to co-ordinate academia, Government and business, paying to the strengths of all three. This is what ended the war with the Manhattan project and got them to the moon. All three need an element of humility to co-operate and make human advances to solve big ticket problems, such as climate change and the energy crisis.

It is no accident that almost all the breakthroughs are taking pace in the US, perhaps the Anglosphere of one includes Deepmind. There seems to be something in this idea that the optimal way forward is collaboration between state, academia and commerce, rather than anti-corporate, anti-government or anti-academic sentiment being allowed to hinder progress, as that is clearly sub-optimal.

Bibliography

Aydın Ö., Karaarslan E. OpenAI ChatGPT generated literature review: Digital twin in healthcare (2022)

Chen Y., Eger S. Generating (humourous) titles from scientific abstracts end-to-end (2022)

Wenzlaff K., Spaeth S. Smarter than humans? (2022)

Dowling M ChatGPT for (Finance) research: The Bananarama Conjecture (2023)

Zhai X. ChatGPT user experience: Implications for education (2022)

Adesso G. GPT4: The ultimate brain. Authorea Preprints (2022)

Valavanidis, A., SCIENTIFIC REVIEWS Artificial Intelligence Application withMachine-learning Algorithm Identified a Powerful Broad-Spectrum Antibiotic.

Merchant, A., Batzner, S., Schoenholz, S.S., Aykol, M., Cheon, G. and Cubuk, E.D., 2023. Scaling deep learning for materials discovery. Nature, pp.1-6.

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BERA Blog, from Sam Illingworth, Associate Professor at Edinburgh Napier University

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Generative AI in academia: Friend or foe? 

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Generative AI in academia: Friend or foe? 

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https://www.bera.ac.uk/blog/generative-ai-in-academia-friend-or-foe

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Inspire active learning: Allow for practice and healthy struggle with timely feedback

Manage cognitive load: Present relevant, well-structured information in multiple modalities

Adapt to the learner: Dynamically adjust to goals and needs, grounding in relevant materials

Stimulate curiosity: Inspire engagement to provide motivation through the learning journey

Deepen metacognition: Plan, monitor and help the learner reflect on progress

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Generative AI is fundamentally changing how we're approaching learning and education, enabling powerful new ways to support educators and learners. It's taking curiosity and understanding to the next level - and we're just at the beginning of how it can help us reimagine learning.

Building a new family of models for learning

Today we're introducing LearnLM: our new family of models fine-tuned for learning, based on Gemini.

Grounded in educational research and tailored to how people learn, LearnLM represents an effort across Google DeepMind, Google Research and our product teams to help make learning experiences more engaging, personal and useful. Our technical report presents our approach to improving generative AI for education and highlights how we're working together with the AI and EdTech communities to responsibly maximize its positive impact and potential.

Working alongside educators and other learning experts, we're infusing learning science principles, like the following, into our models and the products they power:

Inspire active learning: Allow for practice and healthy struggle with timely feedback

Manage cognitive load: Present relevant, well-structured information in multiple modalities

Adapt to the learner: Dynamically adjust to goals and needs, grounding in relevant materials

Stimulate curiosity: Inspire engagement to provide motivation through the learning journey

Deepen metacognition: Plan, monitor and help the learner reflect on progress

Bringing LearnLM to products you already love

With LearnLM we're enhancing learning experiences in products you already use today - like Search, YouTube and when chatting with Gemini - so they can help you deepen understanding, rather than just giving an answer. Here are a few examples:

In Google Search, soon you'll be able to make sense of complex topics by tapping a button to adjust your AI Overview into the format that's most useful for you - whether you want to simplify the language, or break it down.

On Android, Circle to Search can help people get unstuck on math and physics word problems directly from their phones and tablets. Later this year, you'll be able to solve even more complex problems involving symbolic formulas, diagrams, graphs and more.

When chatting with Gemini, soon you'll be able to use Gems, custom versions of Gemini that can act as personal experts on any topic. Learning coach, one of the pre-made Gems, can support you in building knowledge by providing step-by-step study guidance, along with helpful practice activities like quizzes and games. Learning coach in Gemini will launch in the coming months, and with Gemini Advanced, you'll be able to further customize this Gem to suit your unique learning preferences.

On YouTube, a conversational AI tool makes it possible to figuratively "raise your hand" while watching academic videos to ask clarifying questions, get helpful explanations or take a quiz on what you've been learning. This even works with longer educational videos like lectures or seminars thanks to the Gemini model's long-context capabilities. These features are already rolling out to select Android users in the U.S.

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How generative AI expands curiosity and understanding with LearnLM

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LearnLM is our new Gemini-based family of models for better learning and teaching experiences.

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Catalogue of Tools & Metrics for Trustworthy AI