University enrols two AI students - fascinating experiment

An experiment is taking place in a Michigan University (Ferris University), where two AI students have enroled and will take the same route through courses as their fellow human students. The aim is to evaluate the ‘student experience’. Interesting idea.

We’ve had successful teaching assistants since 2016 in Higher Education, along with adaptive, personalised teaching systems that educate everyone uniquely. We also know that current LLMs can crush high stakes exams in HE but this attempts to track the learning experience of students, so can make its own decisions, choose courses and generally have the agency of a real student.

There is a more fundamental idea here, of the mystery shopper or evaluator for any service, whether in education, healthcare, retail, whatever. Run bot customers through the system and see what happens? This is a great way to identify flaws, redundant processes and risks. It can be built to include risk analysis methods to identify and quantify risks, as well as recommend improvements. The choice of 'student experience' is a bit of a get out. Let's not tackle teaching and learning, let's see if they enjoy their gilded cage?

As a reflective experiment this has some merit. Lots is made of the student experience, yet little is done to improve teaching and learning, with the lecture and essay still rock solid as core pedagogies. Placing a proxy learner in context, going through the motions is interesting. 

Careful what you wish for

They have to be careful what they wish for here. The AI will have a flawless digital memory, will not sleep, be super quick at tasks, never distracted, can multitask, network, never gets a hangover. Will is smash the exams? Will it have the urge to cheat?

The set up will be important. Will they be modeled on typical student behaviour, where 40% don't turn up to lectures?

The AI students are called Ann and Fry – odd as they say there’s no genders attributed? They are not robots but will listen through microphones, do assignment and eventually speak . It will have to go through the admissions process, then registration and make decisions on what classes it wants to take.

In HE, it would, I’d imagine, do a hatchet job on lectures (transience effect, cognitive overload, little interaction, poor slide design and poor teaching). It could compare live to recorded lecture experiences and, in its eyes, conclude that the convenience of recorded lectures wins hands down. It could critique the need for long absences during holiday breaks, the odd idea of having to wait for months even a full year to resit an exam. It could notice the number of students consistently absent from lectures.

To be fair, it is being built in partnership with the U.S. Department of Defense, National Security Agency, Department of Homeland Security and Amazon Web Services. That some pretty heavy fire right there.

The lack of research on using AI to teach and learn has been puzzling, compared to studies on productivity in the workplace. They've been noticeable by their absence. Yet we have truckloads of papers, frameworks and report on ethics and AI. I'd like to see two groups, randomised etc. one with, the other without AI, to measure impact on learning and performance. In may ways that would be more useful.

Of course, we all know what will actually happen… you don’t need millions to see the weaknesses already… but more power to their elbow. The US is taking the AI bull by the horns, while others write endless reports on ethics… it’s easy to be a critic much harder to do do real stuff and test against reality.

 

Artificial Intelligence - calm down everyone - meaning is use

‘Artificial Intelligence’ was coined in 1956 by John McCarthy at Dartmouth College for the conference, held there, that kicked off the modern era in AI. Along with Marvin Minsky, Nathaniel Rochester, and Claude Shannon, he posited that AI was "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." Not a bad pop at definition and pretty open. I studied there and they had a little area dedicated to that conference, which has turned out to be much more significant than was thought. It was there that I got interested in tech in the very early 80s, was exposed to a mainframe computer, came back to the UK, built by first technology-based learning programme, to teach myself Russian, and the rest, for me is history.

McCarthy later regretted coining the phrase and preferred the words 'Computational Intelligence'. If we had listened, we’d all be using the letters CI! His problems with the terms Artificial and Intelligence were prescient, in that he saw it encouraging exaggeration, hype and over reach, also misunderstandings about what it actually was. He turned out to be right. The current doomer debate is the perfect example, where its meaning is hopelessly entangled with esoteric moralising. He preferred a phrase that focused on actual computing to do things we humans can do or find difficult.

There is the added problem that the word 'artificial' has perjorative tones, as in artificial grass or meat. The word 'intelligence' is also problematic invoking anthropomorphic and difficulties in definition, even outside of AI. I discuss these issues in detail here.

Four main meanings

In truth, four main meanings have emerged:

1. For many, when they use the phrase AI, they mean the post November 2022 Generative AI explosion around Chatbots, image generation and more recently multimodal AI. Even then, for some, it's ChatGPT3.5 and not 4 and subsequent versions - tools move fast here.

2. Others give it a wider meaning to include pre-November 22 achievements such as Deepmind’s Alphafold and AlphaGO then AlphaZero, AlphaStar, AlphaGeometry (though few know the latter two).

3. Other still, who knew about or were working with AI for years see it wider still, understanding that far from being one thing, AI is many things. This was well covered in Pedro Domingos’s book The Master Algorithm, with chapters on Symbolists, Connectivists, Evolutionists, Bayesians and Analogizers. 

4. Some also fix its meaning in the ethical debate and automatically think of this when the phrase is uttered.

From the ‘learning’ perspective, a word that was emphasised in the original 1956 definition, there’s machine learning (supervised and unsupervised), reinforcement learning, deep learning and all sorts of species and sub species. ‘Learning’ is a word you hear a lot in AI. 

Other ways of looking at AI is through areas of problem solving, such as NLP (Natural Language Programming), image recognition or robotics. There are many other ways still of slicing the AI cake. The important point is to see it as a set of very different technologies or tools that solve problems. Roger Schank, who was heavily involved in these debates, preferred to just call it software – he had a point!

Meaning is use

You can see the problem. There is no one 'meaning', as meaning is nuse As I explained in my book AI for Learning, Wittgenstein claimed the word ‘game’ is difficult to pin down, and is used in itself and with other with other words, to mean all sorts of things, from Olympic Games to board games, games people play, game theory, even simply throwing a ball against a wall. His theory that meaning is use and that meaning emerges from a set of ‘family resemblance’ is interest in AI, as that is exactly how LLMs work. Meaning emerges from its use within a Chatbot. His theory of language games is also useful in this context. Wittgenstein’s message was – beware of large words, especially in their reification.

It shouldn’t bother us too much, as meanings change with semantic drift, new facets of AI emerge and are folded in, or assumed in use. This is how language works. Meaning is not, as many pedants would like you to believe, strict fixed dictionary definitions. It is the other way round – dictionary definitions come from use and people use words in different ways within different contexts.

Funnily enough, LLMs, do exactly this. You can get them to play Wittgensteinian 'language games', by asking them different types of questions, getting them to play different roles and use different forms of expression.  I explore this here and in this podcast.

When one sees meaning as use, we can relax and understand that AI has and will continue to change. It has come to mean different things, as it conquers new challenges. It is, in a sense, what is will be.

PS another beef!

Whenever I hear the phrase ‘stochastic parrot’ I think – here goes, someone’s picked up on a meme to wave around without knowing where the phrase came from. I simply ask where the phrase comes from.

To be fair, those who have tread the paper from which it came, written well before ChatGPT3.5 was launched didn’t really know either. DSo you know the author of the paper or when it was written?)

Sure LLMs rely on statistical processes but they are in no way mere parrots and can exhibit complex language generation capabilities. They do not ‘parrot’ anything in the sense of copying or sampling. If you parrot someone or something, you literally copy or mimic them. LLM output does no such thing. It is all freshly minted.

Of course, it is always used pejoratively, with more than a shade of a sneer, as if it were mindless repetition, which it is not. This reductive approach strips all nuance away from what they do in terms of their utility, abilities to rewrite in different styles, summarise, critique, create and translate. 

The Bender paper is actually an attack on increasing the size of LLMs. It was written a two years before ChatGPT3.5 hit. So they got this completely wrong. Their recommendation for AI was to carefully curate datasets, not pursue Larger Language Models. How wrong they were.

A Large Language Model (LLM) happened once before in history and it changed the world forever…

The Large Language Model GPT, from OpenAI, is one of the great wonders of the modern world. It is captivating, intriguing and above all useful. For the first time in the history of our species we have personal access, on a Global scale, to the sum of human culture. When the world speaks to a language model through ChatGPT, we speak to ourselves, the global mind. A LLM, like a brain and language, is unfathomable but dialogue gradually reveals its nature. Yet this is not the first time this has happened.

2300 years ago, in Alexandria, Ptolemy 1 decided, in this city at the crossroads of Europe, Asia and Africa, to do the same thing. He collected as much of the world’s literature as he could, paying for much of it, confiscating some, even stealing some, whatever it took to create the sum of known human knowledge, in many languages, from many lands “to collect, if possible, all the books in the world.” Reading Islam Issa's wonderful book, Alexandria, I was struck by the parallels.

Global dataset

Ptolemy 1 had Global ambitions and written knowledge was all within his reach, except for China, the only other place on earth where writing had been invented. He wrote to many other leaders and had no cultural bias – anything from any land in any language was welcome. It is thought that something in the order of 700,000 scrolls were assembled into this one, huge, data set.

In gathering content for the Library he used other people content from books, reached out, bought other libraries, copied every book they could find, begged borrowed and even stole. This is close to what has happened with the training data for LLMs, where a huge corpus of text, that would take 22,000 years to read, has been used to train the GPT model. GPT was trained on around 300 billion words. The average scroll in Classical Greece was around 10-15,000 words. If we take 700,000 scrolls at an average of 12,000 words each, the total number of words in the Library was around 8.4 billion words. That was impressive!

Like GPT, it was easily the largest dataset in the world, way bigger and therefore more useful than smaller libraries. They also had a technical advantage - the means of production and delivery – papyrus. Egypt owned the papyrus trade, limited supply to foreign buyers and therefore controlled the means of distribution, just like the data and compute clout of a Microsoft for ChatGPT. It even embargoed papyrus in 190 BC, intentionally restricting the growth of other libraries, like Pergamum. Scale mattered.

They even invented the idea of metadata for large datasets. First data preparation, translating everything into Greek, giving a single data standard. Seventy two Jewish scholars were employed and paid to translate the Bible. Then labelling each scroll with the author’s name and location. Further metadata was produced with categories such as doctors, historians, legislators, philosophers, rhetoricians, comic poets, epic poets and miscellaneous. They then went alphabetical. Finally, a complete catalogue. was produced. All of this increased the efficacy of research through more efficient search. They understood that the interface, ease of acccess to knowledge, mattered. This is what gave ChatGPT its status as the fastest adopted technology in the history of our species - ease of access.

The point was not to just collect all known papyrus scrolls, it was to learn from them and to globalise knowledge. The parallels between that ancient act and the current appearance of Generative AI has some fascinating parallels. Knowledge and access to that knowledge is power and this is a story of power. That power was instantiated when the library encouraged debate, discussion and outputs.

Access was the real key to success. Ptolemy allowed any scholar from anywhere to come and use the dataset, and they did. This is why this new AI tech is so exciting - anyone has access to it at little or no cost, from anywhere. Once we democratise intelligence, we democratise (to a degree) power. LLMs are currently affecting research and outputs in many different fields or sectors, just like the Library of Alexandria, which accelerated research, productivity and the creation of ideas for centuries to come.

Rapid achievements

Foundationally in mathematics, Euclid of Alexandria wrote his 14 volume Elements here, which included the first ever written algorithm,  a method to calculate the Greatest Common denominators for any given number. His theorems and, more importantly, proofs were deduced from axioms. Familiar examples include the proof that the angles of a triangle add up to180 degrees and Pythagoras’s Theorem. It is this logical rigour that is remarkable, influencing the entire history of mathematics and science. It was used as the main textbook in mathematics for over 2000 years, well into the 20th century and all University students for centuries used this book as part of the quadrivium. f algorithms. Beyond this he wrote on the rigour of mathematical proof, conic sections, the geometry of spheres and number theory. In his Phaenomena, Euclid aims at astronomy with a treatment of spherical geometry.. Like LLMs, mathematics lay at the root of this project.

Conon of Samos developed conical mathematics. In astronomy they discovered the planet Mercury, compiled a catalogue of stars and developed a heliotropic view 1800 years before Copernicus. Map drawing and geography flourished with Claudius Ptolemy’s book Geography. He also saw mathematics, an Alexandrian obsession as being superior to the metaphysics of Plato and Aristotle. Eratosthenes calculated the earth’s orbit around the sun creating an accurate calendar, also realising that the earth was round he calculated, using rods, shadows at two locations, the circumference of the earth (he was accurate to within 50 miles). Foundation models are essentially language as maths with outputs as freshly minted language. Mathematics, once again, has proved its worth in the transmission and creation of knowledge.

Medicine also advanced with anatomy, dissection (even on live criminal patients!) and the pulse as a diagnostic sign. This is also happening with AI in healthcare, as new drugs are discovered, new materials and 200 million proteins, saving 1 billion years of research, were unlocked with AI.

In literature, there was the invention of the dictionary, and creative output in poetry, drama and music, sculpture and mosaic work, just as we are seeing, with the augmentation of art and ideas with LLMs.

It did not stop there, as astonishing feats of engineering also emerged from the work at the Library. Archimedes studied here and went back to Syracuse to build sophisticated war machines for the Romans. The mechanical astrolabe was invented, along with mechanical objects such as keyboard instruments, water clocks and automatons such as singing statues, chirping birds, dancing puppets,. There were self-trimming oil lamps, syringes, lab equipment for chemistry, a coin-operated vending machine for Holy Water, fire engines, even a steam engine! With AI we are also seeing the rise of speaking robots with Tesla’s self-driving cars and the astonishing Optimus robot.

Just like modern LLMs, deepfaking started almost immediately in Alexandria’s Library with scams and forgeries. What’s new? But this was a temporary problem and soon overcome. Alexandria grew rapidly as centre of mathematics, art and philosophy. It came to an end hundreds of years later, in the fourth century, when the mathematician Hypatia, who became a woman of mathematical renown and intellectual stature, was murdered by a Christian mob. The Classical world was nearing its end and monotheistic religion was starting to dominate leaders and the intellectual world. By the late 4th century AD, Christianity was banning books, all but scripture and the library was in decline largely through censorship and eventually the banning of non-Christian books as heretical. Yet its influence remains as a conduit for knowledge and creation and invention. There was no fire, it suffered a slow decline through intolerance and misguided moral certainty. There is, perhaps, another lesson to be learnt here - not to let moralisers destroy what is good on the back of their dogmatic belief that learning and innovation is bad.

Conclusion

Alexandria teaches us a lesson, that when we pool resources and create something unique, that benefits the whole of our species, wonderful things happen. It became the intellectual centre of the world for several centuries, one of the most important cities in the world, for the Ptolemies, Romans, Arabs, Ottomans, French and British. 

With GPT we have achieved something similar but this will not take centuries, even decades to prove its worth. It is already bearing similar fruit, wonderful things; real leaps in research, going multimodal, with dialogue, voice, images and video. Significant advanced in unlocking 200 million proteins, drug discovery and millions of new materials have already emerged, along with billions of uses a month.

We are tapping into the hive mind, just as the Alexandrians did over two thousand years ago to further improve the minds of all. We can do this if we focus on learning. We should not allow it to get crushed by the usual moralisers and religious inspired end-of days dogma but look for the bounty that it offers. We cannot say with certainty what will happen but we can be sure that it will be full of surprises and challenges. AI is the new Alexandria.

PS

This is linked to my idea for an AI University. The Library at Alexandria was the first University. Plato's Academy and Aristotle's Lyceum came earlier but they were really schools built around one man and his ideas. Alexandria was a different vision; cheap, open, secular and multicultural.

It had no faculty other than those interested in cataloging and keeping the system going. It had no formal teaching, just debate and discussion with further writing and practical invention. The idea of researchers as teachers came in hte 18th century with Humboldt.

It was also a powerful generator of ideas and inventions, not too abstract but as keen on the real world as ideas themselves. It was the retreat back into the scholastic world of theological beliefs that banned the books and put an end to the Library after 600 years. 

 

AI University and Research? Waves of innovation are hitting the research field

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. It can, of course, increase productivity but its real role may be in streamlining and producing better research.

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.

In truth:

AI has been accelerating research for decades

AI will automate many research 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:

• Titles

• 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.

Conclusion

There is another more serious problem here. Imagine a worldwide system, that trusts institutions to review and produce objective results, that gets billions in public funding, is found to be so corrupt that it has created incentives so bad that it is filling up with fake output, 10,000 fake entities have been found but we know this is the tip of the iceberg, with people buying and selling favours and a major country gaming the system by flooding it with false output. We have already moved towards this position.

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.

 

Paul LeBlanc - talks the talk and has walked the walk!

In support of my idea for an AI University, I relied on a UK precedent - The Open University where the remarkable Harold Wilson, and indomitable Jennie Lee, created a lasting institution that changed the face of UK tertiary Education. But there is another precedent that demands attention - Paul LeBlanc's achievement at SNHU.

He was the president of Southern New Hampshire University. Before joining SNHU, LeBlanc held various positions in academia, including serving as the President of Marlboro College in Vermont and has had a decades long interest in technologies for learning. My idea of the AI University was in part inspired by his success. he doesn't just talk the talk, at SNHU, ha walked the walk, and led the incredible growth on online learning at SNHU to make it  the largest University in the US.

His first book Students first; Equity, Access, and Opportunity in Higher Education (2021) takes a holistic look at reimagining Higher Education, covering everything from our ideas of the learner through to financial structures. It is a considered critique of the existing system, which, as the title suggests, is clearly not student-centred.

Critiques are easy, change is difficult and this is where LeBlanc has real credibility.  Once we see a system as that prioritises flexibility, accessibility and student success we can design innovative solutions. The current system is inflexible, has deeply embedded old practices and is far too expensive. It is also too remote from the real world. Education should be for life and living.

Given his background, which he explains in detail, he supports what he gained from, a student-first perspective, where institutions are deeply committed to student success, which only comes from understanding each student's unique background, challenges and goals.

Only then does he focus on delivering on this promise through competency-based education, online learning and flexible pathways that acknowledge students needs and real lives. A critical aspect of his model is to lower costs to address systemic inequities in higher education. The curriculum must deal with real-world skills and the needs of the modern labour market, preparing students not just for degrees but for meaningful careers. He is right here. The current system pretends that it is all about the intellectual journey, while making tons of money from future Dentists, Doctors, lawyers, Engineers, teachers and Nurses. In an age of critical skills shortages, we need at least some institutions to tackle this global issue.

He is also clear on the demands on leadership to drive change, the need of a culture of innovation and the need to be political savvy. In this sense the book is a call to action. Put the student first, and he means the many not the selected few, and all follows. 

In Broken (2022) he tackles the wider problems of existing approaches to problems in education, health, and other social systems. In all of this he abhors systems that result in failure, debt, emotional trauma and inequalities.

SNHU

LeBlanc admits that his route into online learning was part accident, as SNHU had satellite sites on Navy bases. The Navy has lots of learners at sea, so started delivering distance learning courses in 1995. As SNHU was serving a non-traditional student audience he also puts some of the early success down to this. There was little money and as a small campus, status was not a huge problem. It is both money and status, he thinks, that holds the system back.

LeBlanc transformed SNHU from 2500 students in 2003 to over 200,000 students in 20 years by using technology to switch delivery online. This was an early, significant and relentless investment in pursuit of a more scalable model using online education.

He learnt a lot from The University of Phoenix experiment and had a clear focus on treating applicants well, making the process quicker and easier. A fundamental aim was to keep tuition costs low and provide support for students from diverse backgrounds. LeBlanc has been a vocal advocate for making higher education more accessible and affordable. He continues to focus on innovating and adapting to the changing educational needs of students globally, a proponent of competency-based education and other models that prioritize learning outcomes over traditional measures.

SNHU is now one of the largest non-profit providers of online higher education in the country, offering more than 200 accredited undergraduate, graduate, and certificate programs.

AI

Having retired from SNHU, LeBlanc's is now expanding on his work to transform lives by taking on the challenges and opportunities that AI offers. This technology, he thinks, could help us rethink and reimagine education. Freeing ourselves from the current constraints of the existing models we could shift towards a more efficient, human and student-centred model, through AI.

He is working with George Siemens on a learning platform, alongside a solution to the data problem. The problem, they think, is the lack of any sophisticated approach to data, so they are hoping to build a build a global data consortium, with a focus on safeguards on student privacy and solutions to algorithmic bias. 

His new focus on AI centres around building a platform. This is worthy but not easy, and the world is littered with failures, as ideas are easy, implementation hard. I’m an admirer but disagree with his view of data as providing insights into learning. This is a long shot, as we have plenty of insights to inform practice through existing research and the data sets, as some have seen, have failed to provide much in the way of meaningful predictive insights. Their focus on bias is also, I think, peripheral if they want to do things that are bigger in education in relation to AI.

Having built one of the largest online HE institutions in the world he has helped advance the cause of online education and Degrees. Many institutions now consider this a key strategic goal, having seen it work at SNHU, Western Governors and elsewhere. He continues to inform and influence the sector through his work on using AI to accelerate progress in the sector.

Bibliography

LeBlanc, P., 2022. Broken: How our social systems are failing us and how we can fix them. BenBella Books.

LeBlanc, P., 2021. Students First: Equity, Access, and Opportunity in Higher Education. Harvard Education Press.

AI University?

Tertiary Education in the UK needs a fresh idea. Now that we have a Labour Government what we need is an initiative on the same scale as The Open University, kicked off over 50 years ago. It revitalised UK HE. It was not a threat to the existing Universities. 

We need is something new and additive -  a University that uses AI to create and deliver high quality online education at relatively low cost. We need a Harold Wilson and Jennie Lee moment from Starmer and Jaqui Smith.

The Labour Manifesto states:

"We will ensure our industrial strategy supports the development of the Artificial Intelligence (AI) sector, removes planning barriers to new datacentres. And we will create a National Data Library to bring together existing research programmes and help deliver data-driven public services, whilst maintaining strong safeguards and ensuring all of the public benefit."

It would mark a shift towards a future fuelled by accessible, high quality, relevant, low cost education. I presented this idea at The Open University earlier this year. We now have the opportinity to turn it into something substantial.

Surely an AI University could come from aLabour Government, along the lines of Sperling in the early days, Michael Crow at Arizona State, Paul LeBlanc’s transformative results at SNHU or Ashok Goel’s vision of an AI University at Georgia Tech? It is clear that an educational vision is needed and I think the best starting point is that outlined and executed by Paul LeBlanc at SNHU. It is substantial, well articulated and has worked in what has become the largest University in the US.

Manifesto

It would be based on the competence model, with a focus on skills shortages. Here's a starter with 25 ideas, a manifesto of sorts, based on lessons learnt from other successful models:

1. Non-traditional students in terms of age and background

2. Focus on critical skills shortages (nursing, teaching etc)

3. Quick and easy application process

4. Intake at any time

5. Focus on Generative learning using AI

6. Personalised learning using AI

7. Multimodal from the start - more focus on orality

8. Full range of summarisation, create self-assessment, dialogue tools etc

9. Every teacher has a chatbot available 24/7 for learner support

10. Teaching personalised

11. Teaching at all levels

12. Full accessibility features delivered by AI 13. Teaching in many languages aided by AI

14. Online assessment when ready

15. Every student has a 'Digital Twin'

16. Lectures recorded with GenAI activities automatically generated

17. Automated feedback and assessment

18. Learning journeys optimised

19. Complete at your own pace

20. Make sure AI part of curriculum

21. Data-driven approach with privacy ensured 22. Low admin, high 24/7 teaching and learning environment

23. Use part of apprenticeship levy

24. Get Big Tech to agree on proxy tax to fund

25. Non-residential, based in the North

We know it will face opposition but our political class must get out of supporting the elitism that the current system promotes. We have the advantage of a great track record in HE, English as our teaching language and, as I said, we've done this before.

Our successful precedent for this is The Open University and we can learn from what happened there.

The Open University

The key players behind the creation of The UK Open University were UK Prime Minister Harold Wilson and the first ever Minister for the Arts Jennie Lee.

Wilson was Prime Minister of the United Kingdom from 1964 to 1970 and again from 1974 to 1976. Wilson and his Labour government are credited with founding The Open University, a revolutionary idea to make higher education accessible to a wider population.

Jennie Lee, who was appointed as the first Minister for the Arts and oversaw the establishment of the university, also played a significant role. Lee provided the political drive and determination to ensure the project’s success, navigating through skepticism and opposition. You need this political sponsorship.

White Paper

Wilson envisioned a university of the air, an institution that would utilise television and radio broadcasts to provide education to those who were unable to attend traditional universities due to various constraints like work, family, or distance. The idea was to make higher education accessible to all, irrespective of background or circumstances, reflecting a broader commitment to social justice and educational opportunity.

Lee’s white paper, presented in 1966, laid out the vision and operational plan for what would become The Open University. It outlined plans for the university which would deliver courses by correspondence and through the use of technology, such as television and radio, to broadcast its courses, thus linking it to the technological revolution of the time. The paper was part of a broader initiative to modernise British society, enhance the competitiveness of the economy, and promote greater equality of opportunity and social mobility. It was a pioneering effort to expand higher education beyond traditional boundaries and to utilize contemporary media in a way that had not been done before in the realm of education.

We need a similar strong White Paper idea with 21st century technology, not radio and TV, but multimodal AI. The full array of courses (generate in part by AI), delivered partly by AI, assessed by AI should be the aim.

Opposition

Jennie Lee faced widespread skepticism and opposition from various quarters in her pursuit to establish The Open University. The idea of a ‘University of the Air’ that would reach out to those previously denied the opportunity to study was met with resistance. Skepticism and opposition came from within the Labour Party, including senior officials in the Department of Education and Science (DES), her departmental boss Anthony Crosland, the Treasury, ministerial colleagues like Richard Crossman, and commercial broadcasters. Despite the challenges, The Open University was realized thanks to Lee’s unwavering determination, the support of Prime Minister Harold Wilson, and initially modest anticipated costs. The true, much higher costs only became apparent later, by which time the project had gained too much momentum to be discontinued

The traditional universities were skeptical, even opposed to the idea of The Open University. They had concerns about the quality of education that could be delivered through distance learning and the use of media like television and radio. There was apprehension that an open admissions policy could dilute academic standards. Moreover, traditional institutions might have seen the establishment of a new university that challenged conventional norms as a threat to their established educational models and possibly their funding and enrolment. However, The Open University proved its merit over time by achieving high academic standards and gaining a solid reputation, which helped to alleviate many of these concerns.

Expect such opposition but understand that it gets us out of the obsession with the negatives around AI and moves us as a nation towards the positives. The DfE and traditional Universities need to recognise that they are too slow, expensive and old-fashioned to deliver for 21st century skills.

Influence

The Open University is still a positive force in Higher Education but has some serious setbacks, including a £20 million failed investment in trying to enter the US and Futurelearn, a MOOC company that failed to realise its initial promise, held back by traditional BBC appointments with little business, online technology or educational experience

Yet numerous open universities around the world were influenced by the model of The Open University in the UK. The success of this institution demonstrated that distance learning could be both reputable and accessible, leading to the establishment of similar universities globally. Some examples include:

The Open University of Israel, 1974.

FernUniversität in Hagen, Germany, 1974.

The Open University of the Netherlands, 1984.

Indira Gandhi National Open University (IGNOU) in India, 1985.

The Open University of China, originally established as China Central Radio & TV University in 1979.

Athabasca University in Canada, 1970, which shifted to an open university model following the UK's example.

Universidad Nacional de Educación a Distancia (UNED) in Spain, 1972.

We could have the same influence again.

Library at Alexandria 

There is a much older precursor that can help shape this vision - the Library at Alexandria, which I regard as the first University. Unlike the earlier Plato's Academy and Aristotle's Lyceum Alexandria was a free, open and secular entity. There was no faculty, just a Chief librarian and catalogers. Neither was there any formal teaching, mostly debate and discussion. Researchers as teachers came in the 18th century with the Prussians and Humboldt.

It  proved to be the most powerful generator of ideas and inventions for 600 years, until scholastic religious forces banned the books and the shelves were emptied - it was never burnet down. I have explained the lessons we can learn from this elsewhere.

Educational vision

As for the vision, it can be found way back in the in the Universal Education of Comenius and more recently with Paul LeBlancs ideas and real example at SNHU. That can be found here. That same Alexandrian vision is to be found in his book Students First and his work in building the biggest University in the US. Our current system is far from this vision. Anorger useful precursor was John Sperling and to a lesser degree Michael Crowe at Arizona State. 

Conclusion

There is a sense of our old institutions having become cumbersome and sclerotic. At such advantage then lies with alternatives. These new, agile alternatives can do things faster, better and at lower costs. This is precisely what the evidence already shows for artificial intelligence. Unencumbered by slow, dull lectures, free from the tyranny of location, huge amounts of physical, campus real estate with low occupancy rates, expensive housing robbing others from such opportunities. Also free from the tyranny of time, the scheduled lectured, fixed end of year assessments and slow delivery punctuated by interminable holiday periods, it will thrive. Less detached from the real world it may also rebalance the drift towards overly-theoretical, text-only based learning towards badly needed skills. Education should be for both life and living.

Importantly, the current OU will not and cannot do this. They have developed a negative mindset towards this technology and are now more of a traditional university. It is too old-fashioned and sluggish to do this well. They have shown no signs of effort in this direction.

It is all about scale. The goals of reaching nontraditional students at low cost demand good tech with the sort of scalability that OpenAI and other providers have already delivered. It is AI that should be the catalyst here where the possibility of personalised teaching and learning in any subject, at any time, to any place , at any level 24/7, in almost any language, can be realised. the fact that ChatGPT has already been embraced by 100 million with billions of dialogues, mostly bu curious people who want to learn, should be our guiding light.

It is fine to say, it's not the technology but the vision that counts. I agree but when it comes to getting things done, building platforms and delivering, you need a sense of urgency and catalysts, not vision reports. We have plenty of them. And if you want a vision it is well articulated by Paul LeBlanc in Students First (2021). He also did it!

You need a new team that wants to transcend the tradtional thin diet of lectures plus essays, adopting a more supportive learning environment. Fresh blood, fresh ideas, fresh pedagogy and fresh forms of delivery. Being bold, it could even have no building or place. I don't see Duolingo, Khanmigo, Wikipedia or Google Scholar as being 'places'. The trick is, perhaps, to free learning from the tyranny of time and place.

I know of great institutions that have adopted the philosophy of open admission, flexible distance learning, and the use of technology to provide education to those who otherwise might not have access to it. They have played a significant role in expanding higher education and continue to impact lifelong learning across the globe. We could surely do this again. No massive campus costs with low occupancy rate buildings, all online, no travel to be in line with climate change demands and an exemplar once more for the rest of the world. Even better, base it in the North, that has long sustained a brilliant set of Universities.

Babbage - genius but of little causal significance in history of computers

Charles Babbage (1791 - 1871) was a colourful British mathematician, inventor, and mechanical engineer. He made significant contributions to the field of computing through his pioneering work on the design of mechanical computers. 

A mathematician of great stature, he held the position at Cambridge held by Newton and received substantial Government funds to build a calculating Differential Engine, funds he used to go further to develop an Analytical Engine, more of a computer than calculator.

 

Machine to mind

For the first time we see,  albeit still mechanical product of the Industrial Revolution, the move from machine to mind. Babbage saw that human calculations are often full of errors and speculated whether steam could be used to do such calculations. This led to his lifetime focus on building such a machine.

Babbage had been given government money to conceive and develop a mechanical computing device, the Difference Engine. He designed it in the early 1820s and it was meant to automate the calculation of mathematical tables, basically a sophisticated calculator, hat used repeated addition. He did, in fact, go on to design a superior Analytical Engine, a far more complex machine that shifted its functionality from calculation to computation. Conceived by him in 1834, it was the first programmable, general-purpose computational engine and embodies almost all logical features of a modern computer. Although a mechanical computer, it features an arithmetic logic unit, control flow with conditional branching, and memory, what he called the ‘store’. We should note that it is decimal but not binary but it could automatically execute computations. 

Although this is an astonishing achievement, neither were fully built during his lifetime, Babbage's design laid the groundwork for future developments in computing. His son Henry Babbage did build a part of his differential engine as a trial piece and it was completed before Babbage's death in 1871. A full Differential Engine was built in 1991 from materials that were available at the time and to tolerances achievable at the time. It weighs in at 5 tons, with 8000 moving parts. Both can be seen in the Science Museum in London.

Critique

Babbage was a prickly character who alienated many, especially in Government, who generously funded his work. He alienated the government and in a tale that has been common in UK computing, never turned from theory into practice. Those developments eventually came from the US. In the end he failed but from the drawings alone, Lady Byron called it a ‘thinking machine’ and Ada Lovelace, her daughter, asked for the ‘blueprints’ and became fascinated by the design and its potential.

Influence

Although described as the ‘father of computers’, there is no direct, causal influence between Babbage and the development of the modern compuer. Babbage’s designs were not studied until the 1970s, so Babbage’s designs could not have been the direct descendants of the modern computer. It was the pioneers of electronic computers in the 1940s that were the true progenitors of modern computers. There is a much stronger case made for the idea that it was Holerith and his census machines that had the real causal effect.

 

Bibliography
Swade, D. and Babbage, C., 2001. Difference engine: Charles Babbage and the quest to build the First Computer. Viking Penguin.

Hyman, A., 1985. Charles Babbage: Pioneer of the computer. Princeton University Press.

In Our Time, Ada Lovelace, featuring Patricia Fara, Senior Tutor at Clare College, Cambridge; Doron Swade, Visiting Professor in the History of Computing at Portsmouth University; John Fuegi, Visiting Professor in Biography at Kingston University.

 https://www.bbc.co.uk/sounds/play/b0092j0x

Ada Lovelace - insightful but full of surprises...

Ada Lovelace (1815 -1852) died at the age of 36 but had significant insights into computer science. She was the daughter of the poet Lord and Lady Byron but her parents parted only weeks after her birth. Her mother was interested in mathematics, also social movements, and established a series of schools and helped establish the University of London. It was she who ensured that Ada got a good, disciplined education in both science and mathematics. 

After both attended a Babbage soiree in London, where her mother described Babbage’s engine as a ‘thinking machine’, they both went on a tour round the Midlands where they saw the Jacquard Loom. This was to inspire a series of thoughts in the form of notes from Ada on the potential of the Analytical Engine that Babbage had invented.

The mathematician Hannah Fry describes Ada as intelligent but also “manipulative and aggressive, a drug addict, a gambler and an adulteress!”

Analytical engine

Ada then collaborated closely with the mathematician and inventor, Charles Babbage, who invented what some regard as the first modern computer - his Analytical Engine. This resulted in her translation from French to English, of an article written by the Italian mathematician Luigi Federico Menabrea (future Prime Minister), about Babbage's Analytical Engine, where she added extensive notes and annotations. These notes were three times as long as the original essay and were published in Scientific Memoirs Selected from the Transactions of Foreign Academies of Science and Learned Societies in 1843 and contained some seminal ideas on computing.

Programming

In these notes she described the potential for machines to perform operations beyond simple arithmetic calculations. In one of her notes, she described an algorithm for calculating Bernoulli numbers, which is considered, by some, to be the world's first computer program, although doubt has been cast on this by recent scholarship. It is a detailed and tabulated set of sequential instructions that could be input into the Analytical Engine. This demonstrated her understanding of how machines could be programmed to perform various tasks, a fundamental concept in computer science and AI. 

Insightful though her notes were, she was not a top flight mathematician and the supposed computer programme was really a sort of pseudocode with mathematical expressions. The claim that she wrote the first computer programme some regard as exaggerated and it was never executed on any machine as an actual programme. As the Babbage scholar, Doron Swade, who built the Babbage Analytical Engine, claims, the concept and principle of a computer programme for this machine was actually Babbage’s idea, as his notes of 1836/37 predate those of Lovelace, although her insights on computation beyond mathematics was absolutely original. 

From calculation to computation

The notes had the idea that instructions (programs) could be given to these machines to perform a wide range of tasks, making her a pioneer in the ‘concept’ of computer programming. Accomplished in embroidery, she describes the possibility of input through punched cards similar to the method used on the Jacquard loom. This loom was invented by Joseph-Marie Jacquard in the early 19th century and revolutionized the textile industry by allowing for the automated production of intricate patterns in fabrics. Punched cards were used for patterns, each hole being an on/off switch, one card per line in a column of sequenced cards, a technique used on mainframe computers in the 20th century.

Babbage saw his machines as dealing with numbers only. Lovelace saw that we could see such machines as not just doing calculation but also computation. Numbers can represent others things, representations of the world and she speculated that computers could be used to create outputs other than mathematics such as language, music and design. She understood that machines could be programmed to generate creative works. This anticipation of the creative potential of machines aligns with the field of generative AI, which focuses on developing algorithms that can produce creative content such as music, art, and literature, This was her main insight, although there is no direct causal influence between her work and these developments. 

Education and learning

Her views on education aligned with her own experiences and that of her mother Lady Byron. She received an extensive education in mathematics and science, which enabled her to mix with other intellectuals and practitioners in the field, making ground-breaking insights to the field of computing. She was an advocate for the intellectual and educational development of women and believed in providing women with opportunities for education in mathematics and the sciences, which was uncommon at that time. Lovelace's passion for learning and her advocacy for education for all, regardless of gender, continue to inspire educators and learners today.

 

Critique

Her role in inventing either the idea of computer programming and the first computer programme seems o have been quashed. The said programme was, of course, never used in the Analytical Engine, as it was never turned into actual code and the Analytical Engine was never built.

There was no real causal influence here on modern computing, no real continuity between Lovelace and modern computing. This is an ad hoc legend rather than a matter of history. Turing read her notes and admired her insights, and although one can argue that came through Turing, who was in Bletchley Park, and that she influenced the Colossus machine, which decoded German scripts, the causal link is tenuous and unproven. There is no direct, causal trail to modern compuers, even through Babbage, as Babbage’s designs were not studied until the 1970s, so Babbage’s ideas were not actually the direct descendants of the modern computer. It was the pioneers of electronic computers in the 1940s that were the true progenitors of modern computers.

Influence

Ultimately, says Hannah Fry, her contribution was in seeing that computation was more than calculation, yet “Her work… had no tangible impact on the world whatsoever.” Nevertheless, Lovelace's passion for learning and her advocacy for education for all, regardless of gender, continue to inspire educators and learners today. The Ada Lovelace Institute in the UK is a good example of this legacy and a dozen biographies were published on the 200th anniversary of her birth in 2015.

Bibliography

Notes https://maa.org/press/periodicals/convergence/mathematical-treasure-ada-lovelaces-notes-on-the-analytic-engine

Hollings, C., Martin, U. and Rice, A.C., 2018. Ada Lovelace: The making of a computer scientist (p. 2018). Oxford: Bodleian Library.

In Our Time, Ada Lovelace, featuring Patricia Fara, Senior Tutor at Clare College, Cambridge; Doron Swade, Visiting Professor in the History of Computing at Portsmouth University; John Fuegi, Visiting Professor in Biography at Kingston University.

 https://www.bbc.co.uk/sounds/play/b0092j0x

Hannah Fry https://www.bbc.co.uk/programmes/articles/3jNQLTMrPlYGTBn0WV6M2MS/not-your-typical-role-model-ada-lovelace-the-19th-century-programmer?ns_mchannel=social&ns_campaign=bbc_radio_4&ns_source=facebook&ns_linkname=radio_and_music