Generative AI - my sense of wonder and hope

I feel an absolute sense of wonder at Generative AI. It just seems so surprising that something so simple does things so extraordinary. Letting a mathematical model ‘learn’ by feeding it data, then seeing it create text, images, audio and video is almost sinfully magical. We have seen nothing like this in the history of our species. We are no longer search and retrieving knowledge, we are having a dialogue with knowledge, with something that has its roots in the collaborative hive mind, that tirelessly and politely communicates with us in many voice we choose and creates every new word and image.

I’ve waited my whole life for this. All my adult life has been spent in using technology to do things that humans do, like teach, heal, dispense advice, manage and so on.  For years, going back literally 45 years to Dartmouth College where I first came across AI (it was the home of the modern era of AI), coming back and buying a home computer, my attempts at an intelligent tutor in the late 90s, then putting all my energies into companies and ideas that were heading in this direction since 2005; Learning Pool, then CogBooks, WildFire. I wrote a book in 2020 that said it was coming – soon. And it did – big time.

 

This is not a clickbait fad, it is a profound moment in time, a moment that changes the trajectory of our species. For the first time in my life I see on the horizon, a Universal teacher and Universal doctor, and other useful advisors htat will inform, educated, manage and heal us. In particular, it will dramatically reduce the cost of education and healthcare.  

 

It is clear that once AI embodies great pedagogy, learning science and teaching practice, the Universal Teacher that can teach in any subject at any level, at any time anywhere in any language. Why? Because it can learn these things. What marks this technology apart from all others is its ability to learn and that’s why it will eventually transcend our current model. We will all learn faster because it will learn to teach us. Teaching is a means to an end – that end is learning. If that end can be done in a way that is much cheaper and doesn’t condemn many to failure or keep people trapped and excluded from the world in expensive institutions in long-winded courses, for up to twenty years, then why not?

 

It is also clear that the Universal Doctor is also on the Horizon. As soon as current misdiagnosis rates are undercut by AI, this will happen. It will happen because lives will be saved. It will also learn to do investigative work on available data, blood tests and scans. On top of this we have seen with Alphafold how advances in research and medicines is already being accelerated by AI. Medical practice is a means to an end the health of us all and as patients when we get ill. If that can be achieved at hugely decreased costs, with better diagnosis, investigation and treatment, less suffering, then bring it on.

 

These two benefits alone are why we must calm down on the eschatological doomsaying. It is easy in the comfort of the developed world to want these two things to slow down but of you are in a classroom with no roof in a class of 50 plus and a teacher with little training and a paltry salary, you may thing differently. And of you live in a country where there’s one doctor for up to 10,000 people you may see this as a change to not just improve but save your life.

 

There is no end of people not knowing how to live their own lives, telling everyone else how to live theirs... the moralisers, ethicists, activists. They would rather impose their will on others than realise the benefits of AI. It was always thus, with printing, radio, television, rock ‘n roll, internet, Wikipedia, smartphones, videogames, social media, bioengineering, GM crops, 5G now AI. The usual suspects want to own knowledge and communications; academe, mainstream media, politicians, professional institutions. They have a lot to lose but the the rest of us have a lot to gain with cheaper more productive systems. In truth it is no longer in their grasp. They’d ban it in the blink of an eye if it wasn’t that damn useful.

Africa is not a place and doesn’t need white saviours in learning

Africa is a vast, varied and vexing place but there is no essential thing that is Africa. Dipo Faloyn, the Nigerian writer, sums it up in his very personal book ‘Africa Is Not a Country’. Forget the images you see on TV of safaris, wars, child soldiers and famine, as most of that is through the eyes of visitors who stay in comfortable hotels, working for organisations that have a saviour agenda. There are countries north of the Sahara that are very different from those immediately below. Egypt, Nigeria and South Africa are radically different places at the top, middle and bottom of Africa with substantial economies. There are many Africas, there is no ‘Africa’.

 

I’m just back from Senegal, the host country this year for E-learning Africa and have been to this event many times. It remains the defining Learning Technology event in that continent. The brainchild of Rebecca Stromeyer, it goes way beyond the normal conference format, lively, intense and friendly networking along with a session for Education Ministers from all over Africa and a ‘challenging’ debate, which I’ve participated in several times.

 

You can’t shy away from the chaos when travelling in Senegal. One very large group turned up to a Hotel that did not exist. They had been scammed. We turned up at 2am to our hotel and found that, despite it having been booked and paid for, they had sold on our room and we had to find another. Walking around Dakar at 3.30 am looking for a taxi is no fun, especially when every taxi driver we used required Kissinger-level negotiations on price. On one occasion, having agreed a generous fare, one driver turned round and claimed that the fare was per person! My son returned to his room one night and found someone else in his Hotel bed. The lack of organisational cohesion and training is tangible. On the whole the people are wonderful and chilled but I do0n;t want to varnish over this stuff as there is no way it can develop successful tourism with this level of chaos and hassle.

 

I have travelled a lot and never seen so many police, even in and around the conference.

Also never stepped through so many scanners. Every building has one, yet despite me setting of the red lights and bleeps they just wave you through, many are not looking at the scanner screen at all, bored and on their smartphones. They are experiencing political unrest as we speak, with street protests and a Ministerial building being burned after the opposition leader Ousmane Sonko was arrested on drummed-up sexual assault charges. We were stopped at police roadblocks twice and on the way to the airport we saw people being made to lie down on the road in a massive traffic holdup. 

 

There is lots of equipment here but little training, so nothing really works and when it breaks it is catastrophic. Let me give you an example. The flight from Senegal was late which meant we couldn’t make our connection. The TAP (Air Portugal ) office at the airport had someone who quite simply couldn’t be bothered helping. She sat there arrogantly waving us away with her hand then asking that we pay for another flight! We confirmed in Lisbon that she could have arranged our second leg. The problem is deep rooted nepotism. She has this job but can’t operate the computer, so just sits there taking the salary. I can’t tell you how many times we had our credit cards declined because the signal or card machine didn’t work.

 

You have to wrestle with these contradictions. The fundamental fact is that this is a rich continent full of poor people. It is easy to blame colonialism and see everyone as a victim, but there is obvious money around. I saw a blue Bentley outside of the restaurant we were eating in with an old man buying Moet for six women at the table and huge mansions lining the coast. There is wealth here, with huge sums at the peak of the pyramid, a tiny middle class and a vast number who remain poor. The cash is being spent on vanity projects and a vast army and police force who keep the oligarchy in power.

 

Ok so that is the economic and political backdrop. As I was here to deliver some sessions on AI, how do you position the technology thing? 

 

The white saviour complex sees ‘others’ as the only hope for these hapless Africans. Yet it is the white saviour complex that caricatures them as unable to help themselves, people to be saved from themselves. As I walked through the real market in Dakar, not the ones selling beads back to Western tourists ( a neat and startling reversal), I saw a highly entrepreneurial  society. A lorry load of old spare parts were being dumped out onto the road, while people bid for the spares. Recycling is a necessity here. This is not a functionally helpless society, it is a society held back by its own leaders, who strip it bare. The tech kids I meet here are highly entrepreneurial and will move things forward. It is the anti-business, keep them on a NGO ride that will stop Africa. NGOs are band-aids, not the answer. I feel as though, since the 1980s it is the NGOs who seem to be full of colonial rhetoric, the new institutional colonialism. The Geldof inspired ‘Do they know it’s Christmas?’ is perhaps the most condescending song title in history. Africa has more Christians than any other continent, who maybe knew it was Christmas, it also has  450 million Muslims – wonder how they felt? The hideous, theatrical spectacle of celebrity activists grew out of that time and the simplistic rhetoric of binaries continues, northern hemisphere-southern hemisphere, rich-poor, white-black, evil-good.

 

In any case, we’re not here as tourists, we’re here to get something done. Education and health remain problematic in all poor countries, especially countries like Senegal, where the politicians are clearly rapacious and corrupt. I was the only person in the hall who refused to stand for the Prime Minister after we had waited over and hour and a half for him to grace us with his banal presence. Being here as a white saviour was not my intention. I have been here before in the African Congress building in Addis, Ethiopia, debating that Africa needs vocational skills, not abstract University degrees – we won. I have always supported the spread of free tools and learning on the internet to get to the poor. This has happened with finance, it can happen with education. The infrastructure problem is being solved, via satellites and ever-cheaper smartphones. Sure the digital divide exists but constantly painting a picture of a glass half empty while it is being filled is another example of white saviour behaviour.

 

Michael, my partner in crime in the debate, from Kenya, does something about the problems through his ‘4gotten Bottomillions’ (4BM), Kenya's largest and most trusted WhatsApp Platform, connects the unqualified and poor to real work and jobs. He is critical of white saviour attacks on US tech companies who pay for data work in Kenya. This, for him, is rich white virtue signalling denying poor Africans a living. These companies pay above the minimum wage and the work is seen as lucrative. Michael wants to empower Africans, not disempower them through pity. His message to everyone is to suck it up and get on with things, not to wait on grants and NGO benefactors. Until that happens, Africa will remain in psychological chains to others.

 

My first event was a three hour workshop on AI, as an astounding technology that really can deliver in education and health. It is already being used by hundreds of millions worldwide to increase productivity and deliver smart advice, support and learning free of charge. This is exactly what Africa needs. Let the young use it, don’t cut Africa out of the progress as that will turn an entire continent into North Korea and Iran, the two countries that banned it (even Italy saw the error of its ways). 

 

I did another session on online language learning where the presenters were asking young Africans to learn and get tested on, either English or French. French is the, they claimed, the language of culture. Yes – but whose culture? The French. My position was that Generative AI already delivers in over 100 languages, shortly moving towards 1000, many of them African. We have the chance of having a universal teacher on any subject, with strong tutoring and teaching skills available to anyone, anywhere with an internet connection, for free, in any language.

The solution is to get this fantastic, free and powerful educational software to everywhere, not just Africa. Stop using specious arguments about northern hemisphere bias and footnote ethical concerns to stop action. Let Africans decide what's good for them and allow Africa to use AI in the local context. Getting this stuff into the hands of the young and poor is the answer.

 

My final event was in the Big Debate, on whether ‘AI will do more harm than good in Africa. Michael and I lost. Why? The audience was largely either the donors or receivers of white saviour aid. They do this for a living and anything that threatens to bypass them and go straight to the working poor is seen as a threat. They need to be in control, seeing themselves as providing educational largesse. You get this a lot at Learning Technology conferences, people who fundamentally don’t like technology.

I'm well aware that the sermon has always been the favoured propaganda method of the saviour and see that I too am part of the problem at such events. But the lack of honesty about the saviour-complex has become a real problem. My hope os that this powerful technology bypasses the oligarchies, saviours and gets into the hands of the people that matter.

The opposition played the usual cards. First the argument from authority, which I always find disturbing, ‘I’m a Professor, therefore I’m right, you’re wrong’. My friend Mark, on the opposition side gave an eloquent speech brutally attacking the culture of screens and smartphones. I was sitting behind the lectern and noticed that he had read the entire speech, every last word, verbatim, from, guess what… a smartphone! It’s OK for rich white people to use a full stack of tech but not Africa. I rest my case.

AI in Africa...

I’m in Africa because it’s important to be in a place that gives you perspective. All the usual stuff when you travel, arrive at hotel to find there is no room, wandering about Dakar finding a taxi to a new hotel, being stopped by the police! In any case, we’re in this amazing city and you have to go with the flow.

E-learning Africa, run by the irrepressible Rebecca Stromeyer and her team, have been doing this for many years, because they think it matters. It’s easy to go to these big conferences in Europe and the US but what you get is a cyclopic view of the world, where we assume that everyone works in offices or at home on Zoom. The interest in the topic at our workshop was intense and I'm in a session on AI for language learning. as well as a huge formal debate on the role fo AI in Africa.

 

At this conference in these places, you are faced with the realty the rest of the world. Having been to Uganda, Rwanda, Namibia, Ethiopia and this year, in Senegal. It’s a big world out there and the worst conferences I’ve attended have been in two of the more hideous places on our planet Orlando and Vegas, the first seems like America embalmed, the second some sort of theme park from Hell. Come to Africa and see real unvarnished world.

 

When this new Age of AI struck like lightening in November 2022, I had, for years been extoling the possibilities of its massive impact on learning and health. That impact is not about the already wealthy, the graduate generation in the Northern Hemisphere, it’s about the rest of the world. From the holy pronouncements on ethics and AI from Brussels, you’d think they’d have a little more humility, seeing as well under 10% of the world’s population. The US is only 4.25%.  They both need to get out more.

 

When you have 1 teacher and classes of 50, or 1 doctor for 10,000 people, you have a different perspective on life. Technology matters more here, which is why Africa leapfrogged the rest of the world on mobile – it mattered when your precarious employment depends on that next text message or that your fragile finances need to be executed efficiently and cheaply from the rural world in which you live.

 

I do believe that AI technology is finally the technology that franchises almost everyone. It has the ability to raise productivity to pay for the education and healthcare globally. It can deliver the sort of Universal Teacher and Universal Doctor technology that is finally on the horizon. A teachers that knows more than any teacher, in any subject, has the pedagogic knowledge built into its methods, that is tirelessly friendly and supportive, 24/7, anywhere in the world.

 

But what really matters is delivery in all languages. We live with the legacy of colonial languages. In Africa they are English and French, in South America Spanish and Portuguese. Even in the rest of the word, English has become the lingua franca. Imagine a technology that can teach and deliver healthcare in any first language. Here's the good news. That too is on the horizon. Not only is a wide range of language available from the get go, that number has and is expanding into the thousands. Far from ignoring minority languages it may save them. Generative AI is a modern Babel.

 

Generative AI through ChatGPT was launched in 95 languages. allowing one to, write, summarise, error check and translate between these languages. That is astonishing but that number has grown to include many more. This has been one of the most used functions with significant increases in productivity. It was also launched knowing 12 computer languages, allowing translation between them. One amazing feature of Generative AI that slipped under the radar was its astounding capabilities in speech to text and text to speech. Whisper from OpenAI was world beating and free. What a start.

 

On the day I flew to Senegal, Yann Lecun announced a new Multilingual Language Model that works in 4000 languages! There are only 7000 languages in the world, so that’s more than a great start, it’s into majority territory. Early days but the promise is now here, that AI will open up education, health and other areas of human endeavour to everyone, no matter where they live or what language they speak. Once we can all understand each other in real time translation, perhaps we have a chance of better understanding and co-existing with each other.

 

This really matters, as technology tends to be skewed towards English in particular but also the usual suspects of largely northern hemisphere languages. If we are to see AI as a genuinely global and liberating technology languages do matter. What these large language models have shown us is that language really does matter in learning. Wittgenstein and Vygotsky were right, it is fundamental to learning and intelligence, fundamental to being human. It is not that intelligence produces language but that language produces intelligence.

 

So let’s not allow the over-developed world get on their moral high-horses and hold back the technology that promises so much to so many. A vast army of amateur ethicists (anyone can be one by simply saying they are one) seem determined to hold us back with so much verbiage on the subject that you could train a Large Language Model on that alone. Unfortunately it would an aloof, finger-wagging, moralising bore.

 

In truth, for those most in need, who often find themselves furthest away from education and healthcare, its power and reach are potentially immense.

21st Century Skills – the last refuge of human exceptionalism!

As Nick Shackleton-Jones rightly points out, the rhetoric around AI and automation for the last two decades was “don’t worry - AI & automation will only tackle the routine jobs”. The whole narrative was owned by the professional classes and centred around workers losing their jobs, not the graduate class working at home on Zoom. Nick reminded us on “how quickly we forget and how utterly wrong we were”. The 21st century skills rhetoric has become the last refuge of human exceptionalism, the topic I covered in my last blog.

 

Stages of 21st C grief

Predictably, the professional class, especially learning professionals, go through their five stages of grief, when faced with new technology: 

 

Denial – bewildered they want to ridicule it through clickbait examples of how bad it is, then say block it and ban it

Anger – sabre rattling stage where mostly 3rd rate academics write open letters, warn people of the dangers which only ‘they’ perceive

Bargaining - form ethics committees, write frameworks and generally do their report writing thing

Depression – oh dear we do have to actually learn to work with this stuff

Acceptance – alright, I give in

 

In denial

We’re still in the ‘denial stage. Taking the usual refuge in the rhetoric of vague 21st C skills. But the mistake is to:

1) think these skills are new
2) think we have a clear definition

2) think of them as skills in themselves, as they mostly need domain knowledge 

2) assume that educators have these skills, even if they did exist in a clean form

3) assume that they know how to teach these skills

4) assume that they are wholly unique to we humans

 

The whole thing goes back to the late 1990s, when we were faced with a new millennium. but a key document was  "Partnership for 21st Century Skills," published in 2002 by a coalition of education organizations in the US. This report put great emphasis  on the ‘c’ words and saw them as essential to prepare students for the challenges and opportunities of the 21st century. 

 

Then the bandwagon, report-writing brigade, organizations such as the Organization for Economic Cooperation and Development (OECD) and the World Economic Forum (WEF) began pumping out the rhetoric highlighting the significance of 21st-century skills in their discussions on education and workforce readiness.

 

Nothing new

Mirjam Neelen & Paul A. Kirschner rightfully pointed out, in their excellent blog ‘21st Century Skills don't exist. So why do we need them? (2016) that there is nothing new about these skills, nothing 21st century about them. This was all laid out in the first major counter attack in 2010 from Rotherham & Willingham’s “21st-Century skills. Not new, but a worthy challenge.” I have been making the same point for the last 20 years.

 

Problem of definition

As they pointed out, the first problem, as expected, is definition. They often appear as a list of five or so things on PowerPoint, most starting with the letter ‘C’; communications, collaboration, critical thinking, creativity… then it tails off into the vagueness of problem solving and innovation. There are literally dozens of lists and variations.

 

Skills are notoriously difficult to pin down as they are not, as Bloom suggested represented in some simplistic pyramid They are complex sets of personality traits, motivations, learned knowledge and behaviour – it’s the sum of lots of integrated parts that results in an ability, competence or performance. The fact that it is complicated, mostly involving domain knowledge, makes these very vague 21st C skills difficult to extract as a separate ‘skill’. It is doubtful whether they can be abstracted from domain knowledge at all. Knowledge is marbled like fat into the meat of skills.

 

This 21st C agenda can also be quite dangerous. It can harm learners, especially the disadvantaged by giving them the illusion of progress. Knowledge and skills are not separable like oil and water, they are deeply entwined, so most critical thinking and problem solving requires a depth of knowledge. The 21st C skills agenda led to a hideous focus on ‘Leadership’ in L&D. We assumed that skills were intellectual in some way, so introduced this elitist, hierarchical training, at the expense of real skills and competences for all. We became exclusive not inclusive. That did not go well, as productivity did not increase and although we have lots of rhetoric and training on Leadership, we seem to have precious little of it.

 

AI hits the fan

We’re 23% into the 21st C and they have the last bastion of human exceptionalism (see last blog). The 21st C skills mantra, that has now turned into an empty trope in the Age of AI, especially among educators and learning professionals who needed a serious sounding phrase to rattle around in conferences and reports. It is usually to be found in some overlong, text-heavy Powerpoint presentation at a conference, accompanied by a cocktail of 'C' words  - communication,, collaboration, creativity, critical thinking. Can the real world really be that alliterative?

 

Communication

Young people communicate every few minutes – it’s an obsession. They text, message, chat, post, comment, whatsapp, use Instagram, Facebook, Tik Tok and tools you're you may never to have heard of in various media, including images and video. Note the absence of email and Twitter, the only place you’re likely to hear of 21st C skills. This generation grew up in the 21st C. Never have so many communicated so often with so many. We readily forget that within a few generations, we have moved from letters to the telegraph, then telephone and now free communications, in a bewildering array of types and formats to almost anyone on the planet – for free. Technology not only increases the possibilities in communications, also the skills, through use.

 

In fact, one of the features of Generative AI, such as ChatGPT, is in showing us how poor our communications’ skills can be. It invariably improves almost anything we want to write and send.

 

Collaboration

There’s an abundance of collaboration online, where we share, tag, upload and download experiences, comments, photographs, video, media and now generative AI tools. We collaborate closely in teams, often international, when playing computer games and in the workplace. Never have we shared so much, so often, in so many different ways.

 

Then along comes someone who wants to teach this collaboration as a 21st C skill, usually in a classroom, where much of this technology is banned, ignoring how collaboration works in the real world. I’m hugely amused at this conceit, that we adults, especially in education, think we always have these skills. There is no area of human endeavour that is less collaborative than education. Teaching and lecturing are largely lone-wolf activities in classrooms. Schools, colleges and Universities share little. Educational professionals are deeply suspicious of anything produced outside of their classroom or their institution. The culture of NIH (Not Invented Here) is endemic. Many educators far from being consistently collaborative, are doggedly fixated with delivery by the individual.

 

With AI, especially generteive AI, collaboration at a global, human level, a huge, sophisticated and elegant collective model, trained on the sum of human knowledge. Generative AI is collaboration writ large, the whole of our collaborative cultural capital is being put to good use and available to the individual.

 

Critical thinking 

Critical thinking is the Little Bighorn of human exceptionalism, General Custer’s last stand. Academics, in particular, are fond of these two words, as they see themselves as having these skills in abundance. To be fair soft skills and empathy is not their strong point, so they need something to fall back on. 

 

One species of critical thinking is often well taught in good schools and universities, but only at the level of research, text and essays. As a practical skill it has all but disappeared in theory dominated courses.  It needs high quality teaching and the whole curriculum and system of assessment needs to adjust to this need. As Arun has shown, there is evidence that in our Universities, this is not happening. Arun (2011), in a study that tracked a nationally representative sample of more than 2,000 students, who entered 24 four-year colleges, showed that Universities were failing badly on the three skills they studied; critical thinking, complex reasoning and communications. This research, along with similar evidence, is laid out in their book Academically Adrift. Even research is now under threat as papers appear, where the hypothesis, data work, even the writing is being done by AI.

 

The idea that critical thinking is a uniquely human activity is being challenged by tools that do it better, whether in mathematics or other fields. It is now clear that the inference capabilities of AI will outclass humans and that we are no longer the sole producers of critical analysis. We saw how AI became highly competent at all human games. AI has already defined the 3D structure of the 200 million know proteins, a task that, according to Hassabis, the CEO of Deepmind, would have taken us a billion years using traditional human methods. It’s data analysis abilities have become superb and the growth of the inference side of AI is accelerating fast. We can expect current models top become good at this very quickly.

 

Creativity

The modern understanding of the artist as a unique individual with a distinct creative vision began to emerge during the Renaissance in Europe, strengthened when the Romantic conception of the artist emerged in the late 18th and early 19th centuries as a reaction to rationalism and industrialization. Romanticism placed a strong emphasis on individualism, emotion, imagination, and the awe-inspiring power of nature. Within this context, the creative artist was seen as a visionary figure, capable of tapping into the depths of human experience and expressing profound emotions and ideas. Technology, of course, has always been part of artistic production and expression, from the tools of painting to photography, synthesizers and other generative tools. The idea that art is some sort of divine spark has long gone. Suddenly, generative AI was winning photography competitions, producing music and being generally ‘creative’, another word that is difficult to define.

 

Can they be independently taught?

Of course, those who are most vociferous on the subject of 21st C skills are often those who tend to ‘write’ most about them but are the least skilled in them. Education, whether through lectures in Universities or one teacher to 30 plus kids in a classroom, is not the way to teach any of these skills, even if they did exists. Of course, education continued to scrap vocational learning, starve it of funding and push for even more abstract schooling and higher education. So called 21st C skills were fine for others but not them.

 

Isn’t all this talk of 21st C skills just a rather old, top-down, command and control idea – that we know what’s best for others? Isn’t it just the old master-pupil model dressed up in new clothes? Do the pupils know a tad more about digital skills than the masters?

 

It is an illusion that these skills were ever, or even can be, taught at school. Teachers have enough on their plate without being given this burden. I’ve seen no evidence that teachers have the disposition, or training, to teach these skills. In fact, in universities, I’d argue that smart, highly analytic, research-driven academics tend, in my experience, often to have low skills in these areas. Formal educational environments are not the answer. Pushing rounded, sophisticated, informal skills into a square, subject-defined environment is not the answer. It is our schools and universities, not young people, who need to be dragged into the 21st century. The change will comes through mass adoption and practice, not formal education. 

 

Conclusion

There’s a brazen conceit here, that educators know, with certainty, that these are the chosen skills for the next 100 years. Are we simply fetishising the skills of the current management class? Was there a sudden break between these skills in the last century compared to this century? No. What’s changed is the need to understand the wider range of future possibilities and stop relying on human exceptionalism.

 

Bibliography

Mirjam Neelen & Paul A. Kirschner  ‘21ST CENTURY SKILLS DON’T EXIST. SO WHY DO WE NEED THEM?’ (2016). https://3starlearningexperiences.wordpress.com/2016/11/01/21st-century-skills-dont-exist-so-why-do-we-need-them/

Rotherham, A.J., & Willingham, D.T., (2010). “21st-Century” skills. Not new, but a worthy challenge. American Educator, 17-20. https://www.aft.org/sites/default/files/periodicals/RotherhamWillingham.pdf

Arum, R. and Roksa, J., (2011). Academically adrift: Limited learning on college campuses. University of Chicago Press. 

Human Exceptionalism – we need to get over ourselves

Human exceptionalism is the belief that we are unique. We evolved to see ourselves in this way. This extends to kinship with family, other social groups and our wider species Homo sapiens. Yet, since we started to create technology, especially writing, we have discovered, time and time again, through reflection and inquiry, that we are not as ‘exceptional’ as we assumed. 

Copernicus threw our little sphere out into the whirl of the solar system, de-anchoring us from our central place in the known universe. What we had evolved to see was the sun moving round the earth, so the alternative was counterintuitive and shocking. Yet symbolic writing – mathematics and data – was definitive on the matter. ‘Flat earther’ is now a pejorative term. 

 

Darwin then showed that a creator was not necessary for design and that we were just another animal, the product of a process of genetic accidents and selection. There was no designer only the ‘blind watchmaking’ of the evolutionary process. In fact, our cognitive, affective and psychomotor capabilities are limited by the evolutionary process – limited working memory, forgetting, cognitive biases, fallible and failing ling term memories, inability to network efficiently, sleep and death.

 

Physically, technology trounced us. It was thought we would die if we travelled by train, as we couldn’t handle the speed. Within 66 years of that first Wright Brother’s flight we had landed on the Moon. Physically we are generally weaker, slower and less accurate than the machines we create. That gap is widening. There is no place on earth we can’t get to, no physical task too big, as technology has extended our physical capabilities. Although, to be fair, the robot person, even self-driving car is still some way off. Strength and precision was not our strong point. It was mind. 

 

For a long time the focus was on robots and the mechanical side of AI, it turns out that the real advances in AI, were in the psychological domain. With this we turned to cognitive qualities, like critical thinking, collaboration and creativity. Surely technology couldn’t encroach on these unique skills? Once again, technology surprised us all – it can,

 

Productivity

Economies have seen significant slow-downs in productivity. This has puzzled many. AI as one of the few areas where increased productivity was cleat and measurable. The internet , first with search on Google has massively reduced the need to find information and services. It has created an entire economy based on efficient services from Amazon to Uber. 

 

Productivity has now been turbo-charged with generative AI, where tasks that took hours can be done in minutes, if not seconds. The tasks that have been identified and already researched include: research, brainstorming, report writing, copy writing, coding, image creation and translation. This is merely the tip of the potential iceberg. 

 

Professions

The real gains come in replacing what expensively educated human being currently do – professional tasks such as managers, consultants, accountants, lawyers, teachers, doctors and so on.

 

There is nothing uniquely human about management, teaching, medicine, accountancy or the law. The professions really did turn out to be a conspiracy against the laity, creating a class that traded on being uniquely analytic and smart. The rewards went to those who worked with their head, while those that worked with their heart (social care, nursery staff) or hand (workers with physical jobs) were left behind in terms of pay and advancement. A rebalancing will happen.

 

Managers

Productivity has been levelling off for some time, yet the research is already showing significant improvements on speed and quality with AI. This one area has been happening for some time, with Google, search and tools such as word processing and spreadsheets. Almost every manager or administrator who has contact with generative AI sees an immediate increase in productivity.

 

Consultants

A whole layer was created of consultants are paid handsomely to do organisational analysis and recommendations. It turns out that they are highly vulnerable to replacement by AI that has wider knowledge, better data analysis and clearer ways to recommend courses of action to organisations. We literally have consultancy on-demand, for free.

 

Researchers

Compared to the huge amount of time it used to take researchers, Google Scholar reduced the time taken to find papers and citations buy orders of magnitude. We have also seen advances such as Alphafold, that took a billion years of research into 200 million proteins to create them all in 3D, thereby advancing science, particularly medicine, by disintermediating millions of hours of research and lab work. We will now see a further, more significant, release of potential productivity by researchers through AI, as its data capabilities, including synthetic data, grows to outpace human research in key areas. The bloated and expensive University system can be made far cheaper and more efficient.

 

Teaching

If a virtual teacher increases the performance of learners, why would we persevere with teachers? They are areas where demand is huge and universal, yet supply limited and expensive. Forget the digital divide – the educational divide is far worse. The idea of a Universal Teacher is firmly on the horizon, one that has a degree in every subject and the pedagogic abilities of the best teacher, as that pedagogy is built into the personalised teaching process, available 24/7, to anyone, anywhere.

 

Physicians

The average GP has close to a 5% error rate on diagnosis. If a virtual Doctor gets below that, let’s say 2%, why would we persevere with general physicians. We are more than happy to replace physical labour with smart machines, why not knowledge workers. Why should they get a pass when others did not?

 

This is why Bill gates sees learning and healthcare as the two big beneficiaries of AI. 

Being a teacher or physician are means to an end. If the means has better outcomes, we should embrace its potential.

 

Is there anything left?

We saw ourselves as being uniquely created creatures with exceptional abilities. Skills and abilities were seen as exclusively human. Yet, as we moved from the fields to factories and hardly anyone worked in agriculture, those skills were largely replaced by physical and psychological technology. When manufacturing skills were replaced by machines, we moved from factories to offices. We then thought that we’d all be invincible knowledge workers; high reward professionals who work in offices, or at home on Zoom – we are not. In fact, we are just as vulnerable to technological replacement as those previous groups.

 

To date that expertise has been expensive and lengthy to replicate in humans, so it remained scarce – too few doctors, teachers, lawyers, accountants, so the professions exist, with exceptionally long and expensive training periods, so are not available to all.  That captured cultural capital is can now be used to benefit us all. What we did not forsee was that writing, then printing then digital storage, had captured much of our expertise. It could be mined to generate through AI expertise on-demand, for anyone.

 

We have to accept our limitations and deal with this new future as best we can, to suit our needs. This is especially true in ‘learning’, a social good that through digital disintermediation can be democratised and brought to far greater numbers at very low cost. We need to get over ourselves. We are not as exceptional as we thought, in ANY domain. 

Smart algorithms work for Google Facebook & Amazon can they work for learning?

Algorithms are everywhere

Christopher Steiner in Automate This: How algorithms came to rule the world describes how algorithms have pretty much got everywhere these days. Use the web, you’re using algorithms. Engage with the financial world, you’re engaging with a world driven by algorithms. Look at any image on any screen, use your mobile, satnav and any other piece of technology and you’re in the world of algorithms. From markets to medicine, the 21st century is being shaped by the power of algorithms to do things faster, cheaper and better than we humans, and their reach is getting bigger every day.

Learning is algorithmic

Our brains clearly take inputs but it is the processing, especially deep processing, that shapes understanding, memory and recall. The brain is not a vast warehouse that stores and recalls memories like video sequences and text, it is a complex, algorithmic organ. However, it would be a mistake to see the brain as a ‘simple’ set of algorithms for learning. Take ‘memory’ alone. We have many types of memory; sensory memory, working memory, long-term memory, flash-bulb memory, memory for faces, and remarkably, memory for remembering future events. Huge gains have been made in understanding how these different types of memory work in terms of our computational brain. Even if one rejects the computational model of the brain, alternative models do seem to require algorithmic explanations, namely inputs, processing and outputs. Learning is therefore always algorithmic.

Algorithms in online learning

You’d also be mistaken if you thought that the world of online learning has been immune from this algorithmic reach. From informal to formal online learning, from search to sims, efficient online learning has already been heavily powered by algorithms.

Every time you search for something on Google you’re using seriously powerful algorithms to crawl the web and match its findings to your search item, then produce a set of probable results. It’s algorithms do many other things you may not be aware of, such as identify cheats who create lots of artificial links and keywords to boost their ratings. Every time you use Facebook algorithms determine your newsfeed. The more you engage with someone, the more likely you are to see their posts, and the more Facebook people in general that engage with posts, determines how often that post is presented to others. Every time you use Amazon to see book rankings or get recommendations or offers, they use what they know about you and others to offer you choices. Wouldn’t it be astonishing if this did not happen more and more in online learning?

Even in formal learning, there are algorithms galore. In flight simulators, it’s not just the image-rendering and motion modelling that’s driven by algorithms, it’s also the learning through scenarios and feedback. In games-based learning there are algorithms aplenty. I first implemented algorithms in a BT simulator in the 1980s. What’s new is the fact that the restraints we had then have all but disappeared, with faster processing, better memory and ability to gather large amounts of data for use by adaptive algorithms. They’re here and they’re here to stay.

Algorithms and teaching

Teaching is essentially algorithmic, as the teacher (agent) gathers data about the environment (knowledge and students) and attempts to deliver recommendations or responses, based on their experience. In some cases, such as search and citation-driven research, algorithms do this much faster and better than humans. Teachers, tutors and lecturers, most of whom teach many students, have limited abilities when it comes to gathering data about their students, so no matter how good they are at teaching, tailored, personalised feedback and learning is difficult. It is also difficult to identify what students find difficult to learn. What, for example, are the most difficult concepts or weaknesses in the course? Again, data gathered from individual students and large numbers of students, allow algorithms to do their magic.

The bottom line, and there is a bottom line here, is that warm-body teachers are expensive to train, expensive to pay, expensive to retire, variable in quality and, most important of all, non-scalable. This is why education continues to be so expensive. There is no way that current supply can meet future demand at a reasonable cost. The solution to this problem cannot be throwing money at non-scalable solutions. Smart, online solutions that take some elements of good teaching, and replicate them in software, must be considered. As Sebastian Thrun says, ““For me, scale has always been a fascination—how to make something small large. I think that’s often where problems lie in society—take a good idea and make it scale to many people.”

What is algorithmic learning?

We have seen how smart algorithms, in Google. Facebook and Amazon can significantly enhance users’ experiences, so how can they enhance the learners’ experiences? It is often thought that data is the key factor in this process but data is inert and only becomes useful as input to algorithms that interpret that data to produce useful actions and recommendations. The quality and quantity of data is important but it is the quality of the algorithms that really counts.

Smart software, formula-based algorithms, based on the science of learning, can be used to take inputs and make best guesses based on calculated probabilities. Multiple, linked algorithms can be even smarter. They may use knowledge of you as a learner, such as your past learning experiences and so on. Then, dynamically track how you’re getting on through formative assessment, how long you’re taking in a task, when you get stuck, keystroke patterns and so on. Add to this data gathered from other groups of similar learners and the system gets very smart. So smart, it can serve up optimised, learning experiences that fit your exact needs, rather than the next step in a linear course. The fact that it also learns as it goes make it even smarter.

Iceberg principle

When we learn, most of the processes going on in our brain are invisible, namely the deep processing involved in memory and recall. This activity, that lies beneath consciousness, is where most of the consolidation takes place. Similarly, it is sometimes difficult to see adaptive, algorithmic learning in practice, as most of the heavy lifting is done by algorithms that lie behind the content. Like an iceberg the hard work is done invisibly, below the level of visible content. Different students may proceed through different routes on their learning journey, some may finish faster, others get help more often and take longer. Overall, adaptive, algorithmic systems promise to take learners through tailored learning journeys. This is important, as the major reasons for learner demotivation and drop-out are difficulties with the course and linear, one size-fits-all courses that leave a large percentage of students behind when they get stuck or lost.

Conclusion

One symptom of the recognition of the need for adaptive, algorithmic learning is a recent poll by Inside Higher Ed and Gallup of college and university presidents, who saw more potential in ‘adaptive learning’ (66%) to make a “positive impact on higher education” than they did MOOCs (42%). The 2013 Insider Higher Ed Survey of College and University Presidents. Conducted by Gallup. Scott Jaschik and Doug Lederman, eds. March 2013.

Why would this be so? Well, it’s not only college and University Presidents, the Gates Foundation have also backed ‘adaptive learning’ as an investment. Read their two recent commissioned reports, as well as their funded activity in this area. One UK company, singled out by these reports, as a world class adaptive, algorithmic learning company is CogBooks, who have already taken adaptive learning to the US market. The reason is that it is seen as a way to tackle the problem of drop-out and extended, expensive courses. Personalised learning, tailored to every individual student, has come of age and may just be a solution to these problems.

Learning technologies 2023 - a tale of two events

Spent two solid days at Learning Technologies Conference in London, Europe’s largest organisational learning technology event. First up, a big thanks to Donald Taylor for inviting me to speak. He was everywhere, with his team, keeping the show on the road.We’re oft confused and that happened twice at this event. At the KoganPage bookstall someone asked me to sign my book but it was the other Donald! Mine had sold out, however, I gave her a spare copy. The other was a summarising video of the conference where my talk on ‘AI changing work and learning’ was captioned as a talk by Donald Taylor. He has solid, bona fide Scottish roots in the Glasgow shipyards, so it’s an honour to be connected by confusion!

Learning Technologies was great but I found it a Janus-faced event. One face was the inward looking exhibition, the other the outward facing Conference. 

 

Exhibition

A vast loud, noisy exhibition, with so many stand lights and hot air, it turned into a sweaty hell. I did the rounds. Same old, same old. It was like going back to Disneyland, all smiles and promises of fun times but reflecting an embalmed, vastly overpriced and vanishing world. It there any other industry that produces so much that is so disliked by so many? With a small number of exceptions, there was barely a mention of AI, except in that ‘we’re building it into our product’ sort of way, tinkering.

 

This giant ‘cheese’ factory was churning out courses, stored in LMSs, pouring out the same old scorn, sorry SCORM, data, that ends up as donuts on dashboards. Text - graphic – MCQ – all of the above - repeat. The whole junkyard has become a parody of itself, disengaged from real people and the real world, whose reaction to their latest Leadership, Diversity or Resilience course, is invariably an ‘eye-roll’. We evaluate nothing, which has resulted in the over-production of over-engineered and over-wrought, Disneyfied courses. It is a supply, not demand, industry, not listening to actual business needs, but imposing a therapeutic and moral nonsense. Next thing is they’ll be probing my unconsciousness – hold on…

 

There was much jaw jaw on skills, but so often that manifested itself in Leadership nonsense, DEI or faddish topics. This year’s thing is 'Resilience' yet another hopeless construct from L&D. An excuse for third rate courses, seeing employees as having yet another deficit or disease, of which they must be 'cured'. This therapeutic culture is relentlessly top-down and arrogant. Employees have this stuff force-fed to them, rather than using it autonomously, as they have been doing with Google, YouTube and social media for two decades.

 

This is a technology conference but the technology so often felt like something out of the early 2000s, that’s because it is something out of the Cambrian explosion of LMSs created in early 2000s, with content that has changed little in the last two decades. It lacks the smartness of contemporary tech – the AI, the data-driven approach, the dialogue of social media.

I'm being a little unfair, as this is the technology that was available, became embedded at the enterprise level, integrated with other software and was difficult to update. On content, however, there is less excuse.

 

Meanwhile, literally over the same two days, one edtech company had a half a billion wiped off its market cap, Pearson had a dead cat bounce, IBM has announced that ChatGPT would replace many of its HR staff and the world outside of Disneyland moved on, bypassing this supply pipe, reacting to real demand.

 

Conference

Over the corridor, by contrast, in the conference, AI was the BIG topic. It wasn’t that it was coming, it was already here, with hundreds of millions using it for work. Like some super popular performance support and productivity tool, it seems to have by-passed L&D and most of the vendors. The sisters and brothers are clearly doing it for themselves with AI.

 

There was passing reference to it in David Kelly’s talk, although the talk seemed quite basic, aimed at people new to old ideas like ‘personalisation’ and ‘performance support’. I was genuinely puzzled at the statement about not publishing their Devlearn US sessions online as it would not be equitable. At a Learning Technologies conference that seemed like a cop out.

The Red-something analyst, Dani, had her versions of the Fosway four-way grids, showing her pet companies, oddly absent were some of the European players than those on her slides. Her grasp of the AI phenomenon was thin as gruel. Not sure why we have US people who don’t really know the European market, telling us about our own turf. Fosway are miles better. I tried to suggest some names she had missed but she wasn't interested and fobbed me off. Real analysts, who work deep inside the investment community are way more knowledgeable than these ‘let’s send out some survey questions’ qualitative research houses.

 

I hugely enjoyed the ‘AI for Lifelong Learning’ talk, as Conrado Schlochauer was spot on in saying that adults don’t need all of these courses, as they want to be self-directed and that ChatGPT was the way to go. It was easily the best talk on Life Long Learning I've seen. Although it took a strange turn at the end with the claim that AI was making us illiterate fools, stuck in their echo-chambers. I find that argument unpalatable. The world is full of people in their own bubbles calling out others for being in bubbles. 

 

Talking of bubbles, I find major conference sessions such as ‘Women in Learning’ particularly inward looking. It is a technology conference, not a general L&D conference. I’m thinking about suggesting a ‘Poor People in Learning’ as a counter to the trend to spend all of the budget on Leadership and DEI training that deliberately excludes working class people. L&D seems to assume that everyone works at home or in an office, real practical skills have been underfunded or abandoned in the L&D world and we wonder why the world is falling apart.

 

What I found really heartening was the recognition among almost everyone I met that AI was a Big Bang thing, not just for L&D but for work and the entire species. The debates were intense and informed, Why? Everyone had used it and had their minds blown by it. They immediately saw its potency and potential. I gave a session that was packed to the guddles with people eager to hear what impact this is having on work and learning. That impact is already profound. I also presented to a large room full of students who were as smart as whips, asking all the right questions about AI. 

 

But what really mattered was the myriad of conversations I had in passing, in the pub and restaurants was exciting. A ton of conversations with old friends, and even more valuable lots of new friend made, too many to mention. I particularly loved the enthusiasm of youth, who really did seem a little tired of the old and genuinely wanted to ring in the new.

7 words that worry me in AI

I’m normally sanguine about the use of language in technology and avoid the definition game. Dictionary definitions are derived from actual use, not the other way around - meaning is use. Like those who want to hair split about pedagogy, didactics, androgogy, heutology… whatever. I prefer practical pedagogy to pedantry.

Another is the age old dispute about whether learning can be used as both a verb and a noun, some claiming the noun ‘learning’ is nonsense. ‘A little learning is a dangerous thing’ said Alexander Pope. In the same work he also wrote 'To err is human: to forgive divine" and " Fools rush in where angels fear to tred.'  Pretty impressive for a 23 year old!

 

Nevertheless, in the discourse around AI, several terms make me a bit uneasy, mainly because another force is at work – anthropomorphism. We tend to treat tech as if it were animate, like us. Nass & Reeves did 35 brilliant studies on this, published in their wonderful work ‘The Media Equation’, well worth a read. But this also leads to all sorts of problems as we tend to make abrupt contextual shifts, for example, between different meanings of words like hallucinations, bias and learning. 

 

Another form of anthropomorphism is to bring utopian expectations to the table, such as the idea that software is or should be a perfect single source of truth. There can be no such thing if it includes different opinions and perspectives. Epistemology, the science of knowledge, quickly disabuses you of this idea as do theories of ‘truth’. Take science, where the findings are not ‘true’ in any absolute sense’ but provide the best theories for the given evidence at that time. In other words, avoid absolutes.

 

These category errors, the attribution of a concept from one context to something very different, are common in AI. First, the technology is often difficult to understand – how it is trained, the nature of knowledge propagation from Large Language Models (LLMs), what is known about how they work, the degree which emergent qualities such as reasoning behaviour that arises from such models and so on. But that make us more, not less, wary of anthropomorphism.

 

Ethics

The one area around AI that produces most ‘noise’, in my opinion, is a fundamental lack of understanding about what ‘ethics’ is as an activity. Ethics is not activism, that is a post-ethical activity that comes from those who have come to a fixed and certain ethical conclusion. It tends to be shrill, accusatory and anyone who blunts their certainty becomes their enemy. This faux ethics, replaces actual ethics, the deep consideration of what is right and wrong, upsides and downsides, known and unknown, the domain of real moral philosophy and ethics. One could do worse that start with Aristotle who recommends some moderation on such issues or Hume who understood the difficulty of deriving an ‘ought’ from an ‘’is’, even an appreciation of Mill whose Utilitarian views can be a useful lens through which we can identify whether there is a net benefit or deficit in a technology. It is not as if ethics is some kind of new kid on the block.

 

Hallucinations

Large Language Models tend to ‘hallucinate’. This is an odd semantic shift. Humans hallucinate, usually when they’re seriously ill, mentally ill or on psychedelic drugs, so it brings with it, a lot of pejorative baggage. Large Language Models do NOT hallucinate in the sense of imagining in its consciousness. It is competent without comprehension. They optimise, calculate weightings, probabilities and all sorts of mathematical wonders and generate data – they are NOT conscious, therefore cannot hallucinate. That word exaggerates the problem, suggesting that it is dysfunctional. In a sense it is a feature not a bug, one that can be altered and improved. ChatGPT produces poems and stories – are those hallucinations because they are fiction?

 

Bias

Another word that seems loaded with bias, is ‘bias’! Meaning is use and the meaning of bias has been largely around human bias, whether innate or social. Daniel Kahneman got a Nobel Prize for uncovering many of these. This makes the use of the word difficult when applied to technology. It needs to be used carefully. There is statistical bias, indeed statistics is the science of identifying and dealing with bias. Statistics is a field that aims to provide objective and accurate information about a population or a phenomenon based on data. Bias can occur in software and statistical analysis, which can lead to incorrect conclusions or misleading results. There is also a mathematical definition of bias, the difference between the expected value and the real value of the parameter. The entire science of statistics, where statisticians have developed a ton of methods to deal with bias; random sampling, stratified sampling, adjusting for bias, blind and double-blind studies and so on, help to ensure that the results are as objective and accurate as possible. As AI uses mathematical and statical techniques it works hard to eliminate or at least identify bias. We need to be careful not to turn the ethical discussion of bias into a simple expression of bias. Above it needs objectivity and careful consideration.

 

Reason

Another terms that worries me is ‘reason’. We say that AI cannot reason. This is not true. Some forms of AI specialise in this skill, as they employ formal logic in the tool itself. Others, like ChatGPT show emergent reasoning. We, as humans are actually rather bad at logical reasoning, we second guess and bring all sort of cognitive biases to the table almost every time we think. It is correct to question the reasoning ability of AI but this is a complex issue, and if you want it to behave like humans, reason cannot be your benchmark. 

 

Intelligence

John McCarthy, who invented the phrase ‘Artificial Intelligence’ regretted his definition. Again, taking a word used largely in a human or animal content, is an odd benchmark, given that humans have limited working memories, are forgetful, inattentive, sleep eight hours a day, can’t network and die. We have evolutionary baggage; genetic, instincts, emotions and cognitive abilities. In other words, human ‘intelligence’ is rather limited.

 

Learning

This word, in common parlance, was something unique to human and many animals. When we talk about learning in AI, it has many different meanings, all involving very technical methods. We should not confuse the two, yet the basic idea that a mind or machine is improved by the learning process is right. What is not right is the straight comparison. Both may have neural networks but the similarity is more metaphorical than real. AI has gained a lot from looking at the brain as a network and I have traced this in detail from Hebb onwards through McUlloch & Pitts, Rosenblatt, Rumelhart and Hinton. Yet the differences are huge – in terms of the types of learning, inputs, natures of memory and so on.

 

Alignment

This word worries me as many assume there is such a thing as a single set of human values to which we can align AI systems. That is not true. The value and moral domain is rife with differences and disputes. The danger is in thinking that the US, EU or some body like UNESCO knows what this magical set of human values is – they don’t. This can quickly turn I to the imposition of one set of values on others. Alignment may be a conceit - be like us they implore. No - be better.

 

Conclusion

These are just a few examples of the pitfalls of sloganeering and throwing words around as if we were certain of their meaning when what we are actually doing is slight of hand, using a word in one context and taking that meaning into another. Conceptual clarification is a good thing in this domain.

Are we on the verge of having a ‘Universal Teacher’?

But it can't do what teachers do well…. ChatGPT "Hold my beer...."

 

Universal Soldier is a film franchise that sees a series of soldiers, some melded with AI, to create a superhuman soldier. But what if we imagined something more useful and beneficial. In a world where we have a shortage of teachers, especially in poorer countries where teachers are scarce, poorly paid and class sizes huge, a Universal Teacher would be a Godsend. Not for very young children but certainly for more autonomous learners from secondary school onwards would surely benefit from a teacher that could teach anything to anyone, anyplace at anytime. 

 

The advent of universal internet access, globally, without any blind spots is now with us via ground and satellite services and the ubiquity of devices, many with AI chips, that allow edge computing, do things that were unimagnable only a few years ago, especially voice. The ecosystem is providing the technical means for Universal Teachers to emerge and thrive.

 

Universal Teacher is launched

That ChatGPT4 launched with Khan Academy and Duolingo as partners spoke volumes about its potential for teaching and learning. At the same time Bill Gates wrote a piece called The Age of AI, which despite referencing ‘learning styles’, saw education as the great net beneficiary of Generative AI. Salman Khan, who has been in this game for years, thinks the benefits are enormous and has already launched what could be described as a Universal Tutor in education. What has followed on Twitter was an explosion of innovative and often ingenious applications in teaching and learning around the world.

In just five months we have seen the release of GPT4, a moment in. our history when a new species of technology swept the world. 

Does our unease with this question simply indicates the dying days of human exceptionalism? Copernicus de-centred our species and threw us out into orbit, Darwin de-anchored us further to show we were just a smart ape. One way we got smart was to teach and learn. Yet that exchange has always used learning technologies; painting, writing, printing, internet and now AI. What Generative AI provides is a new pedAIgogy (see more on this), where monologue is replaced by dialogue, what good teachers do in classrooms, tailored to the needs of the learner, something that is difficult, if not impossible with previous technologies, in one-to-many teaching environments. 

A very real question is now on the table. To what degree can AI now replace teachers? That is seen by some as a disturbing question. It is in the sense of possibly dehumanising learning. Nevertheless, it is a worthwhile thought experiment.

The old diad model of teachers and learners is long gone as technology has become a serious mediator, especially online Technology technology. We now have software that learns and can be taught. That, when they can teach, are two new kids on the block.

 

We have to remember that teaching is always a means to an end, that end being the improvement of the learner. It is easy to see teaching as an end in itself but it is not. As a profession it is difficult, hard work, sometimes distressing, often deeply satisfying. Yet the workload can be crushing, the psychological pressure oppressive and it is clearly a demanding job. It is difficult to sustain that pace over weeks, months, never mind years. That contributes to constant teacher shortages, especially in difficult to teach subjects like maths, science and foreign languages. Could technology be a solution to the crushing workload and recruitment problems? I think so. 

 

Right across the learning journey from learner engagement, learner support, content creation, content delivery, personalisation, accessible learning and assessment, AI, especially generative AI can deliver increasingly sophisticated learning. This is effortful, personalised learning, not as learning events but as a process.

 

Generative AI is coming close to delivering personalised tutoring, as good as say the tutors that middle-class parents frequently hire for their kids. I have been convinced of this for some time after seeing its effect in adaptive learning systems at University level in the US, where attainment rose, and dropout fell. The problem with these older adaptive systems is that they were difficult to build and populate. New generative AI is an entirely different beast. It is smart, very smart and as it has been trained on a vast repository of our stored culture, it is smart on all subjects. It is as if it had degrees in all subjects, with some teacher training on top.

 

Universal Teaching

In Khan Academies Khanmigo tuition across dozens of subjects are available to ignite your curiosity, at Elementary school, Middle school, High school and College levels. Paired with revision schedule software these systems would provide lasting and efficient learning support. You can even practice exams with open input and dialogue. You can ask for hints but the standard of dialogue is superb. This is where it gets interesting, like a full discussion with a good teacher, who knows every subject, in remarkable detail, available 24/7/365, all the way up to College level. Even at College level I can see how 101 courses could be delivered this way, then developed to cope with ever more complex aspects of a degree course. These systems need not be as good as teachers to reduce workload and provide real learning for learners across all subjects.

 

Maths

Along comes ChatGPT4 to deliver high quality, personalised Maths tuition. It gives you a maths problem, asks ‘What do you think the first step should be?’, accepts open input, identifies where you went wrong along with a suggestion to move forward. It even relates that maths problem to a subject you love, let’s say soccer. Maths is a difficult subject to teach and learn, a subject where catastrophic failure is common. Here is a system that pays attention to your every step, is engaging and endlessly patient. What’s not to like?

 

Language learning

Duolingo has tens of millions of active users and has used AI for some time, especially to determine practice patters with the half-life algorithms. They have partnered with ChatGPT4 two features that add two powerful pedagogic techniques to their teaching:

1. Explain My Answer, where, if you get something wrong in your second language

Duolingo gives you explanation and 'elaborates’ on what was wrong. It also delivers 'examples' to point you in right direction.

2. Roleplay, where chat with a native speaker who knows your level of competence

and uses human written scenarios to give you endless and much needed practice and immersion. Once completed, each roleplay session gives you a report to suggest improvements.

The ease of translation in LLM models also helps deliver a second language, as it can be readily translated.

 

Critical thinking

Even critical thinking is taught, where you debate against ChatGPT. You present your arguments, it counters, and the debate continues, endlessly knowledgeable, positive, patient and helpful. This Socratic approach to learning plays to our natural propensity toward talking to each other. It is as if we now have a universal Socrates. Debate, a s a formal taxing of the mind, the chess of ideas, where a motion id debated to explore its strengths, weaknesses and limits is not new. To play such mental chess with a machine is what is new.

 

Dialogue with the Greats

Conversations, real dialogue, with dozens of intellectual figures from the past are also available, including scientists like Isaac Newton and Marie Curie, Presidents from Washington to Lincoln, authors like William Shakespeare and Mark Twain, historical figures such as Genghis Khan and Napoleon. As the works of almost all past intellectuals and figures of note have been used to train the model, it can speak as if it were that person. This is on a par with original sources and provides a less dry and text based inroad into the subject. To hear theories and opinions expressed by the person who initiated these thoughts and theories, is surely a novel pedAIgogic approach.

 

Even fictional characters are available, such as Greek Gods like Zeus and  Achilles, Shakespearian characters from Macbeth to Othello and characters from novels such as Mr Darcy to Jay Gatsby, also Dr Frankenstein and Don Quixote. This is an interesting and novel vector into great literature, being able to chat with, interrogate and explore a character, maybe several characters in a work. This is surely a new type of what I’ve called pedAIgogy. 

 

Learning difficulties

Another feature of the Universal Teacher would be sensitivity to accessibility issues. With sight and hearing impairment, text to speech and speech to text through AI delivered via smartphones have already been transformative. ChatGPT was launched with the astounding Bemyeyes app. Dyslexia, autism, ADHD and learning difficulties have all shown some signs of being diagnosed by AI and generative AI can deliver content sensitive to the needs of such learners. 

 

We humans are deeply biased, the biases being largely part of the way the brain has evolved. Daniel Kahneman got a Nobel Prize for uncovering many of these biases and sees them as ‘uneducable’. AI, on the other hand, learns and need not have these biases. It need not ‘know’ the sex, race, cultural background, accent or socio-economic status of a learner. Fairness can be developed as we proceed. This I see as a distinct advantage of the Universal teacher – its lack of bias.

 

Teacher workload

For teachers it can create lesson hooks, lesson plans, assessments, rubrics for assessments, almost any piece of planning and paperwork can be done using Generative AI. As a tool to reduce workload, it has huge potential. This must surely be one of its first deployments.

 

Translations

Imagine being able to do all of the above in almost any language. That is becoming possible. An underestimated feature of ChatGPT and LLMs is translation. That's a global feature for services and products that has huge human benefits. Massive reductions in costs possible through AI here. We would have far less students having to learn, and often struggle to learn in a second language, although that option would still be open, even a blend of languages would be possible.

 

Conclusion

We don’t yet have a Universal Teacher (UT), what we have is a Universal Teaching Assistant UTA). But the Universal teacher is now on the horizon. Difficult to tell how far that horizon is but we have seen the exponential growth of generative AI in just a few months from a good but still error prone service to something far more accurate that has reach across all subjects and has moved technology from embodying simple pedagogic principles such exposition, knowledge assessment, scenario based learning and spaced practice towards new pedAIgogies, such as tutoring, teaching, lesson creation, multimodal content creation, learning support, dialogue, error correction, language immersion and debate.

 

Generative AI will only get better, not only on parameter size, which has proved to be exponentially useful above a certain threshold. Size does matter but so do all the other tools that go into making a Universal Teacher – combinations with web search, maths solvers, and other tools. We are seeing ensembles of technologies create learning tools that solve the problems of provenance, accuracy and efficiency. Generative AI may be the beating heart of the Universal teacher but it has many other weapons it can employ.

An experiment is already underway in the partnership with Khan Academy and OpenAI, with a much in schools already underway. They are taking it carefully, which is right. We will see more of this, AI tutors, like Khanmigo, on all subjects, for all ages.

 

Speculating further, future developments will surely see the embodiment of such teachers as avatars inside 3D virtual worlds, where learning by doing can also occur. We will learn within Digital Twins of the workplace, airport or hospital ward with virtual customers or patients. That is the subject of my next book. The Universal Doctor is surely a spin off, an entity that can investigate, diagnose and treat anyone anywhere.

 

In one way the Universal Teacher is not a teacher at all, it is the learner learning from the cultural achievements from the past. The Universal teacher is us being taught by our own hivemind, a supermind. This is not a mind like ours but it does mediate what we already know so that we can all progress. We can all be in this together. We are being taught by our collective self and that may be the fundamental beauty of this idea.