AI for Learning. So what is the book about?

This is, to my knowledge the first general book about how AI can be used for learning and by that I mean the whole gamut of education and training. It is not a technical book on AI. It is designed for the many people who teach, lecture, instruct or train, also those involved in the administration, delivery, even policy  around online learning, even the merely curious. It is essentially a practical book about using AI for learning, with real examples of real teaching and learning in real organizations with real learners.

AI changes everything. It changes how we work, shop, travel, entertain ourselves, socialize, deal with finance and healthcare. When online, AI mediates almost everything – Google, Google Scholar, YouTube, Facebook, Twitter, Instagram, TikTok, Amazon, Netflix. It would be bizarre to imagine that AI will have no role to play in learning – it already has. 

Both informally and formally, AI is now embedded in many of the tools real learners use for online learning – we search for knowledge using AI (Google, Google Scholar), we search for practical knowledge using AI (YouTube), Duolingo for languages, and CPD is becoming common on social media, almost all mediated by AI. It is everywhere, just largely invisible. This book is partly about the role of AI in informal learning but it is largely about its existing and potential role in formal learning – in schools, Universities and the workplace. AI changes the world, so it changes why we learn, what we learn and how we learn.

It looks at how smart AI can be, and is, used for both teaching and learning. For teachers it can reduce workload and complement what they do, helping them teach more effectively. For learners it can accelerate learning right across the learning journey from learning engagement, support, feedback, creation of content, curation, adaption, personalization and assessment, AI provides smart solutions to make people smarter. 

AI is an IDIOT SAVANT

So how did we get here? Well AI didn’t spring from nowhere. It has a 2500 year pedigree. What matters is where we are today - somewhere quite remarkable. AI is ‘the’ technology of the age. The most valuable tech companies in the world have AI as their core, strategic technology. As it lies behind much of what see online, it literally supports the global web, driving use through personalization. Surprisingly, AI does this as an IDIOT SAVANT, profoundly stupid compared to humans, nowhere near the capabilities of a real teacher, but profoundly smart on specific tasks. Curiously, it can provide wonderfully effective techniques , such as adaptive feedback, on a scale impossible by humans, but doesn’t ‘know’ anything. It is ‘competence without comprehension’ but competence gets us a long way!

AI and teachers

In the book we first look at AI from the teacher or trainer’s perspective, showing that it is not a replacement, but valuable aid, to teaching. Robot teachers are beside the point, a bit like having robot drivers in self-driving cars. The dialectic between AI and teaching shows that there will be a synthesis and increased efficacy in teaching when its benefits are realized. Similarly for learners. AI is not a threat, it is a powerful teaching and learning tool.

AI is the new UI

AI underlies most interfaces online by mediating what you actually see on the screen. More recently it has provided voice interfaces, both text to speech and speech to text. This is important in learning, as most teaching is, in practice, delivered by voice. Then there is the wonderful world of chatbots, the return of the Socratic method, with real success in engagement, support and learning. There’s lots of real examples of how these new interfaces and, in particular, dialogue will expand online learning.

AI creates content

A surprising development has been the use of AI to create of online content. Tools like WildFire have been creating online content in minutes not months with high-retention learning – using AI to semantically interpret answers and get away from the traditional MCQs. AI can also enhance video, which suffers from being a transitory medium in terms of memory like a shooting star leaving a trail of forgetting behind it, towards powerful, high-retention learning experiences. New adaptive learning platforms are proving to be powerful, personalizing learning on scale , delivering entire degrees. AI pushes organisations towards being serious learning organisations by producing and using data to improve performance, not only of the AI systems themselves but also teachers and learners. Models such as GTP-3 are producing content that is indistinguishable, when tested, from human output. This shows that there is far more to AI than at first meets the AI!

AI and learning analytics

Learning is not an event, it is a process. Data describes, analyses, predicts and can prescribe process. Data types, the need for cleaning data, the practical issues around its use in learning and its use in learning analytics along with personalized and adaptive learning shows how AI can educate and train everyone uniquely. Data-driven approaches can also deliver push techniques, such as nudge learning and spaced-practice, embodying potent pedagogic practice. New ecosystems of learning such as Learning eXperience Platforms and Learning Record Stores move us towards more dynamic forms of teaching and learning. Sentiment analysis, using AI to interpret subjective emotions in learning is also covered. AI in this sense, is the rocket with data as its fuel. We explore how you can move towards a more data-driven approach to learning in the book.

AI in assessment

Then there’s assessment, which is being made easier and enhanced by AI. From student identification to the delivery of assessments and forms of assessment, AI promises to free assessment from the costs and restraints of the traditional exam hall. Plagiarism checking is also discussed, as is the semantic analysis of open input in assessment and essay marking.

What next for AI in learning?

Well, there will be a significant shift in the skills needed to use AI in learning away from the traditional ‘media production’ mode and these new skills are explained in detail. More seriously, you can’t have a book on AI for learning without tacking ‘ethics’ and so bias, transparency, race, gender and dehumanisation are all examined. The good news is that AI is not as good as many ethicists think it is and not as bad as you fear. On employment, we look at something few have looked at; the effect of AI on the employment of learning professionals.

AI: the Final Frontier

Finally there a cheeky look at the final frontier. What next? There technology on how AI may accelerate learning through non-immersive and immersive, brain-based technology, as well as speculation on how this may all pan out in the future. It is literally mind-blowing.

Finally…

In these times of pandemic, we have all had to adapt to online learning; teachers, learners and parents. Necessity has become the mother of invention and this book offers a look at the future, where AI technology will provide the sophistication we need to make online learning smart, responsive and up to the the future challenge of a changing world. AI is here, its use is irreversible and its role in learning inevitable. I hope the book answers any questions you may have on AI in learning, more importantly, I hope it inspires you to think about how you may use it in your organization.

AI for Learning. So what's this book about?

 So what is the book about?

This is, to my knowledge the first general book about how AI can be used for learning and by that I mean the whole gamut of education and training. It is not a technical book on AI. It is designed for the many people who teach, lecture, instruct or train, also those involved in the administration, delivery, even policy  around online learning, even the merely curious. It is essentially a practical book about using AI for learning, with real examples of real teaching and learning in real organizations with real learners.

AI changes everything. It changes how we work, shop, travel, entertain ourselves, socialize, deal with finance and healthcare. When online, AI mediates almost everything – Google, Google Scholar, YouTube, Facebook, Twitter, Instagram, TikTok, Amazon, Netflix. It would be bizarre to imagine that AI will have no role to play in learning – it already has. 

Both informally and formally, AI is now embedded in many of the tools real learners use for online learning – we search for knowledge using AI (Google, Google Scholar), we search for practical knowledge using AI (YouTube), Duolingo for languages, and CPD is becoming common on social media, almost all mediated by AI. It is everywhere, just largely invisible. This book is partly about the role of AI in informal learning but it is largely about its existing and potential role in formal learning – in schools, Universities and the workplace. AI changes the world, so it changes why we learn, what we learn and how we learn.

It looks at how smart AI can be, and is, used for both teaching and learning. For teachers it can reduce workload and complement what they do, helping them teach more effectively. For learners it can accelerate learning right across the learning journey from learning engagement, support, feedback, creation of content, curation, adaption, personalization and assessment, AI provides smart solutions to make people smarter. 

AI is an IDIOT SAVANT

So how did we get here? Well AI didn’t spring from nowhere. It has a 2500 year pedigree. What matters is where we are today - somewhere quite remarkable. AI is ‘the’ technology of the age. The most valuable tech companies in the world have AI as their core, strategic technology. As it lies behind much of what see online, it literally supports the global web, driving use through personalization. Surprisingly, AI does this as an IDIOT SAVANT, profoundly stupid compared to humans, nowhere near the capabilities of a real teacher, but profoundly smart on specific tasks. Curiously, it can provide wonderfully effective techniques , such as adaptive feedback, on a scale impossible by humans, but doesn’t ‘know’ anything. It is ‘competence without comprehension’ but competence gets us a long way!

AI and teachers

In the book we first look at AI from the teacher or trainer’s perspective, showing that it is not a replacement, but valuable aid, to teaching. Robot teachers are beside the point, a bit like having robot drivers in self-driving cars. The dialectic between AI and teaching shows that there will be a synthesis and increased efficacy in teaching when its benefits are realized. Similarly for learners. AI is not a threat, it is a powerful teaching and learning tool.

AI is the new UI

AI underlies most interfaces online by mediating what you actually see on the screen. More recently it has provided voice interfaces, both text to speech and speech to text. This is important in learning, as most teaching is, in practice, delivered by voice. Then there is the wonderful world of chatbots, the return of the Socratic method, with real success in engagement, support and learning. There’s lots of real examples of how these new interfaces and, in particular, dialogue will expand online learning.

AI creates content

A surprising development has been the use of AI to create of online content. Tools like WildFire have been creating online content in minutes not months with high-retention learning – using AI to semantically interpret answers and get away from the traditional MCQs. AI can also enhance video, which suffers from being a transitory medium in terms of memory like a shooting star leaving a trail of forgetting behind it, towards powerful, high-retention learning experiences. New adaptive learning platforms are proving to be powerful, personalizing learning on scale , delivering entire degrees. AI pushes organisations towards being serious learning organisations by producing and using data to improve performance, not only of the AI systems themselves but also teachers and learners. Models such as GTP-3 are producing content that is indistinguishable, when tested, from human output. This shows that there is far more to AI than at first meets the AI!

AI and learning analytics

Learning is not an event, it is a process. Data describes, analyses, predicts and can prescribe process. Data types, the need for cleaning data, the practical issues around its use in learning and its use in learning analytics along with personalized and adaptive learning shows how AI can educate and train everyone uniquely. Data-driven approaches can also deliver push techniques, such as nudge learning and spaced-practice, embodying potent pedagogic practice. New ecosystems of learning such as Learning eXperience Platforms and Learning Record Stores move us towards more dynamic forms of teaching and learning. Sentiment analysis, using AI to interpret subjective emotions in learning is also covered. AI in this sense, is the rocket with data as its fuel. We explore how you can move towards a more data-driven approach to learning in the book.

AI in assessment

Then there’s assessment, which is being made easier and enhanced by AI. From student identification to the delivery of assessments and forms of assessment, AI promises to free assessment from the costs and restraints of the traditional exam hall. Plagiarism checking is also discussed, as is the semantic analysis of open input in assessment and essay marking.

What next for AI in learning?

Well, there will be a significant shift in the skills needed to use AI in learning away from the traditional ‘media production’ mode and these new skills are explained in detail. More seriously, you can’t have a book on AI for learning without tacking ‘ethics’ and so bias, transparency, race, gender and dehumanisation are all examined. The good news is that AI is not as good as many ethicists think it is and not as bad as you fear. On employment, we look at something few have looked at; the effect of AI on the employment of learning professionals.

AI: the Final Frontier

Finally there a cheeky look at the final frontier. What next? There technology on how AI may accelerate learning through non-immersive and immersive, brain-based technology, as well as speculation on how this may all pan out in the future. It is literally mind-blowing.

Finally…

In these times of pandemic, we have all had to adapt to online learning; teachers, learners and parents. Necessity has become the mother of invention and this book offers a look at the future, where AI technology will provide the sophistication we need to make online learning smart, responsive and up to the the future challenge of a changing world. AI is here, its use is irreversible and its role in learning inevitable. I hope the book answers any questions you may have on AI in learning, more importantly, I hope it inspires you to think about how you may use it in your organization.

Use code AHR20 here to get 20% discount and free delivery in UK and US.

 

Future of learning technology is invisible AI – AI is new UI...

Donald Norman said that the aim off all good technology is to be invisible. The future of online learning is that it will be smart and that these smarts will sort of disappear.  In learning, this is particularly important, as cognitive issue such as attention, cognitive overload, cognitive effort, feedback and practice really do matter. It is my contention that real efficiencies in learning can only come through AI. You make people smarter by using smart tech. That’s why I wrote ‘AI for Learning’, a book about how the invisible hand of AI will transform why we learn, what we learn and how we learn.

AI is everywhere

AI is everywhere. It is in every smartphone, tablet and laptop. Even in the hardware it optimises battery use and much of the AI functionality is built into hardware such as the Apple Neural Engine (ANE) or its custom-designed GPUs, similarly for Google and others. On the front-end it deals with face or fingerprint identity, is the fundamental technology behind Siri, Google Assistant and Alexa. The cameras and photos produced by your smartphone are laden with AI processing. Before you see a picture, AI has worked its magic. Manipulate that image with filters and image software and AI is the workhorse. More obvious features such as app selection, keyboard prediction, language translation, on-device dictation, health features. Some devices now have lidar, so know if you’ve put the device down, are about to lift it, are moving around, so that the device itself is aware of where it is and what it is being used for in the environment – all using AI.

Do almost anything online and it will be mediated by AI. Email, Facebook, Twitter, Instagram, Google, Google Scholar, Maps, YouTube, Netflix, Amazon…. All interceded by AI. AI is also in online learning. Almost all informal online learning on Google, YouTube and other sources of learning are searched for, mediated by AI. Even in formal learning AI is now being used in real organisations to engage learners, support learners, interface through voice, create content, adapt content, personalise and assess learners. This is the core message in my new book ‘AI for Learning’ where I run through these options, with real examples, to signpost towards this new world of online learning.

Yet you are unlikely to be aware of AI’s ubiquity. It is in there, filtering out spam, stopping dick-pics, porn and hate speech, compressing and decompressing files, selecting things for you so that you are not simply washed over by a tsunami of information. Like the bottom of an iceberg, it now keeps the visible front of the internet afloat.

AI is the new UI

Although there’s lots of talk about UX design, AI is the new UI. It has given us voice in Siri, Google Assistant and Alexa. This is a great example of AI learning and therefore improving with use. My Alexa has gone from me supressing my Scottish accent to me not having to change my diction at all. Beyond voice we have the entire user interface tiled as AI is he intermediate that sits between you and the service. It is your personal butler. In Amazon, it tiles books and goods, in Netflix Box, movies and Box Sets, in learning what is right for you at that precise moment, a decision informed by who you are, what you’ve done so far and the aggregated data from all the others who have been in this situation. Like your satnav, if you go off course, take a wrong turn, it will guide you back on course and to your destination. Beyond this there are hopes of frictionless interfaces between mind and machine that allow the mind to control things, and possible, at some point, to accelerate learning. All of these interfaces, actual and possible are discussed in the book.

AI – a learning example

A good example of the invisible hand of AI is Duolingo (I write about its relevance to learning here). Luis von Ahn is the brains, driver and innovator behind Duolingo. From Guatemala, he’s a mathematician and computer scientist on a mission to keep language learning free. With a value north of $1.5 billion and x50 more users than their nearest competitor and x5 the revenues, its sort of speaks for itself. Its personalization makes it habitual, the daily tasks and streaks are achievable and the behavioural science behind the formation of habit is solid. AI also provides the adaptive delivery of learning chunks. In fact AI drives its entire pedagogy, as it knows what you’ve learnt and, importantly, if you’ve been absent, what you’ve forgotten. Algorithmic personalisation may have more to do with rectifying forgetting than learning. AI also drives engagement, through notifications, algorithmically driven they decide what to say and when to say it. They notify you regularly, but not too much, the most effective notification is the ‘final warning’. If they feel you have dropped off, a timely message, making you feel slightly guilty. The user experience is simple, clean, plenty of white space, consistent palette, no teacher face or teacher avatar, simple progress bar at top of screen. The learning experience has open input for full phrases and sentences, allows people to type what they hear, remediation when you fail, sentence as audio when you get it right and they’re not scared of repetition, single day streaks, spaced practice. They work hard at this. I’ve seen it improve year after year. Importantly, learning wants to be free, and they have achieved this. They are all zealots for free education, well largely, as only 3% of users pay the subscription – learning wants to be free. Duolingo is just one of many examples of AI in learning in the book.

AI futures

To get a feel for how powerful recent advances in AI can be, look at GTP-3, a text generator which points towards the production of online content. It has produced text, even poetry that humans cannot distinguish from real human output. Think about how fundamental AI has become in the hardware and software in your smartphones and online in general.

It is important to focus on benefits of AI in learning. We are at the start of an era when education will be smart, scalable, sophisticated and free. Combined with 5G, possibly delivered anywhere on the planet via Starlink, with low latency and no blind spots, we can start to see a future where personalized education and training is free, translated into multiple languages, to allow knowledge and skills to be a global good.

Conclusion

Invisible interfaces, spam filters, porn filters, hate speech filters, faster delivery, optimised battery life, less latency, voice, improvements in picture quality, better video, personalisation, subtitling, accessibility features, translations, awareness in 3D space, recommendations – all of this is here on your smartphone and there’s more to come.

Yet what used to be called the ‘killer application’ is only just arriving – accelerated learning. AI give us frictionless interfaces with voice, IOT awareness, non-invasive and even non-invasive cognitive devices. AI gives us personalised learning through adaption (entire degrees now being delivered), instant feedback, support and scaffolding in learning. AI will also create content and deliver assessment. AI changes everything in learning. 

AI is already showing significant signs of using smart tech to make you smarter. This is exactly what I’ve written about in my book ‘AI for learning’.

Book: ‘AI for learning’

I tackle this and many other issues in my new book ‘AI for learning’. This is the first general book about how AI can be used for learning across all types of organisations, schools, Universities and workplaces. It is not a technical book on AI, although it tries to explain what it is in non-technical terms and dispels some of the myths. It is written for the many people who teach, lecture, instruct or train, also those involved in the administration, delivery, even policy  around online learning, also the merely curious. It is essentially a practical book about using AI for learning, with real examples of real teaching and learning in real organizations with real learners.

If you want a 20% discount and free delivery in UK or US contact me!

WildFire

Also, of you are interested in using AI to create high-retention, online learning, in minutes not months, using award winning software, contact us here.

How to prompt like a PRO! 100 types of prompt in ChatGPT for learning

We have been stuck in a productivity rut for some time. AI promises to release us from ploughing that particular furrow, and release technology driven learning from its current malaise. AI is clearly the technology of the age. Almost all of the large, global tech companies are, in essence, AI companies.

In learning AI changes life and work, and by consequence, what, whenever, when and how we learn. AI promises to deliver adaptive and personalised learning on any topic at low cost freeing education from expensive scarcity. This is because it increases the efficacy, not only of content creation but, more importantly delivery. We see a glimpse of this with Duolingo, now an AI company with a highly effective personalised learning system, that is free. They have now expanded into Mathematics. 

They used to say that information wants to be free. A far better mantra is to say that education wants to be free. First, free from the tyranny of time, having fixed lengths of time to get to competence, fixed timetables, course times and so on. Secondly, free learners from the tyranny of place, having to be somewhere specific, like a lecture hall or classroom.

I explored this in my book ‘AI for Learning’. Accelerating learning, making it readily available, along with semantic search and the many other gifts that AI will bring, should make organisational development that much easier, cheaper, faster and better. This has been the promise of most technology and if we look at the past, technology has, on the whole, delivered. 

Generative AI

Generative AI is still in its infancy, yet achieved instant global awareness with ChatGPT. That impact came from the realisation, often from the first output to the first question you asked, that this has huge potential. It put AI into the hands of millions, made it real. There was the expected negativity, largely born of fear, as is usually the case with new technology in learning, but that quickly gave way to wonder, lots of creative uses and speculation about the future.

It is important not to see generative AI as the only way AI has and will impact L&D. My books ‘AI for Learning’ , 'Learning Experience Design' and ‘Learning technologies’ go across the entire learning journey, showing how AI has already embedded itself into almost everything we do online. In that sense it has been here for at least 20 years in search, in UI, text to speech, speech to text (increasing accessibility), also in learning support, content creation, recommended learning pathways, adaptive, personalised learning, assessment and spaced practice.

Prompt Engineering

What ChatGPT does is allow you to use the tool to teach and learn. Once you have mastered ‘prompt engineering’, knowing how to construct the right input, understanding that ChatGPT is a dialogue system not just a producer of monolithic pieces of text, you can output wonderful things. This is a new skill for learning designers and generative AI allows all sorts of complex prompting using iterations, logic and parameters that improve the output. 

It allows you to improve up-front design, get the juices going with stakeholders, producing objectives, likely competencies and skills, syllabi, even titles for your learning initiatives. To create content one can prompt for full content with the right level of detail and nuance, summaries, images, all in different styles and voices, suitable for different audiences. You can create assessments and assignments with full rubrics for marking, also learning activities for discussions, scenarios and role playing. Beyond this you can prompt for emails, social media content and marketing. We have put together a list of 100 prompt ideas for learning professionals  to allow learning professionals to widen their perspective on this technology which we will be using in planned talks and workshops.

Remember that this is only the start of AI tools that will dramatically improve productivity for teachers, learning designers and learners.

AI and ethics - it's not as good as you think and not as bad as you fear

Joanna Bryson, one of the world’s experts in AI and ethics is right when she points out that the big problem in AI and Ethics is ‘anthropomorphising’. AI is competence without comprehension. It can beat you at chess, Go and poker but doesn’t know it has won. Literally hundreds of AI and ethics groups have sprung up over the last couple of years. Some are serious international bodies like the EU, IEEE and so on, but it is important to examine but remain level-headed on this issue. The danger is that we destroy the social goods that AI offer, by demonising it  before it has been tried.

Having just launched a new book ‘AI for Learning’ in which I tackle these ethical issues in some detail, I thought I’d provide a taster for the ethical concerns as they may affect the world of learning. 

Existential

Let’s get one moral issue out of the way – the existential threat. This often centres around Ray Kurzweil's ‘Singularity’, the idea that AI will at one point transcend human intelligence and become uncontrollable. Other AI experts like Stuart Russell, Brett Frischmann and Nick Bostrom have speculated at length on ways in which runaway AI could be a threat to our species. Although there are possible scenarios where runaway AI will lead to our demise as a species, this is not an issue that should worry us much in using AI for learning. Many, such as Stephen Pinker, Daniel Dennett and other serious researchers in AI are sceptical of these end-of-days theories. In any case, it is highly unlikely that AI for education will do much other than protect us from such scenarios.

Bias

Much more relevant is the topic of ‘bias’. The problem with many of the discussions around bias in AI, is that the discussions themselves are loaded with biases; confirmation bias, negativity bias, immediacy bias and so on. Remember that AI is ‘competence without comprehension’ competences that can be changed, whereas all humans have cognitive biases, which are difficult to change. AI is just maths, software and data. This is mathematical bias, for which there are definitions. It is easy to anthropomorphize these problems by seeing one form of bias as the same as the other. That aside, mathematical bias can be built into algorithms and data sets. What the science of statistics, and therefore AI, does, is quantify and try to eliminate such biases. This is, essentially, a design problem, and I don’t see much of a problem in the learning game, where datasets tend to be quite small, for example in adaptive learning. It gets to be a greater problem when using a model such as GPT-3 for learning, where the data set is massive. It can literally produce essay-like content at the click of a button. Nevertheless, I think that the ability of AI to be blind to gender, race, sexuality and social class may, in learning, make it less biased than humans. We need to be careful when I comes to making decisions that humans often make, but at the level of learning engagement, support there’s lots of low hanging fruit that need be of little ethical concern.

Race

The most valuable companies in the world are AI companies, in that their core strategic technology is AI. As to the common charge that AI is largely written by white coders, I can only respond by saying that the total number of white AI coders is massively outgunned by Chinese, Asian and Indian coders. The CEOs of Microsoft and Alphabet (Google) were both born and educated in India. And the CEOs of the three top Chinese tech companies are Chinese. Having spent some time in Silicon Valley last year, it is one of the most diverse working environment I’ve seen in terms of race. We can always do better but this should, in my view not be seen as a crippling ethical issue.

Gender

Gender is an altogether different issue and a much more intractable problem. There seems to be bias in the educational system among parents, teachers and others to steer girls away from STEM subjects and computer studies. But the idea that all algorithms are gender-biased is naïve. If such bias does arise one can work to eliminate the bias. Eliminating human gender bias is much more difficult.

Transparency

It is true that some AI is not wholly transparent, especially deep learning using neural networks. However, we shouldn’t throw out the baby with the bathwater… and the bath. We all use Google and academics use Google Scholar, because they are reliably useful. They are not transparent. The problems arise when AI is used to say, select or assess students. Here, we must ensure that we use systems that are fair. A lot of work is going into technology that interprets other AI software and reveals their inner workings.

Dehumanisation

A danger expressed by some educators is that AI may automate and therefore dehumanise the process of learning. This is often within discussions of robot teachers. I discuss the fallacy of robot teachers in the book. It is largely a silly idea, as silly as having a robot driver in a self-driving car. It is literally getting the wrong end of the stick, as AI in learning is largely about support for learners. Far from dehumanising learning it may empower learners.

Employment

The impact of AI on employment is a lively political and economic topic. Yet, before Covid, we had record levels of employment in the US, UK and China. There seems to be a fair amount of scaremongering at learning conferences, where you commonly see completely fictional quotes, such as ‘65% of children entering primary school today will be doing jobs that have yet to exist’. Even academic studies tend to be hyperbolic, such as the Frey and Osborne (2013) report from Oxford University that claimed ‘47% of jobs will be automated in the next two decades’. Seven years in and the evidence that this is true is slim. What is clear is that skills in creating and using AI for learning will be necessary. Indeed, Covid has accelerated this process. I categorise and list these new skills in the book.

Conclusion

I touch upon all of these issues in the book and stick to my original premise that AI is ‘not as good as you think it is and not as bad as you fear’. Sure there are ethical issues, but these are similar to general ethical issues in software and any area of human endeavour where technology is used. It is important not to see AI as separate from software and technology in general. That’s why I’m on the side of Pinker and Dennett in saying these are manageable problems. We can use technology to police technology. Indeed AI is used to stop sexist, racist and hate text and imagery from appearing online. Technology is always a balance between good and bad. We drive cars despite the fact that 1.3 million people die horrible deaths every year from crashes and many more have serious injuries. Let’s not demonise AI to such a degree that its benefits are not realised and , as I discuss in the book, in education and training the benefits are considerable.

 

AI for Learning

The book ‘AI for Learning’ is available on Amazon. In addition to ethics it covers many facets of AI for learning; teaching, learning, learning support, content creation, chatbots, learning analytics, sentiment analysis, and assessment.

 

Bibliography

Bryson, J.J., Diamantis, M.E. and Grant, T.D., 2017. Of, for, and by the people: the legal lacuna of synthetic persons. Artificial Intelligence and Law, 25(3), pp.273-291.

Kurzweil, R., 2005. The singularity is near: When humans transcend biology. Penguin.

Russell, S., 2019. Human compatible: Artificial intelligence and the problem of control. Penguin.

Clark, D., Review of Human Compatible https://donaldclarkplanb.blogspot.com/search?q=Human+Compatible+by+Stuart+Russell+-+go+to+guy+on+AI+-+a+must+read..

Frischmann, B. and Selinger, E., 2018. Re-engineering humanity. Cambridge University Press.

Clark, D., Review of Re-engineering humanity

https://donaldclarkplanb.blogspot.com/search?q=Frischmann

Bostrom, N., 2017. Superintelligence. Dunod.

Pinker, S., 2018. Enlightenment now: The case for reason, science, humanism, and progress. Penguin.

Dennett, D.C., 2017. From bacteria to Bach and back: The evolution of minds. WW Norton & Company.

Clark, D., Review of From bacteria to Bach and back

https://donaldclarkplanb.blogspot.com/search?q=Dennett+-+why+we+need+polymaths+in+the+AI+ethics+debate

10 important things AI teaches us about ‘learning’

Alvin Toffler, who died this week, said, “The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” But he had a better quote, “If you don't have a strategy, you're part of someone else's strategy.” There’s one species of this argument that really matters in the learning game - if you don’t have an AI strategy, you’ll be part of someone else’s strategy.

Most pedagogic change now comes through the use of technology. The internet is a huge Darwinian machine that selects ‘fit for purpose’ services, which millions, sometimes billions, want and use. As a result, in the learning game, we have seen more pedagogic change in the last 20 years, than the last 2000 years. This process is accelerating.

There are some new kids on the learning block, like blockchain. But at a deeper level there’s something far more significant that’s happening; AI is re-shaping the learning landscape. Intriguingly, AI now draws its inspiration from evidence of how we actually learn. This should be a wake up call for those of us who work in the field. The AI community now shows us what works, practically, in learning theory, and what doesn’t. They do this by building ‘learners’, software that learns. Pedagogic change no longer comes from educational research (not sure that it ever did), it comes from insights in cognitive science and, increasingly, through this form of technological innovation. AI is the latest manifestation of digital pedagogy and the one that is now giving us confirmation about what works and doesn’t work.

Learning embedded in AI

We’ve recently seen some super successes in AI, with Deepmind’s spectacular win against one of the World's greatest GO players, the Todai project passing the Tokyo University entrance exam. Deepmind used a layered neural network, with an executive layer, initially trained on 30m human moves, which then played itself using a trial-and-error process (reinforcement learning), with the huge processing power of the Google Cloud Platform – and it won.

This is where it gets fascinating for those of us interested in learning theory. Cognitive scientists, and cross-discipline minds, like Demis Hassabis, have taken principles from cognitive learning theory, such as clustering, attention, learning by doing, reinforcement learning, chunking and practice, embodied these in AI, and are using them to great effect in getting software to learn and problem solve. These learning machines are showing us the way. So what can WE learn from this?

1. Search

Access to knowledge and skills has long been a problem in education and training. The traditional model has been slow, scarce and costly supply through expensive institutions such as Universities and libraries, with rising costs and debt. Then along came Google, a great pedagogic leap, and reduced that time and cost to almost nothing. This has been a huge pedagogic leap, one that completely reshaped the learning landscape at all levels. It is all down to AI. Google is nothing but AI.

2. Feedback

We know that improved and detailed, personal feedback accelerates learning. Yet traditional teaching, especially in the classroom and lectures, make this very difficult. Using data to dynamically adapt, in real time, what is taught next, is a common technique in AI. Much of what you see online is determined by algorithms that constantly monitor your needs. Software (AI) driven feedback is the only way to provide such detailed feedback, personally, on scale. AI thrives on new data to update what it thinks it knows, whether from the individual learner or aggregated learners. This is how Bayes and dozens of other species of AI algorithms work. We need to recognise that the brain has exactly the same needs. Everyone has unique learning needs and everyone need to be educated uniquely.

3. Less is more

The principle of clustering, indeed most refinement of algorithms, reduces what has to be learnt to a minimum, looking for optimal ways forward, while retaining efficacy. This is an important ‘less is more’ principle that is all too often ignored in real world teaching and learning. We still teach at too general a level, for example, teaching how to write essays by repeated essay writing, rather than the more detailed components of good writing and analysis. For a complete breakdown of this error see this excellent video by Daisy Christodoulou. AI has applied and developed a battery of mathematical techniques to optimize learning. They select, reduce, optimize, judge and create recommendations. It has 2500 years of philosophy, logic, probability theory and mathematics behind it and now that we have an abundance of data and cheap computers, is bearing astonishing and exotic fruit.

4. Chunking

We have known about this for decades. Chunking, meaningfully, accelerates learning. AI does this often with data structures, pre-processing and choice of efficient algorithms. Sorting algorithms are a great example of chunking (binary sort) for efficieny. Skills acquisition is not about just, say playing golf, but practising how to putt, drive etc. No golfer becomes great by simply playing golf – they chunk down and practice. AI has shown that, to learn effectively, you chunk problems down into their constituent parts, practice those, then build up your skills. AI is lots of little skills that add up to something big – like the self-driving car. Who saw that coming? Teaching general skills is still all too common, especially in schools, colleges and universities. But it’s operating at the wrong level. AI has taught us that we need to focus on the detail.

5. Reinforcement learning

Another insight from learning theory, that the AI folk have picked up on, is that people learn by DOING things. Children don’t learn much by sitting and listening. They do stuff. This reinforcement learning, the idea that every state has a value, beyond the simple binary ‘win’ or ‘lose’ positions, is powerful. Reinforcement learners (software) rehearse learning a huge number of times, trying and trying again, sometimes the best way, sometimes randomly. It’s a turbo-charged learner. So having learnt what to do from human data, it plays itself, an enormous number of times in a short period of time, to get super-smart. This is how Deepmind beat the GO champion. They have been hugely successful in all sorts of AI, real world tasks. It tries all sorts of habits but selectively chooses those that are successful. It learns how to learn. This effortful learning, learn by doing theory, is at the heart of AI.

6. Deliberate practice

AI has embodied the idea of ‘deliberate practice’, from Ericsson, and built algorithms and methods of propagation that do exactly that. They practice with intent, that intent being improved performance. This is what backpropagation in neural networks and many other machine learning approaches do – they automate and optimize deliberate practice and improvement. They embody what education and training has ignored for too long – deliberate practice.

7. Spaced-practice

Spaced-practice tools are largely driven by algorithms that deliver the pacing, interleaving and load balance for spaced practice. We have known since Ebbinghaus, since 1885, that learning suffers from massive forgetting. We also know that the solution is deliberate, spaced-practice. This can be effected online with smart algorithms that determine how this should be delivered. They do what no teacher can do, identify what needs to be reinforced, how it needs to be reinforced (interleaved etc) and when it needs to be delivered (load balanced etc), related to that individual’s needs. It is all down to AI.

8. Data driven

We are no longer data poor, we are data rich. It is no accident that slow burning AI suddenly had its Cambrian explosion, with thousands of practical examples, from speech recognition to self-driving cars, breakthroughs that are reshaping global industries. The ability to manage, read and interpret this data gives you the radar you need to keep ahead of the game. That’s exactly what AI does applying logic, probability and computer power to the problem of prediction. It has been fashionable to ignore ‘knowledge’ as just ‘data’ in education, but AI has shown that knowledge (data) really does matter. It is an integral part of learning, not something to be abandoned or tritely classed as ‘rote learning’. It is, in fact, what enables deep learning.

9. Socratic learning

Socrates learning theory is often called a ‘theory of ignorance’. It was a process of excising what you think you know, stripping things back until the real knowledge was exposed. He taught us humility in learning. This is also true of AI. It knows what it knows but also knows what the probabilities are in its outputs. In this sense it is free from the cognitive biases, even gender, race and socio-economic biases that teachers, as they are human, almost always have. We have a chance here, to both recognize our limitations and embrace technology enhanced teaching. A little Socratic humility, through the use of AI, recognizing where we are good, and not so good, in teaching (and learning), could go a long way.

10. Learning to learn

An interesting insight comes from what AI folk call ‘learners’. These are machine learning algorithms, that learn themselves. This fundamental point, that the new landscape is not the old one of ‘human teachers’ and ‘human learners’, but also one of’ machine teachers’, and importantly, ‘machine learners’. These new entities cope with complexity; time complexity, space complexity and errors. The machine learners learn to learn and now learn how to teach to learn. The automatons automate automation. This has profound implications for productivity, employment and politics. It will, in time, become an existential issue, first for the professions, even for our species.

Conclusion

AI is all about learning. It is software that learns. It is also software that can create good learning content. We have to pay attention to what is happening here. AI tells us that learning is a process, one that can be unpacked and copied - reverse engineered. They are achieving things that were unimaginable just a few years ago. They are learning about learning by creating effective learners. The fact that AI is having so much success by following the lessons that cognitive science has to teach, is surely a wake up call for learning theorists, especially those still stuck in foggy world of social constructivism.

7 ways AI & Data are transforming learning

Tesla passed $1 trillion market cap today so it is now worth more than Pfizer, Aztrazeneca, GSK, ExxonMobil, BP, and IBM combined. The only companies now worth more than Tesla are Apple, Microsoft, Google and Amazon. Their common denominator is that their underlying tech is now AI. Europe is falling behind, as we'd rather regulate than innovate.

Those who claim to ‘know’ where AI is going, and how fast, are being constantly challenged. 

So where is it going on learning? Well the main area of focus is NLP (Natural Language processing). AI is moving fast on several fronts here.

Data

Tesla has what seems to be an outrageous valuation. Yet what is being valued is not traditional car production, it is the driving data it harvests and the promise of a world where the very concept of vehicles and transport will be transformed. This will happen in learning. The data we gather will feed into optimising future learning experiences, as processes not events. This is why AI, or rather AI that uses data, will shape the future landscape of learning. Data will lie at the heart of all learning experiences. I explain this in my new book ‘Learning Experience Design’.

AI is the new UI

I’ve written about this in ‘AI for Learning’ and ‘Learning Experience Design’, the reshaping of UX as almost all interfaces are now mediated by AI - all social media, Netflix, Amazon, Google, YouTube - almost everything you do online. This is now happening in learning thorough LXP systems. In addition, voice interfaces are now in smartphones and on devices in cars and homes. It is getting better, faster and is scalable. AI is changing our whole relationship with technology, making it more human.

AI personalises

We know that personalised learning gives really significant advantages to large numbers of learners. We’d all love to have one-on-one teaching but that was never economically possible. It is now. Adaptive and personalised learning, enabled by AI, is now here at all levels in learning. CogBooks, a company I helped build has just been sold to Cambridge University Online and will power its online learning. LXPs, such as Learning Pool’s Stream, something I’ve been involved in, will deliver personalised learning to employees in the workplace and workflow.

AI teaches

Teaching largely addresses deficits in motivation and effort, learning is largely achieved by oneself. Took me a long time to truly understand this. It can create sense-making experiences for learners. The problem with traditional online learning is that it was essentially the presentation of content. It never really did what a good teacher does and that is create the opportunities for learning then allow and support you to make the effort to learn. AI enables both. We do this in WildFire.

AI learns

We used to have teachers and learners. Now we have teachers, learners and technology that also learns. Tesla learns as it aggregates driving data and uses that data to improve performance. The more we use Google the better it gets. The more we use personalised and adaptive learning the better it becomes for future students. We are no longer stuck on a plateau of human performance but on an upward trajectory of performance, making learning better, faster and cheaper.

Transformers

Transformers, such as GTP-3 are already useful in learning. We’ve been using them in WildFire for summarisation, content creation and question generation. This software is so powerful that just learning how to ask it questions or do things for you needs a new skillset - it is called ‘prompting’. These AI models have been trained with unimaginably large data sets. They have so much data in their training set that they, at times, transcend the ability of humans to create prose. They are now also entering the world of audio, images and video. They will literally be transformative.

Edge AI

The processing and application of AI on the ‘edge’, on devices, has really arrived. Look at the new Pixel6 mobile phone to see how AI is being delivered via chips in devices such as phones. It has a Tensor AI chip on-board; so translates, transcribes and does speech recognition blazingly fast. It can also erase unwanted objects on photos. These are seriously difficult tasks that require localised processing.

Conclusion

We can wallow in existing practices and technology and see modest but not substantial change in the efficacy and cost of learning. Or we can accept that the future is one where data, and what we do with that data, determines upward progress. A future that uses AI and data to create learning experiences as processes not events, improve interfaces, personalise, teach, support learning. All of this possible to wherever, whenever and to whoever. Technology, specifically AI and Data are finally delivering what we used to call Lifelong Learning.