Could AI replace teachers? 10 ways it could?

Teachers are not ends-in-themselves, they are always a means to an end - improvements in the learner. Given this premise, could it be possible to eventually replace teachers with AI technology? This may not happen soon but let’s, as a thought experiment, ask whether it could.

Obvious points are that AI is 24/7, fast, scalable and cheaper. This gives it a head start. But could it teach? First, we need to break down the functions of teaching and learning. I have used a PGSE schema as my starting point, supplemented by other learning tasks.

1. Searching for answers

Google is nothing but AI and has already delivered a remarkable pedagogic shift with search. Let’s also reflect on something that has already happened. Since Google’s inception, the number of librarians, teachers of sorts, has steadily fallen. We have less need of book and Journal warehouses, now that most knowledge is online. Beyond this, OER resources, such as Wikipedia, YouTube and Khan Academy have transformed the landscape. All of this is available through AI enabled search. Beyond search we see apps, such as Photomaths, where you simply point your smartphone at a maths problem and it gives you the answer. More than this, it gives you the steps in between. That’s how powerful AI has become.

2. Student support

We have a good example of student support in the remarkable Georgia Tech bot, Jill Watson. This AI teaching assistant bot, trained to answer based on prior responses, was used with 300 AI students, without them knowing. They were more than fooled, they praised the teacher for its efficacy and speed. In fact, it was only because it was so fast in its replies that they spotted it was a bot. We can expect a lot more of this, as teacher support, gives way to intelligent AI agent support. Teachers, especially online teachers, spend a lot of time on admin and support, so this is one area that is ripe for AI automation.

3. Demonstrate good subject and curriculum knowledge

This is where AI has made good progress and could outperform teachers. At one level Google already provides access to ‘knowledge’ on every subject, in detail. On ‘knowledge’ Watson beat the Jeopardy Champions way back in 2011. It is now a web-based service with access to huge knowledge bases and AI. At the skills level, YouTube is already the search engine of choice for learning how to ‘do’ things. With 3D, virtual worlds one can see how learn by doing can be expanded, as it was with flight sims, through cheap consumer technology, high in AI. The Todai project in Japan has produced AI software that passed the Tokyo University entrance exam, in Maths, Physics, English and History with a score of 53.8% versus the average human score of human 43.8%. Why is this important? Well, if AI has graduate level skills at this stage, it is likely to replace graduate-level jobs.

4. Plan and teach well-structured lessons

Lessons or learning experiences can be idiosyncratic, even flawed. AI design offers, not only optimal design but also continuous improvement, as it uses individual and aggregated data to spot poor components in lessons. There are plenty of examples of this already, from MOOCs and other forms of online learning, where errors in content, bad designed questions, overlong videos and presentations, have all been caught by data produced by online learning systems that constantly look for improvement. ‘Structured’ is an interesting concept here and one could argue that learning needs to be far more structured than the one-size-fits-all lesson plan. Differentiation could be identified and handled by AI in a way that traditional teaching cannot. The promise is of learning experiences that are not only structured towards individual learners but also continuously improve as machine learning identifies and acts on identified weaknesses. AI may even automatically produce lessons and content. See WildFire.

5. Promote good progress and outcomes by pupils

Progress tracking is not easy, as it requires the simultaneous tracking of actual performance across many learners. This is notoriously difficult in teaching. AI, on the other hand, promises to do this across many learners in realtime, as it gathers evidence that no teacher can possibly hope to gather through traditional observation and testing. More than this, one could argue that AI has a lot to offer in being free from human biases, that sometimes inhibit learner progress. We have ample evidence of gender and socio-economic bias in teaching. AI can be free from bias on gender, race, accent and background. Again, AI promises a scalable solution to this problem that may be of higher quality than a teacher-delivered system.

6. Adapt teaching to respond to the strengths and needs of all pupils

One of AI’s first forays into teaching has been through adaptive systems. These are already at work and producing impressive results. They act like a Satnav, which constantly monitors the performance of individual learners and adjusts what they are asked to do next. This is done in realtime. Content is no longer a linear curriculum of flat resources but a network of learning experiences that can be dynamically delivered to individual learners, based on their precise needs at that precise time. The analytic and predictive strengths of AI may very well identify factors that both inhibit and enhance learning in any individual.

In addition, AI can monitor and deliver to the needs of all pupils, especially those with special educational needs; high ability, English as an additional language, disabilities, and be able to use and improve distinctive approaches. Technology has already made a big difference in SEN, AI will make an ever bigger difference. All of this may be way beyond the ability of any teacher to deliver on scale. It is here that the first successes in AI driven teaching are being achieved.

Chris Piech’s team at Stanford & Google, used 1.4m answers to problems set by Khan Academy to diagnose what any learner was likely to get right/wrong in their next question. The software had to know what went wrong and why. It is, currently, accurate 85% of the time. Why is this important? Because that automated skill is better than the average teacher.

7. Make accurate and productive use of assessment

If we break assessment down into formative and summative, both, it can be argued, could benefit from AI.

Formative assessment is difficult and often of poor quality in one to many classrooms. It is largely absent from the lecture hall. There are three ways that AI could improve formative assessment, in ways superior to current ‘teacher’ delivery. First the quantity; adaptive learning systems, could deliver more feedback than teachers. It is self-evident that AI is scalable in the way a teacher is not and can deliver millions of pieces of feedback to millions of learners in milliseconds – this is what Google does already, albeit at a basic level. Second, AI could deliver higher quality feedback, which can also be used to determine what is literally delivered next in an online lesson. Formative assessment is one area where AI already excels. Increasingly we will also see, through AI, immediate feedback, delivered verbally or in text, as AI driven speech recognition and delivery becomes commonplace. With speech we will move towards the sort of frictionless interface than enables good teaching and learning.

On summative assessment, AI can deliver adaptive questioning and, using Item Response Theory, deliver assessment that includes learner confidence and other data during the assessment that no teacher could gather. It can also deliver to whatever statutory assessment requirements are in place. Essay marking is reaching a level, where it can perform as good as an expert assessor. Automated marking is also becoming more common. Online proctoring uses AI in typing patterns to identify the examinees, as is face recognition for digital identity and realtime face recognition, as the learner takes the exam.

Assessment is clearly one area where AI has, and will continue to make inroads. If teachers no longer have to set, and mark, homework, assignments and exams, a huge portion of the teaching task will have been automated.

8. Set high expectations which inspire, motivate and challenge pupils

This is tricky, as it is difficult to measure. No one would argue that technology is not motivating, even compelling, especially for the young. Whether it is motivating and compelling for learning is another question. With the emergence of the Smartphone, gamification, AR, VR and frictionless speech recognition, we already see signs of technology that is both powerful in terms of learning and compelling. Gamification certainly tackles the motivational issue. AR offers layered experiences. VR offers inspiring, complete immersion and attention, emotional pull, learning by doing, contextualised learning and high retention (witness flight sims for pilots). It promises to democratise something teachers in classrooms cannot offer - experiences. AI brings all sorts of additional dimensions to learning. Many aspects of good ‘learning theory’ are now being embedded in AI and used for teaching. I argue elsewhere, that we in the learning game, have a lot to learn from AI, as it starts to distinguish what is good from bad in learning theory.

9. Manage behaviour effectively

We could envisage a future where adult supervision of learning does not need highly qualified, subject-trained teachers but facilitators. We can even imagine the transformation of schools, into places where one learns, not places where teachers teach. This is a radical shift but as teaching becomes automated so schools become places of learning, not teaching. In practice this pendulum swing has started. There are plenty of online learning courses and degrees out there and learners are starting to do it for themselves. This can only continue. One argument is that it will accelerate, now that AI is in the field, as it has in other areas of human endeavour.

10. High standards of personal and professional conduct

One can hardly ask this of AI but perhaps it becomes less of a problem. However, the modelling that teachers provide is certainly important to young people. Teachers provide values and models of conduct that one hopes are emulated. Again however, we may see the development of attitudinal learning, that was never adequately delivered in classrooms, with simulations and the ability to put yourself in the shoes of others. AI is already feeding the VR industry with intelligent avatars, which are commonly used in games but increasingly in attitudinal learning. You become the bullied person, the subject of racism, sexism or bigotry.

Conclusion

Few saw self-driving, autonomous cars coming. That happened because of AI. Few may also see self-driving, autonomous learners. That may also come through AI. Machine learning not only embodies learning, it learns about learners while they learn. It is like a fast learning teacher. There is every reason to suppose that teaching may, to some degree, even a large degree, be automated by AI. If this is possible, it will be a momentous breakthrough, as education at all levels, whether schools, colleges, universities or workplaces, will experience massive reductions in costs. The developing world, where teaching is a problem in terms of quality and supply, is also likely to receive a huge boost. At some point we may look back at teachers and classrooms as we look back on manual manufacturing in factories. I’m not suggesting that teachers are in any way not valuable or smart, just that AI technology may, as in many other areas, get more valuable and smarter.

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.

AI builds e-learning at 10% of cost, in minutes not months, with higher retention and recall

Paying 20k an hour for e-learning content, over many momnths, that is laden with noisy pages of text/graphics, punctuated by low retention multiple-choice questions? AI can help you to build content at 10% of the cost, in minutes not months, with higher retention and recall.

Problem

The problem with the traditional online learning bespoke content paradigm is the tools. They push vendors and buyers into producing content that has ten major flaws:

1. Expensive to produce

2. Too long to produce

3. Difficulties with SMEs

4. Media (but not mind) rich

5. Weak Multiple-Choice

6. Low effort learning

7. Pavlovian gamification

8. Impersonal learning

9. Low retention and recall

10. No practice

1. Expensive to produce

I ran a large, bespoke, content company. It was very successful but that was back in the day and it used the tools of the day. Yet the content produced today costs and looks much as it was 30 years ago, despite the fact that computers are faster, better and cheaper and online. Why does it still cost 20k an hour to produce e-learning content? Because we’re still in the old paradigm of traditional authoring tools and a mindset besotted with appearance, not learning. Imagine reducing that cost by 90% through a radically different approach, using AI and automation. You can.

2. Takes too long to produce

E-learning projects can take up to six months or longer, with lots of process and angst. It’s a highly iterative process, and takes a huge amount of management, definition, design, development and delivery time to produce anything. Imagine doing all of this in minutes, not months. You can.

3. Difficulties with SMEs

By far the most difficult step in the production of online learning is getting the knowledge and expertise from the mind of the subject matter expert (SME) across and into the course. It’s a tricky process and often full of angst and recrimination. Imagine taking SME content – any document, PowerPoint or video and turning it automatically into online learning. You can.

4. Media (but not mind) rich

As computers have allowed us to deliver media rich experiences, we have, often blindly, ignored the research by Mayer, Clark and many others, showing that media-rich is not always mind-rich. This has resulted in an often garish concoction of movement, graphics, cartoons and animation, that inhibits, rather than enhances learning. There’s maybe not enough ‘edu’ and too much ‘tainment’ in ‘edu-tainment’. We need to pay attention to the research, reduce cognitive overload and focus on the learning. Less is more.

5. Weak Multiple Choice Questions

Long the staple of online learning, yet when in real life does anyone choose an option from a list, as if the mind was a simplistic ATM, choosing from menus? Besotted with MCQs, we have produced low-effort, low retention and low recall learning. It doesn’t have to be this way. Use more effortful, open response learning. The research shows it is superior.

6. Low effort learning

The illusion of mastery is what much online learning produces, the feeling that you’ve learnt things through light-touch exposure and occasional selections from lists. In truth, real learning is learning by doing, real effort, not page turning and exposure to fancy media. Recent reseach turns traditional onine learning on its head. Make the learner make the effort – that’s what results in high retention and recall.

7. Trivial Pavlovian gamification

Does gamification play Pavlov with learners? You can focus on the sort of gamification that Demis Hassabis uses in AI learning – repeated deliberate practice. Don’t make it too easy to sail through, allow learners to fail, make them work, make them do things until they get 100% competence…. That’s true gamification.

8. Impersonal

Good online learning is always a balance between directed and open-learning. There needs to be structure, often quite directed, but there also has to be the opportunity for support, expansion and curiosity. Allow the learner to explore by providing automatically generated links out to extra content. Make your course porous. This is possible with AI.

9. Low retention and recall

Over the last ten years, evidence has emerged (summarized in Make It Stick) that effortful learning matters, that open response is superior to multiple choice and that deliberate and practice matters. We have the ability to use AI to embody and deliver practice based on contemporary learning theory.

10. No practice

The once only, sheep-dip experience was the target of online learning, with its anytime, anywhere offer. Yet online learning simply replaced one type of sheep-dip (offline) with another (online). The fact that they rarely delivered opportunities for reinforcement and practice, was the same in both camps. AI, the algorithmic delivery of simple and effective online learning, through effortful and deliberate practice, can change this.

Conclusion

We can now, for certain types of leaning, produce content at 10% of the cost, in minutes not months, with higher retention and recall. We can avoid the trap of whizz-bang graphics, weak MCQs and Pavlovian ‘collect the coins’ gamification. The new approach, using AI, creates e-learning which is effortful and allows the learner to expand on their learning with access to external resources and further opportunities for practice. It’s called WildFire.

WildFire takes any document, PowerPoint or video and turns it into online learning, within minutes, using AI. More than this, it uses effortful open-response learning to increase retention and recall. Beyond this it automatically curates links to content beyond the course and, in real time, can create online learning from this content. It is unique. For more information see the WildFire website.

AI builds e-learning at 10% of cost, in minutes not months, with higher retention and recall

Paying 20k an hour for e-learning content, over many months, that is laden with noisy pages of text/graphics, punctuated by low retention multiple-choice questions? AI can help you to build content at 10% of the cost, in minutes not months, with higher retention and recall.

Problem

The problem with the traditional, online learning, bespoke content paradigm is the tools. They push vendors and buyers into producing content that has ten major flaws:

1. Expensive to produce

2. Too long to produce

3. Difficulties with SMEs

4. Media (but not mind) rich

5. Weak Multiple-Choice

6. Low effort learning

7. Pavlovian gamification

8. Impersonal learning

9. Low retention and recall

10. No practice

1. Expensive to produce

I ran a large, bespoke, online learning content company. It was very successful but that was back in the day and used the tools of the day. Yet the content produced today costs and looks much as it was 30 years ago, despite the fact that computers are faster, better, cheaper and online. Why does it still cost 20k an hour to produce e-learning content? Because we’re still in the old paradigm of traditional authoring tools and a mindset besotted with appearance, not learning. Imagine reducing that cost by 90% through a radically different approach, using AI and automation. You can.

2. Takes too long to produce

E-learning projects can take three to six months or longer, with lots of process and angst. It’s a highly iterative process, and takes a huge amount of management, definition, design, development and delivery time to produce anything. Imagine doing all of this in minutes, not months. You can.

3. Difficulties with SMEs

By far the most difficult step in the production of online learning is getting the knowledge and expertise from the mind of the subject matter expert (SME) across and into the course. It’s a tricky process and often full of angst and recrimination. Imagine taking SME content – any document, PowerPoint or video - and turning it automatically into online learning. You can.

4. Media (but not mind) rich

As computers have allowed us to deliver media rich experiences, we have, often blindly, ignored the research by Mayer, Clark and many others, showing that media-rich is not always mind-rich. This has resulted in an often garish concoction of movement, graphics, cartoons and animation, that inhibits, rather than enhances learning. There’s maybe not enough ‘edu’ and too much ‘tainment’ in ‘edu-tainment’. We need to pay attention to the research, reduce cognitive overload and focus on the learning. Less is more.

5. Weak Multiple Choice Questions

Long the staple of online learning, yet when in real life does anyone choose an option from a list, as if the mind was a simplistic ATM, choosing from menus? Besotted with MCQs, we have produced low-effort, low-retention and low-recall learning. It doesn’t have to be this way. Use more effortful, open response learning. AI can be used to semantically interpret students answers. The research shows it is superior.

6. Low effort learning

The illusion of mastery is what much online learning produces, the feeling that you’ve learnt things through light-touch exposure and occasional selections from lists. In truth, real learning is learning by doing, real effort, not page turning and exposure to fancy media. Recent reseach turns traditional onine learning on its head. Make the learner make the effort – that’s what results in high retention and recall. Read to remember.

7. Trivial Pavlovian gamification

Does gamification play Pavlov with learners? It so often does, collecting coins, trivial games that just put more cognitive effort into the mix. Or, you can focus on the sort of gamification that Demis Hassabis uses in AI learning – repeated deliberate practice. Don’t make it too easy to sail through, allow learners to fail, make them work, make them do things until they get 100% competence…. That’s true gamification.

8. Impersonal

Good online learning is always a balance between directed and open-learning. There needs to be structure, often quite directed, but there also has to be the opportunity for support, expansion and curiosity. Allow the learner to explore by providing automatically generated links out to extra content. Make your course porous. This is possible with AI.

9. Low retention and recall

Over the last ten years, evidence has emerged (summarized in Make It Stick) that effortful learning matters, that open response is superior to multiple choice and that deliberate practice matters. We have the ability to use AI to embody and deliver practice based on contemporary learning theory.

10. No practice

The once only, sheep-dip experience was the target of online learning, with its anytime, anywhere offer. Yet online learning simply replaced one type of sheep-dip (offline) with another (online). The fact that they rarely delivered opportunities for reinforcement and practice, was the same in both camps. AI, the algorithmic delivery of simple and effective online learning, through effortful and deliberate practice, can change this.

Conclusion

We can now, for certain types of leaning, produce content at 10% of the cost, in minutes not months, with higher retention and recall. We can avoid the trap of whizz-bang graphics, weak MCQs and Pavlovian ‘collect the coins’ gamification. The new approach, using AI, creates e-learning which is effortful and allows the learner to expand on their learning with access to external resources and further opportunities for practice. It’s called WildFire.

WildFire takes any document, PowerPoint or video and turns it into online learning, within minutes, using AI. More than this, it uses effortful open-response learning to increase retention and recall. Beyond this it automatically curates links to content beyond the course and, in real time, can create online learning from this content. It is unique. For more information see the WildFire website.