Amazon’s Alexa is about to get a lot smarter – could it help teach?

The folk at Amazon have a road map for Alexa that will take it to a new level. In the long-term, this could has profound implications for learning. It’s based on these new features:

Better dialogue

Memory

Seamless skills

Personalisation

Better sustained dialogue

First up, you’ll be able to interact without first saying ‘Alexa….’ That’s great, as Alexa turns you into a didactic monster, as if you were speaking to a small child or domestic slave. It will do this by carrying over the context, so that the dialogue can continue, without having to repeat ‘Alexa’, even dialogue at a later time. This carryover feature identifies context and provides replies related to that context, which matters in learning. It will know what you’re trying to learn, as well as how well you’re doing, what you are most likely to need next and support you along the way. This will, eventually, be like having a teacher in your home.

Memory

These improvements towards better language recognition and generation, and more natural dialogue, will be welcome, but that only comes if Alexa can ‘remember’ what you both said earlier. Google Assistant already has this feature, albeit quite primitive. Note that these systems already store your shopping and ‘to do’ lists but you can also ask Google to remember where you stored your keys and so on. This data can inform future learning conversations. Alexa will know what you have learnt previously, your level of competence and can keep you in that learning zone, nothing to easy, nor too difficult. As Alexa will  ‘remember’ what you asked her, and use that information to inform future learning conversations, it truly becomes a teaching assistant. It then starts to have real teacher attributes.

Seamless skills

One problem with Alexa is the rather clumsy process of integrating new skills. This will be more seamless. Rather than having to find skills, they will be streamed into the learning process. You may need some specific tuition on a specific problem or skill, Alexa will provide that opportunity. You may need to know how to perform a specific experiment in science, piece of grammar in language learning, practice cube roots in maths, learn a poem in English…

This matters in learning, as teaching is not some general skill but lots of different integrated skills. With a range of teaching skills - providing learning opportunities, learner engagement, learner support, adaptive learning, personalisation, practice and assessment – Alexa, or something similar, may well turn out to be at first a part-competent teacher., then gain in skills.

Personalisation

Just as Google, social media, Amazon’s online services and Netflix, have sophisticated recommendation engines, so Alexa will get to know, not only you, but other learners, and all of that individual and aggregated data can be used to improve teaching and learning. Like Duolingo, it will not only know what you’ve learnt, it will know what you’re likely to have forgotten. It will also know what the strengths and weaknesses of certain pedagogic approaches will be, and correct the weaknesses. In short, it will learn to be a better teacher by measuring this in terms of the success of millions of learners.

All of this offers very specific services across the teaching and learning journey:

1. Learning opportunities

A home assistant will be able to find, even suggest, new skills and learning opportunities. It may know that you are going to Italy, so offer some tuition in basic Italian. Everything from free courses, MOOCS to microskills, could be on offer. This really could deliver the promise of lifelong learning, something that was never going to be delivered through institutions. If we are to pick up new competences in our lives, we need this type of learning to be available, on demand, cheaply, in our homes.

2. Learning engagement

Learners are lazy. We all waited until the last moment to do our homework, write essays, complete assignments. Many of us fail by simply not doing things in a timely manner. Chatbots are already being used to engage students, push reminders, offer help, even offer help on well being (see Woebot). Engagement can be personalized, nudge-like, and improve the efficacy of learning and reduce dropout. I like it when I get alerts, messages, likes on Facebook, retweets, comments on my blog – that approach should be applied to learning. Engagement cannot be left to the intermittent, erratic and formal processes of institutions, term times, teacher availability and training courses.

3. Learning support

We’ve seen how the Georgia Tech bot ‘Jill Watson’ was an effective teaching assistant, as judged by learners, who put it up for a teaching award. ‘Differ’ is already being sued in Nordic Universities. Quick, polite, constructive help and feedback is what keeps learners going. A teaching assistant, that is available 24/7/365, is precisely what is needed to combat the inefficiencies of current practice, where teaching is subject to the tyranny of limited teacher time. Learners need consistent help when ‘they’ need it, not just when the teacher is available.

4. Adaptive learning

When your own teaching assistant, in your own home, knows who you are, your age, subjects you are taking at school or college, job, competences you need at work, interests and lifelong learning needs, that will be spendid. It will adapt to your current needs and constantly be on hand to help you learn. Learning, in a sense, will become what it needs to be – invisible, simply part of your life. It will also be like a GPS system that knows when you’ve gone off course and literally gets you back on course.

5. Performance support

Most learning does not take place in schools, colleges and universities, but in the workplace. There it becomes more informal. You learn most of what you learn informally, not formally, from colleagues, doing the work and other sources, increasingly online. Imagine a service that simply delivers what you need on demand, when you need it to solve the problem at hand. The invisible LMS may be on the horizon. Chatbots, such as Otto, are already on the market.

6. Practice

How do you get to Carnegie Hall? Practice, practice, practice… Learners need to make the effort to retrieve, apply, generate, elaborate and practice what they learn. This is so easily left to chance. But home technology could allows us to do this efficiently, as part of personalised learning. We know that ‘forgetting’ is endemic in teaching and learning. We forget more of what we’re taught and learn than we ever retain. This technology can deliver, efficiently, and personally, deliberate and spaced practice that combats the forgetting curve. 

7. Assessment

Formative and summative assessment have a lot to gain from voice recognition (identification) to the practice and preparation for exams. Spaced practice and a scheduled approach to a learning journey can be known and delivered by such systems and your learning scheduled. Online exams, especially oral exams, may be delivered through such systems. 

Will take time

This will all take time, as AI is nowhere near delivering many of these skills. There are real challenges here around improvements in speech recognition (accents, background noise, false triggering), the recognition of meaning itself in the spoken word (there are many failures), dialogue management (not easy as context is complex) and personalization (data issues, relevance). But the promise is clear, as some have already been mastered, and more are in the pipeline.

Trojan horse

It may not be Alexa, or Google Home, but chatbot assistants in the home are here to stay. This technology is about to get a lot smarter. We’re getting a glimpse into a future where every home will have a teacher. Home schooling will start to develop, at first with assistance for homework, then some active learning (especially languages) then other subjects. Parents pay a ton of cash for extra home tuition, but could this eventually be available for free? Let’s suppose this is successful. Could such assistants become teachers, pushing engagement, delivering adaptive, personalized learning, sensitive to deliberate and spaced practice, with lots of retrieval, formative assessment and even exam practice. Imagine a future where you can go at your own pace in a subject, know with certainty that you’ve reached a certain level, self-assess, then simply sign up for a formal assessment. This is an interesting Trojan Horse.

Conclusion

This is the start of something interesting. I predicted some time back that Amazon may well be the company to create a Netflix of learning company and saw their progress as useful in terms of their AI.  If this roadmap works, they have a device that teaches, through dialogue, as if you had a teacher in every home. Imagine the impact in developing countries, the fact that it is cheap and can scale – scale globally.

Lords report ‘AI in the UK: ready, willing and able?’ Let’s be honest - ready – no, willing – sort of, able not really…

Politicians love a good report. Problem is, we produce them like pills, in the hope that they will make things better, when all they do is act as a placebo. It seems as though things are happening but they ain’t. Whenever we are worried by something, in this case AI, we get a bunch of people, usually well past their sell by date to produce a ‘report’. To be fair this is a substantial piece of work, at 420 numbered sections and 74 recommendations, but it’s all over the place, lacks focus and at times is way off the mark.

Ethics heavy

First, I’m not sure about a document that tries to climb and descend a mountain at the same time. No sooner has something been stated as a way forward, than it’s drowned under a wave of repetitive moralising. Although they wisely stop short at blanket regulations, it full of pious statements about dangers, challenges and ethics. As Hume said, you can’t derive an ought from an is – and that’s exactly what they do, over and over again. It is hopelessly utopian in its assumption, even that AI can be defined, never mind regulated. Perhaps too much is attributed to its efficacy and promise. In the end it’s just software.

Crass identity politics

There’s the usual obsession with identity politics and the idea that bias in algorithms will be solved as follows,  “The main ways to address these kinds of biases are to ensure that developers are drawn from diverse gender, ethnic and socio-economic backgrounds.” Oh dear – not that tired old idea. All this shows is that the writers of the report have succumbed to the diversity lobby or suffer from a series of human biases, starting with confirmation bias – the confirmation that diversity will solve mathematical and ethical problems. Bias is a complex set of problems in both human affairs and AI – it needs sharp analysis, not Woolworth’s‘pick and mix’ team building. There’s one really puzzling sentence on this that sums their naivety up perfectly.“The prejudices of the past must not be unwittingly built into automated systems, and such systems must be carefully designed from the beginning.” Put aside the fact that this is largely what the House of Lords does for a living, it

is not even wrong. AI has 2300 years of mathematics behind it – from the first identified algorithm in Euclid’s Elements, through centuries of theory in logic, probability, statistics and other areas of mathematics. AI is built on the past.

Exploiting AI

The UK has an excellent track record of academic research in the field of artificial intelligence, but there is a long-standing issue with converting such research into commercially viable products. Damn right. They’re once again pained over the age-old problem the UK has on spending oodles of public money on world-class research, which doesn’t translate into commercial success. There is the usual error of equating AI SMEs with University start-ups. Actually, many have nothing to do with Universities. We need to support SMEs with business ideas. Yet where are the people like me, who put their own money and energy into starting an AI company and invest in others? Every AI academic in the land seems to have been consulted, along with many who wouldn’t know AI of they saw it in their soup. We know that our HE system is deeply anti-corporate. To assume that research equals success is a complete non sequitur. We need to encourage innovation AND commerce around AI – not just hose yet more money into Universities.

Usual suspects

Then there’s the usual tired old suspects. First, a Global Summit. Really? Nothing like a junket to advance our AI capability. Then a code of conduct. Yet another one? Politicians do love codes of conduct. Then there is the predictable call for a quango – creatively named the ‘AI Council. It’s all so unimaginative.

AI in education

But the worst section by far is the section on EDUCATION. There is a great deal of soul searching about AI in education but only in the sense of teachers and curricula about AI. The big win here is using AI to improve and accelerate teaching and learning. This is what happens when you only talk to teachers about AI. It’s all about the curriculum and nothing about actual practice. This is a massive, wasted opportunity. I’m selling an AI learning company to the US as I write this. We’re already losing ground. There’s something called the Hall-Presenti review – whatever that is. I’ve worked in AI in learning for years, run an AI company (WildFire), have invested in AI in learning companies, speak all over the world on the topic, write constantly on the topic – yet have no idea what this is. That’s the problem – Parliament is an echo-chamber. They don’t really speak to the people who DO things.

Conclusion

To be fair there’s some good stuff on healthcare and a few shells over the bow for defence and autonomous weapons, but it’s a bit tired, pious and lacks punch. It will, of course, fall stillborn from the press.

The Fallacy of ‘Robot’ Teachers

I talk a lot on AI in learning but these days you can’t move for robot teacher articles and presentations, usually some diminutive piece of white plastic, sometimes, oddly, with a tablet stuck on its chest, that invariably responds with silence, something banal or falls over. This is seriously flawed thinking. I call it the ‘Robot Fallacy’, the idea that AI in learning is largely about physical robots. Fuelled by a century of cinema, where killer, and sometimes friendlier, robots dominate, due to the fact that it is a visual medium and needs ‘characters’ in drama, robots signify lazy thinking about AI. In practice, 99% of AI has nothing to do with robots. We are all enmeshed in AI, as AI is the new UI. Google, Facebook, Twitter, Netflix and most other online services are all mediated by AI, with not a robot in sight. Sure, Amazon uses them in its warehouses but this is a tiny portion of the process.

Robot teachers are, largely, as stupid an idea as robot drivers in self-driving cars, robot cleaners pushing a robot vacuum cleaner around the floor or a robot pilot sitting in the cockpit running autopilot on a plane. Auto pilot is a sophisticated piece of invisible software with secondary systems. The whole point of these self-driven systems is to ‘eliminate’ humans. Sure there’s a role for companion robots for people with severe learning difficulties or the very young, but on the whole the idea of a robot teacher is ridiculous. The point of this technology is to augment or disintermediate the physical teacher. Your automated banking is not a robot teller, it is online. Not a robot in sight.

The most ridiculous examples I know of AI in learning, are robot projects. They get tons of attention and grants. Doomed to succeed, they are usually a simple chatbot inside a big bit of plastic with barely moveable parts. Take Professor Hiroshi Ishiguro from Japan, whose robot self gives lectures, while he swans around conferences. To be fair his robot self looks more human than himself. This is bizarre and says more about the useless pedagogy of the lecture than any useful lessons in learning. My sense is that it’s a form of device fetish – education has disastrously focused on spending money on devices and not solutions to pedagogic problems. Tablets have been showered on schools in acts of folly. The robot thing is simply a another alluring device.

Robots in factories, that find, select and porter goods around factories make sense. Robots in manufacturing with their precision, speed and strength makes sense. Self-driving cars, make sense. Robot vehicles on Mars make sense. Robot teachers make no sense.

It is not just that AI has no significant cognition. AI is an ‘idiot savant’, incredibly good at specific, narrowly defined tasks but magnificently bad at generalist tasks – namely being a teacher. There is a huge amount of unwarranted hype around AI, not helped by the robotic presentation of robots as teachers, whereas in practice, AI can only be applied online to many specific parts of the learning journey. So far it is a story of augmentation not automation.

Find things out

That is not to say that AI has no role to play in learning. In fact, it will shape what we learn, why we learn ad how we learn. AI, in my opinion, will be the single most important technology to shape the learning landscape in the future. In many ways it already has. Google changed things for the better, a useful tool that heralded an irreversible pedagogic shift. Amazon revolutionized access to books, online and offline, as well as self-publishing. AI also shapes social media, as algorithms select personalized information on your timelines. It is a shame that the only form of AI you’re likely to see formally adopted by education is plagiarism checkers – but there you go – education can be a slow learner.

Online learning

That first wave of Google-led search and social media had had a profound influence on the learning landscape but the second wave is more significant, with AI-driven online content creation, curation, consolidation, adaption, personalization, retrieval and assessment. Tools now exist to do all of these using AI, and the efficacy is clear. Take one example, content creation. WildFire creates content in minutes not months, at a fraction of the cost of traditional online learning. Guided curation using AI is also possible. Spaced practice tools are now readily available and online assessment has benefited from online identification, face recognition, keyboard pattern checks and so on.

Learner interfaces

Another feature of this second generation AI is the shift in interfaces for learning. With NLP (Natural Language Processing) we also have text to speech (automated podcasts) and speech to text (speech recognition). This has opened up a switch from poor retention multiple-choice to open input, as well as spoken interaction. With WildFire, we have open input and interactive speech recognition for both navigation and interactive retrieval.

Chatbots

One recent advance has been in chatbots, which uses our natural propensity for dialogue to teach and learn. This return to a more Socratic approach to learning as been enabled by smart AI. These chatbots are used to find things, student support, deliver learning and mentor. Otto is a chatbot that sits above your content and find answers and learning opportunities for you as performance support, when you need it. We’ve developed an assessment chatbot that delivers questions on what you’ve learnt. Other chatbots provide help and support on courses. (10 uses of chatbots in learning)

Conclusion

So AI is present right across the learning journey. It can already deliver answers to questions, find things, create content, curate, allow natural language input and output, deliver personalized, adaptive learning as well as enable online assessment. Major learning services, such as Duolingo, are now delivering language learning to hundreds of millions of learners. With the introduction of software that learns (machine learning) we have software that get better the more you use it. Teachers have brains that are superb at general teaching but, bit by bit, aspects of teaching and learning practice will be automated. That has already happened. Every learner uses AI to search and find. Almost every learner uses AI-mediated social media. Teachers use it for CPD. AI at present augments teaching but a teacher is not replicable or scalable. If we want to solve the problem of increasing demand for learning we need to scale the process of teaching and learning. That has little or nothing to do with silly, teacher robots but everything to do with AI.

AI-driven speech may revolutionise online learning

Over the last year or so we built the world’s first AI content creation service, WildFire, which creates online learning in minutes not months, at a much lower cost and with high retention. The reason for claiming it is high retention, is that we largely abandoned multiple-choice-questions for open-input, making the learner think, recall and actively input their thoughts. It is this ‘effortful’ learning that really matters in learning. This worked well and we have delivered online learning on factual knowledge, high-end academic content, processes, procedures and management content to a range of audiences in large organisations, from apprentices to high-end clinicians, in finance, healthcare, travel and manufacturing. Having seen how well open-input worked, we turned our attention to the use of AI to go several steps further and improve the interface. What if the learner could simply speak the answers? ... it was a revelation.

Speech

We learn to speak almost effortlessly, whereas, writing takes many years. So why not exploit what we do everyday in our lives - use speech input. It was thought that women spoke much more than men, a myth started in The Female Brain, by Louann Brizendine, who claimed that, whereas women spoke on average 20,000 words a day, it was only 6,000 for men. This proved to be nonsense. An actual study, at the University of Arizona by Mehl (2007), using an electronic recording device to sample everyday speech from 396 people found that we speak, on average, around 16,000 words a day, with no significant difference between men and women.

This is much greater than the average for writing. So it makes sense to use speech in learning. Consider also that if spelling is not part of the learning, you eliminate problems around misspelling, especially for those who are nervous on that score or who may have dyslexia. On top of this you do not have to make the physical effort to move a cursor around the screen into a field, then physically type.

Retention

However, it is interesting to compare different forms of input in terms of retention. So, you think of an answer, then:

   CHOOSE your answer from a list (multiple choice)

   TYPE your answer

   SPEAK your answer

CHOOSE (MCQs)

It is clear that simply clicking on an already provided answer from a list is the least effective of the three. The answer is there in front of your eyes, you have a 25% chance of getting it right without knowing anything, questions are often designed so that you can guess and the distractors are often remembered rather then the correct answers. It makes you wonder why the online learning industry is so wedded to MCQs.

TYPE

This has the advantage of making you recall the answer into your brain first (a powerful reinforcement event), then actively type in the answer, another reinforcement event, without having been given the answer or suffering from the drawbacks of simply choosing from a pre-written list. We have found this to be a much more powerful way of learning.

SPEAK

Things get interesting here, as you are communicating directly, without any of the artificiality of choosing from a list or typing. My initial impression (not based on any studies) is that this may be even better. Being hands free, your attention and cognitive focus is entirely on thinking and expressing your thoughts. None of your cognitive bandwidth is taken up by moving the cursor, typing and letter-by-letter spelling. You get a focus on meaning but there’s an additional advantage, as you get more of a flow and the learning is faster.

Podcasts

Using another form of AI, text to speech, we can also, automatically, create podcasts. This is built into the service. Simply tick a box and your online learning will create a podcast of the module or page by page speech. This is a useful supplement to the active learning.

Context

In addition, as we have audio only learning, including navigation, using the words, NEXT, BACK, GO and SCROLL, so we can place the learning experience within VR, which we have done, instantly and cheaply. We know that context helps retention, so speech input allows a further level of retention to be achieved. This is getting interesting in say, training fror healthcare professionals in a hospital or cabin crew inside an aircraft.

Conclusion

Simultaneously, using different forms of AI, we hope to have increased the efficacy of online learning by the:

1. Superfast creation of content

2. Higher retention open-input

3. Higher retention speech input

4. Automatically created podcasts

5. Full 3D VR delivery

All at lower costs and far greater speed than traditional and expensive methods. If you are interested we can show you all of this by Skype. Contact us here.

Bibliography

Mehl M (2007). Are Women Really More Talkative Than Men? Science ,Vol. 317, Issue 5834, pp. 82