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.

Are 'chatbots' a gamechanger in learning? 10 reasons and some warnings!

This was the debate motion at the LPI conference in London. I was FOR the motion (Henry Stewart was AGAINST) and let me explain why....

1. AI is a gamechanger

AI will change the very nature of work. It may even change what it is to be human. This is a technological revolution as big as the internet and will therefore change what we learn, why we learn and how we learn. The Top Seven companies by market cap all have AI as a core strategy; Apple, Alphabet, Microsoft, Amazon, Tencent, Facebook and Alibaba. AI is a strategic concern for every sector and every business, even learning. Nevertheless we must be careful not to hype their functionality. They are not capable of fully understanding every question you throw at them, neither do they have the general human capabilities of a teacher. They are, essentially, good within narrow parameters. We must manage expectations here.

2. Evidence from consumers

Several radical shifts in consumer online behavior move us towards chatbots. First the entry of voice activated bots into the home and connected the the IoT (Internet of Things) – Amazon Alexa and Google Home. My Alexa is linked to my internet music service, lights in my home and I use it a a timer for Skype calls and events during the day. It is integrated into my workflow.
3. Rise of voice
The rise of ‘voice’ as a natural form of communicating with technology – Siri, Cortana and other similar services. Over 10% of all search is now by voice. We have been using computer generated voice from text files in WildFire for some time. It adds some humanity to what can often be seen as the sterility of online learning.
4. Chat has superseded social media
The switch from social media to messaging/chat apps took place in late 2014 and the gap is growing – chat is the home screen for most young people. Look over someone's shoulder and you're far more likely to see a 'chat' screen than a website. The lesson here is that chatbots allow us to play to the natural online behaviours of learners. Then again, chat with another human is a little different from chat with a chatbot, far more limited.
5. Social
We are social animals and it was no accident that chatbots first emerged in social tools such as facebook and Slack. They are a natural extension of existing social learning, allowing us to place them in the workflow. Chatbots, like Otto, are designed to lie within these workflow tools, moving learning from the LMS to a more demand-driven model.

6. Pedagogy in chatbots

Most teaching is through dialogue. The Socratic method may have been undermined by blackboards and their successors through to PowerPoint, but voice and dialogue are making a comeback. Speaking and listening through dialogue is our most natural interface. We’ve evolved these capabilities over 2 million years, it’s natural and we’re grammatical geniuses aged 3, without having to be taught to speak and listen. Within dialogue lies lots of pedagogically strong learning techniques; retrieval, elaboration, questions, answers, follow ups, examples and so on. It just feels more natural. Once again, however, we must be careful in thinking of chatbots as people. They are not conscious and not capable of full-flow, open dialogue.

7. Evidence in learning

An exit poll taken by Donald Taylor, from Learning and Technologies conference this year, showed Personalised learning at No 1 and AI and No 3. The interest is clearly strong and there’s lots of real projects being delivered to real clients from WildFire, Learning Pool and so on. However, be careful about vendors telling you their chatbot is true AI. Many are not. It is fiendishly difficult to do this well, so most are very structured, branching bots with limited functionality.

8. Chatbots across the learning journey

There are now real chatbot applications at points across the entire learning journey. I showed actual chatbot applications in learning in the following areas:

   Recruitment bots   
   Onboarding bots

   Learner engagement bots

   Learner support bots

   Invisible LMS bots

   Mentor bots
   Reflective bots

   Practice bots

   Assessment bots

   Wellbeing bots
The problem we have is that most bots are actually just FAQ bots. They are pixies for search. In the learning game they have much more potential. My own view is that we'll see a range of bot types emerge that will match the needs of learners and organisations. We must think more expansively around bots if they are to play a significant role in learning.

9. They’re learners

An important feature of modern chatbots, compared to ELIZA from the 1960s, is the fact that they now learn. This matters as the more you train and use them, the better they get. We used to have just human teachers and learners, we now have technology that is both a teacher and learner. This means one can take advantage of bot services from some of the large tech companies. But be careful - it's not cheap and they tend t swap out functionality with little sensitivity around your delivery.

10. It’s started

Technology is always ahead of the sociology, which is always ahead of learning and development. Yet, we see in these many projects, even with relatively primitive technology and emerging trend – the use of technology delivered chatbot learning. In time, this will happen. I've been involved in several projects now across a range of chatbot types.

Objections

Nigel Paine chaired the debate with his usual panache and teased questions out of the audience and the real debate ensued. The questions were rather good.

Q Has AI has passed the Turing test?

First, there are many versions of the Turing test but the evidence from the many chatbots on social media all the way to Google Duplex, shows that it has been passed but only in limited areas. Not for long, sustained and very detailed dialogue, but certainly within limited domains. Google Duplex showed that we’re getting there on sustained dialogue and the next generation of Amazon’s Alexa and Google Home will have memory, context and personalisation in their chatbot software. It will come in time.

Q AI can never match the human brain

This is true but not always the point. We didn’t learn to fly by copying the wings of a bird – we invented new technology – the airplane. We didn’t go faster by looking at the legs of a cheetah, we invented the wheel. The human brain is actually a rather fragile entity. It takes 20 years and more of training to make it even remotely useful in the workplace, it is inattentive, easily overloaded, has fallible memory, forgets most of what its tries to learn, has tons of biases (we're all racist and sexist), sleeps 8 hours a day, we can’t download, can’t network and we die. But it is true that it is rather good at general things. This is why chatbots are best targeted at specific uses and domains, such as the species of chatbot I listed earlier.

Q Chatbots v people

Michelle Parry-Slater made a good point about chatbots not replacing people but working alongside people. This is important. Chatbots may replace some functions and roles but few suppose that all people will be eliminated by chatbots. We have to see them as being part of the learning landscape.

Q Chatbots need to capture pedagogy

Good question from Martin Couzins. Chatbots have to embody good pedagogy and already do. Whether it’s models of engagement, support, learning objectives, invisible LMS, practice, assessment or well being, the whole point is to use both the interface and back-end functionality (important area for pedagogic capture) to deliver powerful learning based on evidence-based theory, such as retrieval, effortful learning, spaced-practice and so on. This will improve rather than diminish or ignore pedagogy. In all of the examples I showed, pedagogy was first and foremost.

Q Will L and D skills have to change

Indeed. I have been training Interactive Designers on chatbot and AI skills as this is already in demand. The days of simply producing media assets and multiple choice questions is coming to a close – thankfully.

Conclusion

Oh and we won the debate by some margin, with a significant number changing their minds from sceptics to believers along the way! That doesn't really matter, as it was a self-selecting audience - they came, I'd imagine, as they were curious and had some affinity with the idea that chatbots have a role. My view is these debates are good at conferences - by starting with a polarised position, the audience can move and shift around in the middle. The audience in this session were excellent, with great questions, as you've seen above. Note to conference organisers - we need more of this - it energises debate and audience participation.

10 uses for Chatbots in learning (with examples)

As chatbots become common in other contexts, such as retail, health and finance, so they will become common in learning. Education is always somewhat behind other sectors in considering and adopting technology but adopt it will. There are several points across the learner journey where bots are already being used and already a range of fascinating examples.

1.    Onboarding bot

Onboarding is notoriously fickle. New starters in at different times, have different needs and the old model of a huge dump of knowledge, documents and compliance courses is still all too common. Bots are being used to introduce new students or staff to the people, environment and purpose of the organisation. New starters have predictable questions, so answers can be provided straight to mobile, directed to people, processes or procedures, where necessary. It is not that the chatbot will provide the entire solution but it will take the pressure off and respond to real queries as they arise. Available 24/7 it can give access to answer as well as people. What better way to present your organization as innovative and responsive to the needs of students and staff?

2.    FAQ bot

In a sense Google is a chatbot. You type something in and up pops a set of ranked links. Increasingly you may even have a short list of more detailed questions you may want to ask. Straight up FAQ chatbots, with a well-defined set of answers to a predictable set of questions can take the load off customer queries, support desks or learner requests. A lot of teaching is admin and a chatbot can relieve that pressure at a very simple level within a definite domain – frequently asked questions.

3. Invisible LMS bot

At another level, the invisible LMS, fronted by a chatbot, allows people to ask for help and shifts formal courses into performance support, within the workflow. LearningPool’s ‘Otto’ is a good example. It sits on top of content, accessible from Facebook, Slack and other commonly used social tools. You get help in various forms, such as simple text, chunks of learning, people to contact and links to external resources as and when you need them. Content is no longer sits in a dead repository, waiting on you to sign in or take courses, but is a dynamic resource, available when you ask it something.

4. Learner engagement bot

Learners are often lazy. Students leave essays and assignments to the last minute, learners fail to do pre-work, and courses– it’s a human failing. They need prompting and cajoling. Learner engagement bots do this, with pushed prompts to students and responses to their queries. ‘Differ’ from Norway does precisely this. It recognizes that learners need to be engaged and helped, even pushed through the learning journey, and that is precisely what 'Differ' does.

5. Learner support bot

Campus support bots or course support bots go one stage further and provide teaching support in some detail. The idea is to take the administrative load off the shoulders of teachers and trainers. Response times to emails from faculty to students can be glacial. Learner support bots can, if trained well, respond with accurate and consistent answers quickly, 24/7.

The Georgia Tech bot Jill Watson, and its descendants, responds in seconds. Indeed they had to slow its response time down to mimic the typing speed of a human. The learners, 350 AI students, didn’t guess that it was a bot and even put it up for a teaching award.

6. Tutor bots

Tutor bots are different from chatbots in terms of the goals, which are explicitly ‘learning’ goals. They retain the qualities of a chatbot, flowing dialogue, tone of voice, exchange and human (like) but focus on the teaching of knowledge and skills. Straight up teaching is another approach, where the bot behaves like a Socratic teacher, asking sprints of questions and providing encouragement and feedback. This type of bot can be used as a supplement to existing courses to encourage engagement. Wildfire, the AI content generation service uses bots of this type to deliver actual teaching on apprenticeship content, as a supplement to courses, also built using AI, in minutes not months. Once the basic knowledge has been acquired, the bot tests the student as well as getting them to apply their knowledge.

7. Mentor bot

The point of a bot may not be to simply answer questions but to mentor learners by providing advice on how to find the information on your own, to promote problem solving. AutoMentor by Roger Schank,  is one such system, where the bot knows the context and provides, not just FAQ answers but advice. Providing answers is not always the best way to teach. At a higher-level chatbots could be used to encourage problem solving and critical skills, by being truly Socratic, acting as a midwife to the students behaviours and thoughts. Roger Schank is using these in defence-funded projects on Cyber Security.

As the dialogue gets better, drawing not only on a solid knowledge-base, good learner engagement through dialogue, focused and detailed feedback but also critical thought in terms of opening up perspectives, encouraging questioning of assumptions, veracity of sources and other aspects of perspectival thought, so critical thinking could also be possible. Bots will be able to analyse text to expose factual, structural or logical weaknesses. The absence of critical thought will be identified as well as suggestions for improving this skill by prompting further research ideas, sound sources and other avenues of thought. This ‘bot as critical companion’ is an interesting line of development.

8. Scenario-based bots

Beyond knowledge, we have the teaching and learning of more sophisticated scenarios, where knowledge can be applied. This is often absent in education, where almost all the effort is put into knowledge acquisition. It is easy to see why – it’s hard and time consuming. Bots can set up problems, prompt through a process, provide feedback and assess effort. Scenarios often involve other people this is where surrogate bots can come in.

9. Practice bots

Practice bots, literally take the role of a customer, patient, learners or any other person and allows learners to practice their customer care, support, healthcare or other soft skills on a responding person (bot). Bots that act as revision bots for exams are also possible.

A bot that mimics someone can be used for practice. For example, the boy with attitude ‘Eli’, developed by Penn State, that mimics an awkward child in the classroom. It is used by student teachers to practice their skills on dealing with such problems before they hit the classroom. Duolingo uses bots after you have gathered an adequate vocabulary, knowledge of grammar and basic competence, to allow practice in a language. This surely makes sense.

10. Wellbeing bots

If a bot is being used in any therapeutic context, its anonymity can be an advantage. From Eliza in the 60s to contemporary therapeutic bots, this has been a rich vein of bot development. There is an example of the word ‘suicidal’ appearing in a student messenger dialogue, that led to a fast intervention, as the student was in real distress. Therapeutic bots are being used in controlled studies to see of they have a beneficial effect on outcomes. Anonymity, in itself, is an advantage in such bots, as the learner may not want to expose their failings.

Bots such as ‘Elli ‘ and ‘Woebot’ are already being subjected to controlled trials to examine the impact on clinical outcomes.

Bot warning

The holy grail in AI is to find generic algorithms that can be used (especially in machine learning) to solve a range of different problems across a number of different domains. This is starting to happen with deep learning (machine learning). The idea is that the teacher bot will replace the skills of a teacher, not just be able to tutor in one subject alone, but be a cross-curricular teacher, especially at the higher levels of learning. It could be cross-departmental, cross-subject and cross-cultural, to produce teaching and learning that will be free from the tyranny of the institution, department, subject or culture in which it is bound. Let’s be clear, this will not happen any time soon.  AI is nowhere near solving the complex problems that this entails. If someone is promising a bot will replace a teacher – show them the door. Bots will augment not automate teaching.

We have to be careful about overreach here. Effective bots are not easy to build, have to be ‘trained (in AI-speak ‘unsupervised’) and are difficult to build. On the other hand trained bots, with good data sets (in AI-speak ‘supervised’), in specific domains, are eminently possible. Another warning is that they are on a collision course with traditional Learning Management Systems, as they usually need a dynamic server-side infrastructure. As for SCORM – the sooner it’s binned the better. Bots fit n more naturally into the xAPI landscape.

Conclusion

Chatbots have real potential in a number of learning activities, all along the learning journey, not as a general; ‘teacher’ but in specific applications within specific domains. They need to be trained, built, tested and improved, which is no easy task, but their efficacy in reducing the workload of teachers, trainers, lecturers and administrators is clear. The dramatic advances in Natural Language Processing have led to Siri, Amazon Echo and Google Home. It is a rapidly developing field of AI and promises to deliver chatbot technology that is better and cheaper by the month.

As a bot does not have the limitations of a human, in terms of forgetting, recall, cognitive bias, cognitive overload, getting ill, sleeping 8 hours a day, retiring and dying - once on the way to acquiring, albeit limited, skills, it will only get better and better. The more students that use its service the better it gets, not only on what it teaches but how it teaches. Courses will be fine-tuned to eliminate weaknesses, and finesse themselves to produce better outcomes.

We have seen how online behaviour has moved from flat page-turning (websites) to posting (Facebook, Twitter) to messaging (Txting, Messenger). We have seen how the web become more natural and human. As interfaces (using AI) have become more frictionless and invisible, conforming to our natural form of communication (dialogue), through text or speech. The web has become more human.

Learning takes effort. Personalised dialogue reframes learning as an exploratory, yet still structured process where the teacher guides and the learner has to make the effort. Taking the friction and cognitive load of the interface out of the equation, means the teacher and learner can focus on the task and effort needed to acquire knowledge and skills. This is the promise of bots. But the process of adoption will be gradual.

Finally, this at last is a form of technology that teachers can appreciate, as it truly tries to improve on what they already do. It takes good teaching as its standard and tries to support and streamline it to produce faster and better outcomes at a lower cost. It takes the admin and pain out of teaching. They are here, more are coming.

One thing often happens at keynotes and conferences. It surprised me….

Ready to step on stage for 2500 people in a huge theatre, the Grieghallen in Bergen Norway. It really felt like ‘The Hall of the Mountain King’. Given talks in many countries in Europe, US, Asia and Africa since GenAI launched and something happens at these events. I had exactly the same experience at a talk I gave the next day.

Time and time again, someone with dyslexia, or with a son or daughter with dyslexia, came up to me to discuss how AI had helped them. They describe the troubles they had in an educational system that is obsessed with text. Honestly, I can’t tell you how often I’ve had these conversations. 

They rightly want to tell their story, as it has often been one of struggle, in a system that often ignores them, where they have had to find their own way to overcome their problems, or see institutions ban the very tools they need to survive. It is always heartfelt.

Text on blackboards, text-based subjects, textbooks, text-based homework, text-based exams. I now wonder at the simple fact that we send our kids off to school aged 5 or so, to emerge at 20+ having done not much more that read or write text. Is it any wonder we have skills shortages? The net results may be causing problems, with a text-trained graduate, managerial class, high on report writing, bureaucracy and rules, but low on operational and social skills.

AI is welcomed by those with dyslexia, and other learning issues, helping to mitigate some of the challenges associated with reading, writing, and processing information. Those who want to ban AI want to destroy the very thing that has helped most on accessibility. Here are 10 ways dyslexics, and others with issues around text-based learning, can use AI to support their daily activities and learning.

Text-to-Speech & Speech-to-Text Tools

This two-way street uses AI to convert difficult to read text to speech and speech to text where that is required in the system. Both are often mentioned, as text to speech cuts out the need to read allowing dyslexics and others to listen rather than read. This reduces the cognitive load associated with decoding and dealing with written text.  Its sibling, transcription, converting the spoken word into text help dyslexics write assignments, essays, emails, or notes more easily by speaking their thoughts instead of typing. These are now built into smartphones. Texts are often photographed and OCR turns them into text then to speech.

Grammar and Spelling Assistants

Dyslexics often struggle with spelling and grammar, so AI-powered writing assistants, with real-time corrections and suggestions, making writing more accurate and less frustrating, are a boon. These have become normalised and built-in to contexts where text is required. There are also tools like Grammarly, that take things a step further.

Comprehension Tools

AI can break down complex texts, summarise information and provide definitions or explanations for difficult words or concepts. This can make reading less daunting and more manageable for dyslexics. Apps like Rewordify simplify complex language, while any Chatbot can summarise to provide quick summaries of long articles or papers.

AI in Note-Taking

We have been involved in building AI features for Glean, who are a market leader in helping those with disabilities in learning, on note taking. To be honest, any learner would benefit from AI assisted note taking. AI can help dyslexics take notes more efficiently by transcribing what the teacher/lecturer says, summarising long texts or lectures, converting handwritten notes into digital format, and organizing information in a way that’s easy to understand. It can also be used to generate retrieval practice quizzes, expand notes, find links to useful sources and so on. Good examples are Glean and tools like Otter.ai can transcribe and summarise meetings or lectures, while apps like OneNote or Evernote can convert handwritten notes into searchable text and help organise information.

Visual and Multisensory Tools

Dyslexics often benefit from visual aids and multisensory learning techniques and as AI can create interactive, visual learning experiences that help dyslexic learners grasp complex concepts without relying solely on text, the creation of mindmaps, diagrams and so on, can help in organizing thoughts visually.

Translation

For dyslexic individuals who are learning a new language or need to translate text, chatbots are now AI-powered translation tools that can simplify and assist in understanding and producing text in multiple languages. Google Translate and language learning apps like Duolingo use AI to provide real-time translations and language learning support. But AI chatbots can take entire books and do this in one go.

Chatbot assistants

AI, voice enabled, digital assistants can help dyslexics manage daily tasks, set reminders, dictate and send messages, and control smart home devices through voice commands, reducing the need for reading and writing. These assistants can answer questions, engage in full dialogue and act as tutors. They can also handle tasks such as setting alarms, creating to-do lists, searching the web, all through simple voice interactions.

AI-Powered Personalized Tutors

Self-paced learning allows the dyslexic to go at their own pace and not be stymied by fast based delivery. Pace, style, difficulty and level of language can all be delivered using AI. It is astonishing how ‘academic’ text (often obtuse and badly written) is delivered to novices. It is rife in medical education. The personalised approach can make learning more accessible and effective.

AI-Powered Personalized Tutors

One step beyond self-paced material is AI-driven tutors which can provide one-on-one support, adapting lessons to suit the pace and learning style of dyslexic individuals, offering tailored explanations, practice exercises, and feedback. These tutors are now integrated into platforms and can help dyslexic students with personalized learning experiences. Khanmigo and Duolingo are good examples.

AI Accessibility Features in Devices

Smartphones, tablets, and computers now come with built-in AI-powered accessibility features that can be customised for dyslexic users. These include voice commands, screen readers, and customizable display settings. These include Apple's VoiceOver, Android's TalkBack, and Windows Narrator, all examples of AI-driven features that enhance device usability for those with dyslexia.

Conclusion

It is often forgotten that huge numbers of disadvantaged learners leverage AI tools on their own. They are the original ‘AI on the SLY’ users. Individuals with dyslexia have been using these tools to overcome some of the challenges they face with reading, writing, and processing information, making it easier to learn, work, and communicate effectively. We have a lot to learn from them, especially our almost fanatical obsession in education with the written word. So next time you hear someone who wants to ban AI in learning, think again. Having finally found solutions to the problem, do not throw them back into a world where they feel abandoned again.