On the left is the core of what I taught last year. Malcolm Knowles, in the 1970s and 80s, described adult learners as self-directed, internally motivated, and problem-centred, and contrasted that with children. On the right is what the research supports. First, a major medical education guide, AMEE Guide 83, says the split between adult and child learning “now seems to be artificial”. Most of the principles apply to children too. It is better thought of as a continuum. Second, Knowles himself called these assumptions, not a tested theory. A 2002 review of the research on andragogy found it inconclusive after three decades, partly because nobody agreed on what to measure. Third, the claim that adults are driven from within. A 2002 essay on medical students argues that internal and external motivation are mixed together and change with context. You want to learn medicine and you want to match. Both are real. What survives: your experience is a resource, and relevance matters. Those are good design rules. They just do not make adults learn in a different way from children. (Mezirow’s transformative learning describes how people change their frame of reference. It is a description more than a tested model, so I have dropped it from the slides.)

This is, I think, the most important finding for this course. On the left: a Harvard physics study. Students were randomly assigned to active classes or to lectures by experienced, highly rated lecturers, with the same content and handouts. The active-class students learned more on the test. But they rated their own learning lower. The effort felt like failure. On the right: a JAMA systematic review of physicians. When they compared physicians’ ratings of themselves with outside measures of competence, most comparisons showed little, no, or even an inverse relationship. Self-ratings were least accurate among the least skilled physicians and among the most confident. \[Click\] Why this matters for AI. A chatbot gives you a fluent, confident, well-organised answer. Reading it feels like understanding. But feeling that you learned and actually learning are different things, and you, like everyone, are not good at telling them apart from the inside. So you need ways of checking that do not depend on how it feels.

In 2013, five cognitive psychologists rated ten common study techniques by how useful they are, based on whether the benefits hold across ages, materials, and kinds of test. Most of you already do this: Anki and question banks are testing and spacing. So I will be quick, and the point is the AI link coming up. Two came out on top. Testing yourself: pulling the answer out of your memory rather than reading it again. And spacing: coming back to material over days and weeks rather than cramming. In the middle: explaining why something is true, explaining it to yourself step by step, and mixing different kinds of problems together. At the bottom: rereading, highlighting, summarising. Rereading and highlighting are the ones students report using most, because they feel productive. A 2008 review in Medical Education argued for exactly this in medicine: test often, space it out, give feedback. You will see these ideas in the course design: a weekly journal where you recall what you tried, small groups where you explain it out loud, and a routine that brings you back every week.

So if “adults are internally motivated” is too simple, what do we know about motivation? Self-determination theory, from Deci and Ryan, is one of the best-tested theories of motivation in psychology, and there is an AMEE guide applying it to medical education. It says people stay motivated when three needs are met: autonomy, real choice; competence, seeing yourself improve; and relatedness, doing it with other people. It also distinguishes motivation that comes from within, because you value the thing, from motivation that is controlled from outside, by grades or pressure. A Dutch study of 383 medical students found that motivation from within went with deeper study strategies and more effort, and through those, better grades. On the right is how this course is built around it. The course is ungraded on purpose. You choose what to attend and what to learn. You try things and record what you caught. And you do it with other people on Fridays.

Self-directed learning is the third theory I taught last year. The evidence here is more encouraging, with a catch. A Mayo Clinic systematic review of 59 studies in health professions education found that self-directed learning gave a moderate improvement in knowledge compared with traditional teaching, though no clear effect on skills. It worked better when learners helped choose the resources, and advanced learners benefited more. You are advanced learners in medicine. In AI, most of you are beginners. A widely cited review argues that for beginners, discovery with little guidance works less well than guided practice, because a novice does not yet know what to look for. That claim is debated, but it fits what I have seen in this course. So that is the design: you direct your own learning, inside a framework, with a weekly routine and people to check in with.

This is the evidence behind a line from last year’s slides: use AI in support of your learning, not to replace it. A 2025 field experiment with nearly a thousand high school students practising maths. One group had a plain ChatGPT-style assistant; one had a tutor version designed to give hints rather than answers; one had nothing. With the plain assistant, practice grades went up 48 percent. Then, on the exam without AI, those students did 17 percent worse than students who never had it. They had used it as a crutch. The tutor version largely avoided that harm. On the right, the same worry in clinicians. Endoscopists at four Polish centres started using AI to detect polyps. When they did colonoscopies without the AI, their adenoma detection rate fell from 28 to 22 percent. That is one observational study, and there are other explanations, but it is exactly the risk: the skill you hand to the machine is the skill you stop practising. The lesson is not “avoid AI”. It is “use it in a way that makes you do the thinking”.

Putting it together, six habits, each tied to the research we just saw. One: tell the AI what you want to learn, why, and what you already know. You are setting the goal, not the machine, and it can pitch its help at your level. Two: try first, then ask. Do the thinking before the machine does. This is what the tutor version in the maths study enforced. Three: ask the AI to quiz you, not to explain again. Testing yourself beats rereading, and a chatbot is a tireless quiz-maker. Four: explain it back in your own words and ask what you got wrong. Five: come back to it later in the week. Spacing beats cramming. Six: write down what you caught, including the AI’s mistakes. Remember that your feeling of understanding is not a reliable gauge, so you need a record. \[Click\] This is what we call “Learn it with AI”: two to four hours a week using AI to teach yourself something you care about. It is the main hands-on work of the course, and the Thursday journal is where you record what you caught.

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