The learning stays with you

1. Why not a chatbot tutor

A chat window that can explain anything is a good product to demo. Ask it about a past-paper question and it writes a clean, correct answer. The student reads it, nods, and moves on. Reading a good answer feels a lot like knowing how to write one, and that feeling is the problem.

Learning research has said for decades that memory is built by effortful retrieval, not by reading. Testing yourself beats re-reading even when re-reading feels more productive (Roediger & Karpicke, 2006). Material you produce yourself is remembered better than material you're given (the generation effect: Slamecka & Graf, 1978). Difficulties that slow you down during practice, like recalling instead of looking up or spacing sessions out instead of cramming, make learning stick for longer (Bjork & Bjork's desirable difficulties). A large review of study techniques rated only two as highly useful: practice testing and spreading practice out over time (Dunlosky et al., 2013).

A chatbot tutor removes those difficulties. That is what makes it pleasant, and why it can backfire. Handing mental work to a tool is called cognitive offloading (Risko & Gilbert, 2016). It's often sensible: nobody needs to memorise a phone number. But in studying, the effort you offload is the thing you came to do.

2. What the recent studies found

The evidence on generative AI and learning is new and still thin, but the early results point the same way.

High-school maths, about 1,000 students (Bastani et al., PNAS, 2025). Students practised with either a plain ChatGPT-style assistant (GPT Base), an assistant with guardrails that gave hints instead of answers (GPT Tutor), or no AI. During practice, both AI groups did far better than the control group. Then the AI was taken away for the exam. The GPT Base group scored 17% lower than students who never had it. The authors put it down to students using the assistant as a crutch. The guardrails mostly removed that harm: the GPT Tutor group did about as well as the control.

Essay writing with EEG, 54 adults (Kosmyna et al., MIT Media Lab, 2025). People wrote essays with ChatGPT, with a search engine, or with nothing. The brain-only group showed the strongest, most widespread brain connectivity and the ChatGPT group the weakest. ChatGPT users struggled to quote essays they had written minutes earlier and felt little ownership of them. In a fourth session, people who had been using ChatGPT wrote without it and still showed lower engagement. The paper's title gave the idea its name: accumulation of cognitive debt. It's a preprint with a small sample, and some critics say the term goes further than the EEG can show. But it fits the other findings.

University students, 117 in a randomised trial (Fan et al., British Journal of Educational Technology, 2025). Students revising an essay with ChatGPT improved their essays more than the other groups. Their knowledge gain and transfer were no better. The authors call the pattern metacognitive laziness: leaning on the tool instead of planning, monitoring and checking your own work.

Knowledge workers, 319 surveyed (Lee et al., CHI 2025, Microsoft Research and Carnegie Mellon). The more people trusted the AI, the less critical thinking they reported doing. The more they trusted themselves, the more they did. This is self-reported, so it measures what people noticed, not what they could do.

The pattern across all four: an AI that does the task makes the output better and the person no better, or worse, once it's gone. An exam is the moment it's gone.

3. Where Simeon draws the line

So the question for us wasn't "how much can the AI teach?" It was: which parts of studying cost a student time without teaching them anything? Those parts go to the AI. The rest stays with the student.

Setting up good studying is real work, and it's what stops most people starting. A student who could spend six hours building a revision plan from a messy Canvas course usually doesn't, and crams instead. Taking that work away doesn't take away any learning; it gets them to the learning sooner.

4. How the app keeps the thinking with the student

The line is in the product, not just in this page. Where it shows:

• Test mode hides the answer. On a past-paper question, the model answer and marking points stay hidden while a timer runs at the exam's pace. Revealing them takes two presses: I have finished — reveal answer, then a confirmation that says there's no un-seeing them, and that a self-mark on an unfinished answer means nothing.
• The student marks themselves. The marking points are a checklist they tick against what they actually wrote. When they ask Simeon to mark a photo of their handwriting (or a typed answer), Simeon proposes the ticks, and the student checks them before saving the score. The judgement is theirs.
• "Teach me how to do it" gives hints, never the answer. The assistant sees the model answer so its hints are right, but is told not to give the result, a number or a step from it, even partly, even when asked. It teaches the idea on a different worked example, then asks the student to try the first step, checks what they tried, and gives the next hint. This is the GPT Tutor design from the study above.
• Quizzing waits for an attempt. When Simeon is asked to quiz, it picks a question and says what to watch for, without the answer. In Claude or ChatGPT (Simeon's MCP server), the rule is the same: ask, wait for the student's answer, only then reveal and check it point by point.
• Ask Simeon answers questions, grounded in the course. Highlight anything and ask about it. That one is explanation on demand, and we don't pretend otherwise. It answers in the course's own notation, points back into the plan (a day, a topic, a question to practise), and won't write answers for graded work that's still open.
• Numbers show where they came from. Tap a figure in a model answer and Simeon shows how it was calculated and which numbers in the question it used, so a student can follow the working instead of taking it on trust.

5. What we don't claim

• The studies are early. Small samples, short time spans, one preprint. We read them as a warning about a direction, not a precise measurement.
• Simeon still uses AI with students, not only for them. Ask Simeon explains, and marking a photo is the AI's first opinion. Both are things a good study partner does, and both keep the student's own attempt first.
• The plan is a start, not a script. It's built from the course, checked by code, and the student can change it. It decides what to practise and when; it can't make anyone practise.
• Nothing here stops a student from offloading. They can reveal an answer without trying. Simeon makes the effortful path the default and the easy one, but the choice stays theirs.

Reusing it in other software: a recipe

If you're building a learning product with AI in it:

1. List what the learner does, and sort it. For each step ask: does doing it build the skill being tested? If not (finding material, formatting, planning, checking arithmetic), automate it hard. If yes, keep it with the learner.
2. Make the learner's attempt come first. Hide answers by default, and make revealing them a deliberate act.
3. Hints, not answers. Give the model the answer so its hints are right, and tell it plainly never to reveal it. Teach on a different example; check the learner's step; give the next hint.
4. Let the learner judge. Marking against a scheme is itself practice. If the AI marks, it proposes and the learner confirms.
5. Check with code what code can check. Learners trust what they're shown. Recompute numbers outside the model.
6. Measure without the AI. If your product's success metric is how well people do with it, you'll build a crutch. Measure how they do when it's gone.

Sources

• Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. PNAS, 122(26). doi:10.1073/pnas.2422633122
• Kosmyna, N., et al. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv:2506.08872 (preprint).
• Fan, Y., et al. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology. doi:10.1111/bjet.13544
• Lee, H.-P., Sarkar, A., et al. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. CHI '25. doi:10.1145/3706598.3713778
• Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255.
• Slamecka, N. J., & Graf, P. (1978). The generation effect: Delineation of a phenomenon. Journal of Experimental Psychology: Human Learning and Memory, 4(6), 592–604.
• Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In Psychology and the Real World.
• Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4–58.
• Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688.

Where to look in the code