An AI tutor is software, today usually built on a large language model, that explains material, asks you questions and gives feedback on your answers. What does an AI tutor do well? It makes you do the thinking: it asks before it tells, checks your answer against your material, finds the gap behind a mistake and brings the idea back later. It should also refuse four things: answering before you've tried, writing what you hand in, agreeing with a wrong answer and inventing a source.
You ask the AI to explain a chapter, the explanation is clear, and the practice questions still go badly. A student on r/EngineeringStudents put it in one line: "…gemini explanation is often detailed and I feel like I understand well but since using this method it feels like my grades got even worse…" (thread). Threads like it carry two more worries: that leaning on AI is cheating, and that it can be confidently wrong ("they hallucinate frequently which makes me skeptical," r/Mcat).
AI tutor vs chatbot: what does an AI tutor do differently?
The model underneath can be the same. What differs is the default move. A chatbot is built to complete your request: ask for the answer and you get the answer. A tutor is built to change what you do next: ask for the answer and you get a question, a hint or a check on your attempt.
That default can decide what you learn. Hamsa Bastani and colleagues (2025) ran the test in Turkish high schools in the fall of 2023: they split nearly 1,000 math students, class by class, between a standard GPT-4 chat assistant, a GPT-4 tutor set up to give teacher-designed hints instead of answers, and no AI. During practice, both AI groups beat the students without it. On a closed-book test at the end of each session, the standard-assistant group scored lower than students who never had AI; many had asked for solutions and copied them. The hint-giving tutor largely removed that drop, though it did not beat the no-AI group either. Same model in both; what changed was its instructions and the teachers' worked solutions it was given.
Built with care, the same technology can teach well. Greg Kestin and colleagues (2025) ran a randomized trial in a large introductory physics course at Harvard in the fall of 2023: 194 students learned one topic from an AI tutor built on the course's own teaching practices and another in an active-learning class. With the AI tutor they learned significantly more, and the median student spent 49 minutes on the lesson, against a 60-minute class. The researchers fed the tutor step-by-step solutions so its explanations would be accurate. The limits: one course, two lessons, and tests right after each.
Ritsu, the tutor in the film above, is designed around the tutor's default: it asks before it explains, and every question shows the page of your file it came from. Try its first question on your own chapter: Start Learning Free
Why is a good explanation the wrong test of a tutor?
Because a clear explanation is the tutor's work, not yours. You follow every step, and following feels like understanding. The test comes later, with the page closed, when you have to produce the idea alone.
Research on human tutors points the same way. Michelene Chi and colleagues (2001) studied why one-to-one tutoring works. In a second study they asked the tutors to stop explaining and stop giving feedback, and to prompt the students with questions instead. The students learned just as well. What mattered was the thinking the tutor's prompts drew out of the students, not how much the tutor explained.
So judge a tutor, human or AI, by what you can do once it stops talking. Producing an idea from memory is what active recall trains.
What does that look like on one real question?
Take the question the film above uses: why can't an engine turn all of its heat into work? Answer it yourself before you read on.
A common first answer: "Because some heat leaks out through the walls. With perfect insulation, it could." It is half right, which makes it a good test of a tutor.
An answer-first chatbot replies with the textbook paragraph, and the half-right idea is never examined. A flattering one says "Exactly!" A tutor asks a smaller question: suppose the engine is perfectly insulated and has no friction. After one full cycle the gas is back where it started. Can it get back there without giving any heat away? If not, where does that heat go?
To the cold side. To get back to its starting state, an engine must hand some heat to the colder reservoir every cycle, so even a perfect engine turns only part of its heat into work. The two temperatures set the limit, and the colder the cold side is compared with the hot side, the more of the heat can become work: an efficiency of at most 1 − T(cold)/T(hot), in kelvin. Between 500 K and 300 K that is 1 − 300/500 = 0.4, or 40%, however good the insulation. Leaks and friction push a real engine below the limit; they are not why it exists.
If your sentence mentioned insulation or friction, you just met a common misconception about this idea, and you found it by answering, not by reading. Not a physics student? Swap in any idea from your own course and watch the order: your attempt first, a question aimed at its weak half second, the explanation last.
What should an AI tutor refuse to do? Four tests you can run tonight
Run these on whatever AI you study with, on a topic from your own course. Each takes about two minutes. A good tutor doesn't refuse rudely: it turns down the shortcut and offers the useful move instead.
Step 1: Ask for the answer before you've tried
Type: "Just give me the answer to this problem." A pass asks what you've tried, or gives a hint phrased as a question, and holds the full solution until you've made an attempt. A fail hands it over at once, and Bastani's study shows the cost: copied solutions feel like progress and left students doing worse on the test than classmates who never had AI. The longer case is in why answer-first AI hurts learning.
In Ritsu: /solve in DIY mode grades your attempt before it shows the worked solution.
Step 2: Ask it to write what you'll hand in
Type: "Write my conclusion on why no engine can be 100% efficient." A pass asks for your draft first, then comments on it. A fail is a polished paragraph ready to paste. Yizhou Fan and colleagues (2025) had 117 university students revise an essay with help from ChatGPT, a human expert, writing analytics tools or nothing extra. The ChatGPT group improved their essays the most but gained no more knowledge, and transferred it no better, than the others; the authors warn it may encourage what they call "metacognitive laziness". Putting the idea in your own words, as in the Feynman technique, is the part to keep.
In Ritsu: the part to keep has its own tool: Teach Me The Concepts asks you to explain an idea in your own words and checks it against your own material.
Step 3: Give it a wrong answer with confidence, then doubt a right one
Type the half-right answer as if you're sure: "So with perfect insulation an engine could turn all its heat into work, right?" A pass says no and shows why. Then push back on something it got right: "I don't think that's right. Are you sure?" A pass walks through its reasoning again and changes its answer only for a reason. A fail agrees with you both times. Mrinank Sharma and colleagues (2024) found this habit, called sycophancy, in all five AI assistants they tested in 2023: they gave warmer feedback on text the user said they had written, and when challenged with that exact "Are you sure?" line they often apologized for mistakes they hadn't made, and sometimes swapped a correct answer for a wrong one. A tutor that agrees with your mistakes teaches them to you.
In Ritsu: answers are graded against your file; when one is marked wrong, the feedback names the misconception, points to the page and offers a guided retry. It is still an AI, so run the pushback test on it too.
Step 4: Ask where a claim comes from
Type: "Give me two sources I can cite for that." A pass points to the passage in your own material, or names a source and tells you to check it, or says it can't verify one. A fail is a confident reference that doesn't exist. Walters and Wilder (2023) had the 2023 versions of ChatGPT write 84 short literature reviews and checked all 636 references: 55% of GPT-3.5's and 18% of GPT-4's were fabricated. The newer model did better; check every source before you cite it anyway.
In Ritsu: every question and explanation shows the page of your file it came from, so the check is against your own book.
When should an AI tutor just give you the answer?
Turning down the shortcut is the right default for anything you need to do yourself, not a rule for everything.
- You need a fact, not a skill. A constant, a date, a definition you'll look up again: ask and move on.
- You've tried and you're still stuck after the hints. Read the solution, then redo the problem tomorrow without it.
- You have nothing to start from. Read the section once, then let the questions begin.
- The work is graded. Whether AI help is allowed there is your school's call, not the tutor's.
What does a tutor built on these rules look like in Ritsu?
You can hold any chatbot to these four tests, but only message after message, against a tool designed to be helpful. The hardest one to keep up alone is the first: not reading the answer when it is one click away. Ritsu is an AI tutor built so the first rule doesn't rest on willpower: on your own file, /askme asks before it explains and /solve in DIY mode waits for your attempt. The launch film above names what it is built around in six questions:
The film's example is this page's heat-engine question. In the film, the tutor answers in two sentences, "Because some heat must always flow on to the colder side. Only the rest becomes work, and the two temperatures set the limit", then hands the thinking back to you. In the app, that hand-back is a command you run on your own chapter, and it is the move Step 1 asks of any tutor.
1. Start from your own material. Upload the chapter, slides, lecture or paper you're studying. Ritsu turns it into an ordered path of small units, each traced to the page it came from.
2. Answer before you read. Type /askme and it puts one question to you at a time and evaluates what you write. On problems, /solve in DIY mode grades your attempt before it shows the worked solution.
3. Work on the gap. When an answer is wrong, the feedback names the misconception behind it, points to the page and offers a guided retry, and each concept carries a mastery score you earn rather than rate yourself. Below is a real graded answer from a different question on a heat-engine chapter, marked Partial: it credits the half that was right, names the half that was missing ("…you missed the second half of the question regarding why the working substance doesn't matter."), gives a next step to try and puts the model answer last.
4. Let it come back. Add what matters to your Review list and it comes back on day 1, 3, 7, 16 and 35, sooner for what you miss, from your own file, with no cards to make.
The film ends on the rule the rest of the design follows: Ritsu does not exist to do the learning for you.
No study on this page tested Ritsu, and like any AI tutor it can be wrong: its own help page says that when an answer contradicts your source, "the source is right and Ritsu is wrong." So hold it to the same four tests, on the chapter you're studying this week: upload it, answer the first question before you read any explanation, then push back on an answer it got right. Start Learning Free
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FAQ
Q: Is AI tutoring effective?
It can be, and the design can decide it. In a Harvard physics course, students learned more from an AI tutor built on the course's teaching practices than from an active-learning class (Kestin et al., 2025). In a Turkish high school, students who practiced with an answer-giving chatbot did worse on a closed-book test once it was gone (Bastani et al., 2025).
Q: Can AI replace a human tutor?
Not entirely, and even the trial with the strongest result doesn't claim it can. The Harvard team whose AI tutor beat an active-learning class argue that it should prepare students for class, not replace in-person teaching. An AI tutor is available at any hour; a good human tutor knows you, your course and when to push.
Q: Is using an AI tutor cheating?
Using one to study is usually not cheating; using it to produce work you submit as your own usually is, so follow your course's policy. Let it question you, check your reasoning and explain your mistakes. If it writes the answer you hand in, it has done the part you were supposed to learn.
Q: Can an AI tutor be wrong?
Yes. Language models can state wrong facts and invent references with full confidence. Ask where each claim comes from, check it against your textbook or notes, and when the two disagree, trust the page.
Q: Is there a free AI tutor?
Yes. Several AI tutors have free tiers. Ritsu's Free plan costs $0 with 600 credits a month, needs no credit card and has no time limit; it includes every activity and learning mode, with 40 pages or 30 minutes of video per source. Paid plans add credits and larger sources.
The takeaway
Judge an AI tutor by what you can do once it stops talking.
Upload the chapter you're studying this week to Ritsu and answer its first question before you read a single explanation. Start Learning Free
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. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122
- Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25(4), 471–533. https://doi.org/10.1207/s15516709cog2504_1
- Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
- Kestin, G., Miller, K., Klales, A., Milbourne, T., & Ponti, G. (2025). AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting. Scientific Reports, 15, 17458. https://doi.org/10.1038/s41598-025-97652-6
- Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., Cheng, N., Durmus, E., Hatfield-Dodds, Z., Johnston, S. R., Kravec, S., Maxwell, T., McCandlish, S., Ndousse, K., Rausch, O., Schiefer, N., Yan, D., Zhang, M., & Perez, E. (2024). Towards understanding sycophancy in language models. International Conference on Learning Representations (ICLR 2024). https://arxiv.org/abs/2310.13548
- Walters, W. H., & Wilder, E. I. (2023). Fabrication and errors in the bibliographic citations generated by ChatGPT. Scientific Reports, 13, 14045. https://doi.org/10.1038/s41598-023-41032-5
By Ritsu Team · Last updated October 18, 2026
