A Tale of Two Tutors
The difference between getting the answer and learning to think.
You are halfway through the assignment when you get stuck. Your textbook explains the theory, but you cannot see the next step, and you message your buddy, who sends you a photo of his work. “Good luck, mate!” You read each line. It makes sense. “Thank you, mate!” You move on to the next question, only to see that the previous method doesn’t apply here.
Not wanting to ask your friend twice, you ask the chatbot; the bot is always awake and eager to please you. You paste in the question and get the whole solution, with steps and rationale. Aha! The method is clear. The evening begins to look productive again, until you show up to the term exam and realize that you don’t really know the topic and you are not ready to solve a new problem.
Following the work when somebody else has done it for you is easy and can give you false confidence, but starting from a blank is a whole different business. Copying the solution from your friend or the chatbot gives you the answer without having to do the work yourself.
This can happen without you noticing. Research by Bastani et al. (2025) found that students using a standard AI chatbot were more inclined to ask for the answer outright, whereas those using a guardrailed AI tutor were more likely to ask for help or attempt to answer questions independently.
Later, back at your desk, you are studying for your final exam. The clock moves on, and you are beginning to dislike both the exam and the clock. If only you could get an hour with one of those big-name tutors in your country. The tutors everybody recommends, who know your syllabus front to back. They can take one glance at your work and see exactly where your trouble began, and they know all the exam hacks. You would point to what is confusing, and they would guide you step by step, watching you try and assessing whether their explanation had stuck.
In reality, getting an hour with an exceptional tutor is hard. Exceptional tutors are rare, private lessons are expensive, and their schedules are full. There are spots in large tuition classes, but then you will have to compete with other students to get attention. You want to go back two steps while the student in the front row wants to go forward?
Theory of Pi exists so that good help reaches all students.
A chatbot does not become a teacher simply by explaining an answer. Good tutoring needs to make room for students’ thinking.
A useful test of tutoring is what you can do once the help is taken away. Bastani et al. (2025) found that students using a standard AI chatbot performed better during practice but worse on an unassisted test than students who had practised without AI, and did not perceive that they had performed worse or learned less.
In a separate study of 194 Harvard physics students, Kestin et al. (2025) found that a custom AI tutor designed around teaching principles led to higher immediate post-lesson test scores and greater reported engagement than classroom instruction using active learning.
The difference, then, is not simply whether AI is used, but how it is used. A generic chatbot is designed to answer your question; a tutor is designed to help you learn how to answer it yourself. These studies did not test Theory of Pi, but they show why the way an AI tutor teaches matters.
Theory of Pi, an education platform, integrates courses based on standardized curricula, trusted lessons, worked examples, practice problems, interactive simulations, and teaching knowledge about common mistakes and exam questions. Theory of Pi follows evidence-based learning principles such as retrieval practice, spaced practice, and interleaving (Dunlosky et al., 2013). Retrieval practice, in particular, can produce better long-term retention than simply restudying material (Roediger & Karpicke, 2006).
It also includes Pi, your virtual learning companion, built around the same kind of preparation and guidance that make an experienced tutor useful. Pi analyzes your work, guides you to recognize missing knowledge or misconceptions, and teaches you how to avoid those pitfalls. Theory of Pi also has a Question Studio feature where students make their own problems, yes, just like an examiner, and are graded based on the creativity of the problem. Problem-posing requires students to think about how concepts fit together and has been associated with positive cognitive learning outcomes (Zhang et al., 2024).
Theory of Pi also offers revision for every module, with explicit guidance on exam strategies for different classes of problems, and its built-in algorithms give you an estimate of your exam readiness based on your study engagement and problem-solving skills.
Theory of Pi is designed to go beyond a problem bank with AI guidance. Key differentiators are the range of content and learning techniques built natively into the platform and how Pi helps you recognize the strengths in your submitted work and identify missing concepts, rather than simply giving you a grade—the same qualities that distinguish the best tutors in your country.
Parents, that means you can depend on Theory of Pi not simply to give out the answers to questions, but to improve your child’s comprehension of the subject. Every subject at Theory of Pi is carefully crafted with hundreds of hours of human work, then revised and presented through AI-aided design.
Soon, Theory of Pi will release Pi Flow, a virtual voice tutor that interacts with the student much like an online human tutor, with animated slides and a two-way whiteboard. Flow can revise a subject with you and, just like a great tutor, help you prepare for your exam. This is for students who do better with live audio guidance.
Theory of Pi’s product walkthrough shows how the lessons, tutoring, and practice fit together. Theory of Pi’s vision is to make that individual support available wherever you study and whenever you need help. Meet Pi.
Join the future of AI-assisted learning and start for free at theoryofpi.com.
Works Cited
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. Article.
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. Article.
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. Article.
Roediger, H. L., III, & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. Article.
Zhang, C., Zhou, Y., Wijaya, T. T., Chen, J., & Ning, Y. (2024). Effects of a problem posing instructional interventions on student learning outcomes: A three-level meta-analysis. Thinking Skills and Creativity, 53, 101587. Article.