Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education

Xinming Yang, Haasil Pujara, Jun Li

Keywords: Large Language Model, Computer Science Education, Human-AI Collaboration, Role Reversal

TL;DR: Students learn computer science better by teaching large language models, reversing the usual teacher-student roles.

Abstract: While Large Language Models (LLMs) are often used as virtual tutors in computer science (CS) education, this approach can foster passive learning and over-reliance. This paper presents a novel pedagogical paradigm that inverts this model: students act as instructors who must teach an LLM to solve problems. To facilitate this, we developed strategies for designing questions with engineered knowledge gaps that only a student can bridge, and we introduce Socrates, a system for deploying this method with minimal overhead. We evaluated our approach in an undergraduate course and found that this active-learning method led to statistically significant improvements in student performance compared to historical cohorts. Our work demonstrates a practical, cost-effective framework for using LLMs to deepen student engagement and mastery.

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Submission Number: 1206