🤖 AI Summary
In LLM-augmented education, students often exhibit passive learning behaviors and overreliance on large language models, hindering deep conceptual understanding and metacognitive development. Method: This paper proposes the “Student-as-Teacher” paradigm, wherein students actively design programming problems embedded with *knowledge breakpoints*—intentional gaps in prerequisite knowledge—and guide LLMs to solve them iteratively. We implement Socrates, a lightweight interactive system supporting breakpoint annotation, progressive prompting, and feedback-driven refinement loops. Contribution/Results: By reversing the LLM’s role—from tutor to tutee—the paradigm transforms problem authoring into a high-order learning activity that fosters active engagement and self-regulated learning. A controlled study in undergraduate computer science courses demonstrated statistically significant improvements: +12.3% in final exam scores and enhanced conceptual mastery, validating its efficacy in promoting active learning, reducing tool dependency, and strengthening metacognitive awareness.
📝 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.