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

• URL: http://arxiv.org/abs/2508.05979v1
• Date: Fri, 08 Aug 2025 03:25:19 GMT
• Title: Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education
• Authors: Xinming Yang, Haasil Pujara, Jun Li,
• Abstract summary: Large Language Models (LLMs) are often used as virtual tutors in computer science (CS) education.<n>This paper presents a novel pedagogical paradigm that inverts this model: students act as instructors who must teach an LLM to solve problems.
• Score: 4.088336228217055
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• PATS: Personality-Aware Teaching Strategies with Large Language Model Tutors [66.56586559631516]
  Large language models (LLMs) have potential as educational tutors.<n>But different tutoring strategies benefit different student personalities.<n>Despite this, current LLM tutoring systems do not take into account student personality traits.
  arXiv  Detail & Related papers  (2026-01-13T10:17:26Z)
• Partnering with AI: A Pedagogical Feedback System for LLM Integration into Programming Education [19.441958600393342]
  This paper introduces a novel framework for large language models (LLMs)-driven feedback generation.<n>Our findings suggest that teachers consider that, when aligned with the framework, LLMs can effectively support students.<n>However, we found several limitations, such as its inability to adapt feedback to dynamic classroom contexts.
  arXiv  Detail & Related papers  (2025-07-01T03:48:48Z)
• Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education [1.1517315048749441]
  Large language model (LLM) applications have sparked both excitement and concern.<n>Recent studies consistently highlight students' (mis)use of LLMs can hinder learning outcomes.<n>This work aims to teach students how to effectively prompt LLMs to improve their learning.
  arXiv  Detail & Related papers  (2025-06-23T20:39:17Z)
• From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning [76.09281171131941]
  Large language models (LLMs) can transform education, but their optimization for direct question-answering often undermines effective pedagogy.<n>We propose an online reinforcement learning (RL)-based alignment framework that can quickly adapt LLMs into effective tutors.
  arXiv  Detail & Related papers  (2025-05-21T15:00:07Z)
• Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure [36.83786872708736]
  One-to-one tutoring is one of the most efficient methods of teaching.<n>We develop StratL, an algorithm to optimize LLM prompts and steer it to follow a predefined multi-turn tutoring plan represented as a transition graph.<n>As a case study, we create a prototype tutor for high school math following Productive Failure (PF), an advanced and effective learning design.
  arXiv  Detail & Related papers  (2024-10-03T16:15:41Z)
• Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs [49.18567856499736]
  We investigate whether large language models (LLMs) can be supportive of open-ended dialogue tutoring.<n>We apply a range of knowledge tracing (KT) methods on the resulting labeled data to track student knowledge levels over an entire dialogue.<n>We conduct experiments on two tutoring dialogue datasets, and show that a novel yet simple LLM-based method, LLMKT, significantly outperforms existing KT methods in predicting student response correctness in dialogues.
  arXiv  Detail & Related papers  (2024-09-24T22:31:39Z)
• Evaluating and Optimizing Educational Content with Large Language Model Judgments [52.33701672559594]
  We use Language Models (LMs) as educational experts to assess the impact of various instructions on learning outcomes. We introduce an instruction optimization approach in which one LM generates instructional materials using the judgments of another LM as a reward function. Human teachers' evaluations of these LM-generated worksheets show a significant alignment between the LM judgments and human teacher preferences.
  arXiv  Detail & Related papers  (2024-03-05T09:09:15Z)
• AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails [43.19453208130667]
  Large Language Models (LLMs) have found several use cases in education, ranging from automatic question generation to essay evaluation. In this paper, we explore the potential of using Large Language Models (LLMs) to author Intelligent Tutoring Systems. We create a sample end-to-end tutoring system named MWPTutor, which uses LLMs to fill in the state space of a pre-defined finite state transducer.
  arXiv  Detail & Related papers  (2024-02-14T14:53:56Z)
• YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
  This paper introduces YODA, a teacher-student progressive learning framework. It emulates the teacher-student education process to improve the efficacy of model fine-tuning. Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
  arXiv  Detail & Related papers  (2024-01-28T14:32:15Z)
• Democratizing Reasoning Ability: Tailored Learning from Large Language Model [97.4921006089966]
  We propose a tailored learning approach to distill such reasoning ability to smaller LMs. We exploit the potential of LLM as a reasoning teacher by building an interactive multi-round learning paradigm. To exploit the reasoning potential of the smaller LM, we propose self-reflection learning to motivate the student to learn from self-made mistakes.
  arXiv  Detail & Related papers  (2023-10-20T07:50:10Z)
• RLTutor: Reinforcement Learning Based Adaptive Tutoring System by Modeling Virtual Student with Fewer Interactions [10.34673089426247]
  We propose a framework for optimizing teaching strategies by constructing a virtual model of the student. Our results can serve as a buffer between theoretical instructional optimization and practical applications in e-learning systems.
  arXiv  Detail & Related papers  (2021-07-31T15:42:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.