Simulating Students' Java Programming Errors with Large Language Models
Understanding student errors in the programming is a cornerstone of programming education, yet obtaining a representative set of student errors for any newly designed task remains slow and costly, since authentic submissions only accumulate after extensive classroom deployment. This paper explores whether large language models (LLMs) can serve as scalable proxies for students by simulating realistic logical errors in code submissions. Using the CodeWorkout dataset of 74,000+ unique student Java submissions across 37 problems, we evaluate five LLMs under three mainstream prompting strategies: Input-Output (IO), Chain-of-Thought (CoT), and iterative Self-Refine. We assess performance along ...
| Authors | Ali Keramati, Jie Cao, Iman Mohammadi et al. |
|---|---|
| Primary category | cs.SE |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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