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LLMs Simulate Student Java Errors, Claude Sonnet 4 Shows Balanced Performance

A new research paper explores the use of large language models (LLMs) to simulate student programming errors in Java. The study evaluated five LLMs using different prompting strategies on the CodeWorkout dataset, which contains over 74,000 student submissions. Results indicate that while LLMs can generate diverse errors, Claude Sonnet 4 showed the most balanced performance in aligning with authentic student mistakes. Expert annotations confirmed that the synthetic errors were functionally indistinguishable from real student errors. AI

IMPACT LLMs can be used to generate realistic programming errors, aiding in the development of educational tools like intelligent tutoring systems.

RANK_REASON The cluster contains a research paper detailing an academic study on LLM capabilities.

Read on arXiv cs.CL →

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LLMs Simulate Student Java Errors, Claude Sonnet 4 Shows Balanced Performance

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Ali Keramati, Jie Cao, Iman Mohammadi, Mark Warschauer, Yang Shi ·

    Simulating Students' Java Programming Errors with Large Language Models

    arXiv:2606.14113v1 Announce Type: cross Abstract: 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 …

  2. arXiv cs.CL TIER_1 English(EN) · Yang Shi ·

    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. T…