Timely formative feedback is considered as one of the most important drivers
for effective learning. Delivering timely and individualized feedback is
particularly challenging in large classes in higher education. Recently Large
Language Models such as GPT-3 became available to the public that showed
promising results on various tasks such as code generation and code
explanation. This paper investigates the potential of AI in providing
personalized code correction and generating feedback. Based on existing student
submissions of two different real-world assignments, the correctness of the
AI-aided e-assessment as well as the characteristics such as fault
localization, correctness of hints, and code style suggestions of the generated
feedback are investigated. The results show that 73 % of the submissions were
correctly identified as either correct or incorrect. In 59 % of these cases,
GPT-3.5 also successfully generated effective and high-quality feedback.
Additionally, GPT-3.5 exhibited weaknesses in its evaluation, including
localization of errors that were not the actual errors, or even hallucinated
errors. Implications and potential new usage scenarios are discussed.

本文探讨了人工智能在提供个性化代码改正和生成反馈方面的潜力，根据两个真实作业的学生提交进行了调查，结果显示 73% 的提交正确识别，并且在这些情况中，GPT-3.5 还成功生成了有效且高质量的反馈。