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Timely formative feedback is considered as one of the most important driversfor effective learning. Delivering timely and individualized feedback isparticularly challenging in large classes in higher education. Recently LargeLanguage Models such as GPT-3 became available to the public that showedpromising results on various tasks such as code generation and codeexplanation. This paper investigates the potential of AI in providingpersonalized code correction and generating feedback. Based on existing studentsubmissions of two different real-world assignments, the correctness of theAI-aided e-assessment as well as the characteristics such as faultlocalization, correctness of hints, and code style suggestions of the generatedfeedback are investigated. The results show that 73 % of the submissions werecorrectly 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, includinglocalization of errors that were not the actual errors, or even hallucinatederrors. Implications and potential new usage scenarios are discussed.

AI-enhanced Auto-correction of Programming Exercises: How Effective is GPT-3.5?