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The rapid evolution of artificial intelligence (AI), specifically largelanguage models (LLMs), has opened opportunities for various educationalapplications. This paper explored the feasibility of utilizing ChatGPT, one ofthe most popular LLMs, for automating feedback for Java programming assignmentsin an introductory computer science (CS1) class. Specifically, this studyfocused on three questions: 1) To what extent do students view LLM-generatedfeedback as formative? 2) How do students see the comparative affordances offeedback prompts that include their code, vs. those that exclude it? 3) Whatenhancements do students suggest for improving AI-generated feedback? Toaddress these questions, we generated automated feedback using the ChatGPT APIfor four lab assignments in the CS1 class. The survey results revealed thatstudents perceived the feedback as aligning well with formative feedbackguidelines established by Shute. Additionally, students showed a clearpreference for feedback generated by including the students' code as part ofthe LLM prompt, and our thematic study indicated that the preference was mainlyattributed to the specificity, clarity, and corrective nature of the feedback.Moreover, this study found that students generally expected specific andcorrective feedback with sufficient code examples, but had diverged opinions onthe tone of the feedback. This study demonstrated that ChatGPT could generateJava programming assignment feedback that students perceived as formative. Italso offered insights into the specific improvements that would make theChatGPT-generated feedback useful for students.

Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming