Using GPT-4 to Provide Tiered, Formative Code Feedback
Ha Nguyen, Vicki H. Allan
Abstract
Large language models (LLMs) have shown promise in generating sensible code explanation and feedback in programming exercises. In this experience report, we discuss the process of using one of these models (OpenAI's GPT-4) to generate individualized feedback for students' Java code and pseudocode. We instructed GPT-4 to generate feedback for 113 submissions to four programming problems in an Algorithms and Data Structures class. We prompted the model with example feedback (few-shot learning) and instruction to (1) give feedback on conceptual understanding, syntax, and time complexity, and (2) suggest follow-up actions based on students' code or provide guiding questions. Overall, GPT-4 provided accurate feedback and successfully built on students' ideas in most submissions. Human evaluators (computer science instructors and tutors) rated GPT-4's hints as useful in guiding students' next steps. Model performance varied with programming problems but not submission quality. We reflect on where the model performed well and fell short, and discuss the potential of integrating LLM-generated, individualized feedback into computer science instruction.