Summary of Feedback-generation For Programming Exercises with Gpt-4, by Imen Azaiz et al.
Feedback-Generation for Programming Exercises With GPT-4
by Imen Azaiz, Natalie Kiesler, Sven Strickroth
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the quality of GPT-4 Turbo’s generated output for prompts containing both programming task specifications and student submissions. It investigates the model’s ability to provide feedback on 55 authentic student submissions from an introductory programming course, focusing on correctness, personalization, fault localization, and other features. Compared to prior work with GPT-3.5, GPT-4 Turbo shows notable improvements in output structure, consistency, and accuracy. However, some inconsistent feedback was noted. The study contributes to our understanding of LLMs’ potential, limitations, and integration into e-assessment systems, pedagogical scenarios, and instructional applications based on GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well a language model called GPT-4 Turbo can give helpful hints to students doing programming assignments. They asked the model to review 55 actual student submissions from an intro programming course and found that it did better than earlier models like GPT-3.5 in some ways. The model’s feedback was often correct, personalized, and good at finding mistakes. However, sometimes it gave mixed messages or made errors too. This research helps us understand what language models can do to help with online learning and teaching. |
Keywords
» Artificial intelligence » Gpt » Language model » Online learning
