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Enhancing Programming Error Messages in Real Time with Generative AI (2402.08072v1)

Published 12 Feb 2024 in cs.HC and cs.AI

Abstract: Generative AI is changing the way that many disciplines are taught, including computer science. Researchers have shown that generative AI tools are capable of solving programming problems, writing extensive blocks of code, and explaining complex code in simple terms. Particular promise has been shown in using generative AI to enhance programming error messages. Both students and instructors have complained for decades that these messages are often cryptic and difficult to understand. Yet recent work has shown that students make fewer repeated errors when enhanced via GPT-4. We extend this work by implementing feedback from ChatGPT for all programs submitted to our automated assessment tool, Athene, providing help for compiler, run-time, and logic errors. Our results indicate that adding generative AI to an automated assessment tool does not necessarily make it better and that design of the interface matters greatly to the usability of the feedback that GPT-4 provided.

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References (31)
  1. Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2023). Association for Computing Machinery, New York, NY, USA, 500–506. https://doi.org/10.1145/3545945.3569759
  2. Compiler Error Messages Considered Unhelpful: The Landscape of Text-Based Programming Error Message Research. In Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education (ITiCSE-WGR ’19). Association for Computing Machinery, New York, NY, USA, 177–210. https://doi.org/10.1145/3344429.3372508
  3. Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2023). ACM, NY, USA, 1136–1142. https://doi.org/10.1145/3545945.3569823
  4. Prompt Problems: A New Programming Exercise for the Generative AI Era. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2024). ACM, NY, USA, 7.
  5. Computing Education in the Era of Generative AI. arXiv:cs.CY/2306.02608
  6. On Designing Programming Error Messages for Novices: Readability and Its Constituent Factors. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 55, 15 pages. https://doi.org/10.1145/3411764.3445696
  7. The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming. In Australasian Computing Education Conference (ACE ’22). Association for Computing Machinery, New York, NY, USA, 10–19. https://doi.org/10.1145/3511861.3511863
  8. My AI Wants to Know If This Will Be on the Exam: Testing OpenAI’s Codex on CS2 Programming Exercises. In Proceedings of the 25th Australasian Computing Education Conference (ACE ’23). Association for Computing Machinery, New York, NY, USA, 97–104. https://doi.org/10.1145/3576123.3576134
  9. Exploring the Responses of Large Language Models to Beginner Programmers’ Help Requests. In Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 1 (ICER ’23). Association for Computing Machinery, New York, NY, USA, 93–105. https://doi.org/10.1145/3568813.3600139
  10. Studying the Effect of AI Code Generators on Supporting Novice Learners in Introductory Programming. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 455, 23 pages. https://doi.org/10.1145/3544548.3580919
  11. Sam Lau and Philip Guo. 2023. From ”Ban It Till We Understand It” to ”Resistance is Futile”: How University Programming Instructors Plan to Adapt as More Students Use AI Code Generation and Explanation Tools Such as ChatGPT and GitHub Copilot. In Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 1 (ICER ’23). Association for Computing Machinery, New York, NY, USA, 106–121. https://doi.org/10.1145/3568813.3600138
  12. Comparing Code Explanations Created by Students and Large Language Models. arXiv:cs.CY/2304.03938
  13. Using Large Language Models to Enhance Programming Error Messages. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2023). Association for Computing Machinery, New York, NY, USA, 563–569. https://doi.org/10.1145/3545945.3569770
  14. CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes. arXiv:cs.CY/2308.06921
  15. Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2023). Association for Computing Machinery, New York, NY, USA, 931–937. https://doi.org/10.1145/3545945.3569785
  16. Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances. arXiv preprint arXiv:2307.01142 (2023).
  17. No More Pencils No More Books: Capabilities of Generative AI on Irish and UK Computer Science School Leaving Examinations. In Proceedings of the 2023 Conference on United Kingdom & Ireland Computing Education Research (UKICER ’23). Association for Computing Machinery, New York, NY, USA, Article 2, 7 pages. https://doi.org/10.1145/3610969.3610982
  18. Do Enhanced Compiler Error Messages Help Students? Results Inconclusive.. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE ’17). Association for Computing Machinery, New York, NY, USA, 465–470. https://doi.org/10.1145/3017680.3017768
  19. The Robots Are Here: Navigating the Generative AI Revolution in Computing Education. In Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education (ITiCSE-WGR ’23). Association for Computing Machinery, New York, NY, USA, 108–159. https://doi.org/10.1145/3623762.3633499
  20. First Things First: Providing Metacognitive Scaffolding for Interpreting Problem Prompts. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE ’19). Association for Computing Machinery, New York, NY, USA, 531–537. https://doi.org/10.1145/3287324.3287374
  21. Metacognitive Difficulties Faced by Novice Programmers in Automated Assessment Tools. In Proceedings of the 2018 ACM Conference on International Computing Education Research (ICER ’18). Association for Computing Machinery, New York, NY, USA, 41–50. https://doi.org/10.1145/3230977.3230981
  22. On Novices’ Interaction with Compiler Error Messages: A Human Factors Approach. In Proceedings of the 2017 ACM Conference on International Computing Education Research (ICER ’17). Association for Computing Machinery, New York, NY, USA, 74–82. https://doi.org/10.1145/3105726.3106169
  23. “It’s Weird That It Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers. ACM Trans. Comput.-Hum. Interact. 31, 1, Article 4 (nov 2024), 31 pages. https://doi.org/10.1145/3617367
  24. Evaluating the Performance of Code Generation Models for Solving Parsons Problems With Small Prompt Variations. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (ITiCSE 2023). Association for Computing Machinery, New York, NY, USA, 299–305. https://doi.org/10.1145/3587102.3588805
  25. Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models. In Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1 (ICER ’22). Association for Computing Machinery, New York, NY, USA, 27–43. https://doi.org/10.1145/3501385.3543957
  26. Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses. The 19th ACM Conference on International Computing Education Research (ICER) (2023).
  27. dcc –help: Transforming the Role of the Compiler by Generating Context-Aware Error Explanations with Large Language Models. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2024). ACM, NY, USA, 7.
  28. Dwayne Towell and Brent Reeves. 2010. From Walls to Steps: Using online automatic homework checking tools to improve learning in introductory programming courses. ACET Journal of Computer Education and Research (2010).
  29. Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In CHI Conference on Human Factors in Computing Systems Extended Abstracts. Association for Computing Machinery, New York, NY, USA, 1–7.
  30. A Large Scale RCT on Effective Error Messages in CS1. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2024). ACM, NY, USA, 7.
  31. Generative AI in Computing Education: Perspectives of Students and Instructors. arXiv preprint arXiv:2308.04309 (2023).
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Authors (9)
  1. Bailey Kimmel (4 papers)
  2. Austin Geisert (1 paper)
  3. Lily Yaro (1 paper)
  4. Brendan Gipson (1 paper)
  5. Taylor Hotchkiss (1 paper)
  6. Sidney Osae-Asante (1 paper)
  7. Hunter Vaught (1 paper)
  8. Grant Wininger (1 paper)
  9. Chase Yamaguchi (1 paper)
Citations (6)
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