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Scaling CS1 Support with Compiler-Integrated Conversational AI

arXiv 2408.02378 ↗First posted 22 Jul 2024cs.CY cs.AI cs.SEcited by 0

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Abstract (arXiv)

This paper introduces DCC Sidekick, a web-based conversational AI tool that enhances an existing LLM-powered C/C++ compiler by generating educational programming error explanations. The tool seamlessly combines code display, compile- and run-time error messages, and stack frame read-outs alongside an AI interface, leveraging compiler error context for improved explanations. We analyse usage data from a large Australian CS1 course, where 959 students engaged in 11,222 DCC Sidekick sessions, resulting in 17,982 error explanations over seven weeks. Notably, over 50% of interactions occurred outside business hours, underscoring the tool's value as an always-available resource. Our findings reveal strong adoption of AI-assisted debugging tools, demonstrating their scalability in supporting extensive CS1 courses. We provide implementation insights and recommendations for educators seeking to incorporate AI tools with appropriate pedagogical safeguards.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 28 references that are in the map. First posted on arXiv 22 Jul 2024.

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5 of 28 references.

References · 5

  1. 2023Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programmingcited by 87
  2. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
  3. 2023CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classescited by 71
  4. 2023Exploring the Responses of Large Language Models to Beginner Programmers' Help Requestscited by 44
  5. 2023Large Language Models in Introductory Programming Education: ChatGPT's Performance and Implications for Assessmentscited by 10

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