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.
Cite
@article{arxiv.2408.02378,
title = {Scaling CS1 Support with Compiler-Integrated Conversational AI},
author = {Jake Renzella and Alexandra Vassar and Lorenzo Lee Solano and Andrew Taylor},
journal= {arXiv preprint arXiv:2408.02378},
year = {2024}
}
Comments
Papers, funding sources, and Github Repositories at: https://dcc.cse.unsw.edu.au/