Scaling CS1 Support with Compiler-Integrated Conversational AI
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916346590134272 |
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| author | Renzella, Jake Vassar, Alexandra Solano, Lorenzo Lee Taylor, Andrew |
| author_facet | Renzella, Jake Vassar, Alexandra Solano, Lorenzo Lee Taylor, Andrew |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_02378 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scaling CS1 Support with Compiler-Integrated Conversational AI Renzella, Jake Vassar, Alexandra Solano, Lorenzo Lee Taylor, Andrew Computers and Society Artificial Intelligence Software Engineering 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. |
| title | Scaling CS1 Support with Compiler-Integrated Conversational AI |
| topic | Computers and Society Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2408.02378 |