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AutoMCQ - Automatically Generate Code Comprehension Questions using GenAI
Open accessAbstract
Students often do not fully understand the code they have written. This sometimes does not become evident until later in their education, which can mean it is harder to fix their incorrect knowledge or misunderstandings. In addition, being able to fully understand code is increasingly important in a world where students have access to generative artificial intelligence (GenAI) tools, such as GitHub Copilot.
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References 4 · Cited by 3
Metadata
- Authors
- Goodfellow, M., Booth, R., Fagan, A., & Lambert, A.
- Affiliations
- University of Strathclyde (GB)
- Venue
- Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 2
- Publisher
- ACM
- Published
- 2025-06-13
- Pages
- 737-738
- OpenAlex
- W4411374921
- Cited by
- 3
- References
- 4
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 4.32
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- program comprehension, multiple-choice questions, automatic question generation, generative AI, programming education, GitHub Copilot, AI-assisted learning
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: AutoMCQ - Automatically Generate Code Comprehension Questions using GenAI. https://app.cochat.ai/papers/10.1145/3724389.3731266
- The paper
- Goodfellow, M., Booth, R., Fagan, A., & Lambert, A. (2025). AutoMCQ - Automatically Generate Code Comprehension Questions using GenAI. Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 2. https://doi.org/10.1145/3724389.3731266
