From code to questions : leveraging generative AI to support code comprehension in introductory programming
Goodfellow, Martin and Lambert, Alasdair and Booth, Robbie and Fagan, Andrew (2026) From code to questions : leveraging generative AI to support code comprehension in introductory programming. Human-Centric Intelligent Systems. ISSN 2667-1336 (https://doi.org/10.1007/s44230-026-00150-9)
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Abstract
Students frequently exhibit only a partial understanding of the code they produce. Such gaps in comprehension may not become apparent until later stages of their studies, at which point misconceptions are more difficult to address and correct. Developing a deep understanding of code is particularly critical in the current context, where learners increasingly have access to generative artificial intelligence (GenAI) tools, such as GitHub Copilot. A commonly employed strategy to assess and promote code comprehension involves posing targeted questions on student submissions, enabling instructors to evaluate understanding directly. This method can also incidentally assist in identifying potential cases of plagiarism. However, while effective, this approach is resource-intensive and presents significant challenges in terms of scalability and sustainability. In response to these limitations, this work proposes an automated solution that leverages GenAI to generate multiple-choice code comprehension questions. We present an empirical study of this approach conducted within an introductory programming course, integrating the method within the CodeRunner automated assessment platform.
ORCID iDs
Goodfellow, Martin
ORCID: https://orcid.org/0000-0003-2151-8442, Lambert, Alasdair
ORCID: https://orcid.org/0000-0002-9762-2193, Booth, Robbie and Fagan, Andrew
ORCID: https://orcid.org/0000-0001-9714-2096;
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Item type: Article ID code: 96355 Dates: DateEvent20 May 2026Published20 May 2026Published Online13 April 2026AcceptedSubjects: Education > Theory and practice of education
Science > Mathematics > Electronic computers. Computer scienceDepartment: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 27 May 2026 10:20 Last modified: 30 Jun 2026 13:22 URI: https://strathprints.strath.ac.uk/id/eprint/96355
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