Author Information
Michael Pin-Chuan Lin, Mount Saint Vincent University, CanadaGaganpreet Jhajj, Athabasca University, Canada
Fuhua Lin, Athabasca University, Canada
Eric Poitras, Dalhousie University, Canada
Daniel Chang, Simon Fraser University, Canada
Jeeho Ryoo, Fairleigh Dickinson University, Canada
Abstract
The rapid adoption of generative artificial intelligence (GenAI) tools such as ChatGPT has outstripped empirical evidence regarding their instructional value, particularly in computer science education (CSE), where learning depends on debugging, problem-solving, and iterative reasoning. This study presents a rapid review of peer-reviewed empirical research published between 2022 and 2025, after the public release of ChatGPT, synthesizing evidence on how GenAI is used and perceived in CSE contexts. A systematic search of the EBSCO platform identified nine studies examining the active use of GenAI by learners or academics. Across the included studies, GenAI use clustered into five instructional roles: general learning support; programming and debugging assistance; code comprehension and self-explanation support; feedback and revision support; and assessment-oriented instructional support. Findings indicate that GenAI is typically positioned as on-demand support embedded within existing coursework, with learning benefits reflected primarily in process-level outcomes such as engagement, revision, and evaluative judgment. Student perceptions were generally positive but qualified by concerns about over-reliance, output reliability, and academic integrity, while academic perspectives reflected cautious and conditional adoption. The review highlights the need for principled integration approaches that emphasize instructional framing, assessment alignment, and self-regulated engagement, and identifies directions for future research on durable learning outcomes and responsible use in CSE.
Paper Information
Conference: WCE2026Stream: Innovation & Technology
This paper is part of the WCE2026 Conference Proceedings (View)
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To cite this article:
Lin M., Jhajj G., Lin F., Poitras E., Chang D., & Ryoo J. (2026) Generative AI’s Role in Computer Science Classrooms: A Rapid Mapping Review ISSN: 2760-7259 The Washington DC Conference on Education 2026: Official Conference Proceedings (pp. 219-231) https://doi.org/10.22492/issn.2760-7259.2026.20
To link to this article: https://doi.org/10.22492/issn.2760-7259.2026.20
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