AI-assisted pair programming in undergraduate computing education: A mixed-methods study on student acceptance and collaborative dynamics

Keywords: AI-assisted pair programming, Pedagogical innovation, Higher education, Educational technology, Collaborative learning

Abstract

This article presents a scientific contribution articulating theoretical synthesis, technopedagogical design, and empirical validation of AI-assisted pair programming. In a first phase, a systematic literature review was carried out following the PRISMA methodology, based on 35 academic sources indexed in Springer, ACM and ScienceDirect, in order to identify trends, benefits and limitations in the use of conversational assistants, virtual tutors, and Large Language Models –LLMs applied to programming teaching. The results of this review show that AI favors motivation, reduces anxiety and contributes to improving academic performance, although it does not replace the social interaction typical of collaborative learning, in addition to showing gaps in the integration of these solutions in management systems such as Moodle and in Latin American contexts. Based on these conclusions, an exploratory classroom study was designed with a prototype tool that combines pair-programming, AI and technological development, the objective of which was to analyze student perceptions about its use. Preliminary findings show a positive assessment, highlighting the usefulness of automated support for immediate feedback and reducing cognitive load, although students emphasized the need for ongoing teacher support and progressive scaffolding to consolidate learning. It is concluded that the integration of pair programming, AI, and technological developments constitutes a promising pedagogical innovation, whose effective adoption depends on clear institutional policies and responsible implementation.

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How to Cite
Suarez-Carvajal, F.-E., Lobo Quintero, R. A., Santoyo Diaz, J. S., Pinzón Castellanos, J., & Gamba González, Y. G. (2026). AI-assisted pair programming in undergraduate computing education: A mixed-methods study on student acceptance and collaborative dynamics . Revista Colombiana De Computación, 27(1), 40–52. https://doi.org/10.29375/25392115.5739

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2026-06-30
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