Impact of Generative Artificial Intelligence Use on the Academic Performance of Undergraduate Students in Canada

Abstract

Generative artificial intelligence (AI) has entered everyday undergraduate study faster than the evidence base has kept up with it, and the evidence that does exist points in opposite directions. This paper synthesizes peer-reviewed and carefully delimited contextual research on how generative AI use relates to undergraduate academic performance, with attention to what Canadian universities can already act on. Searches of Google Scholar, Scopus, Web of Science, ERIC, and ScienceDirect covering 2022 to 2026 produced a two-tier evidence base: Tier 1 peer-reviewed empirical and synthetic studies of student learning outcomes, and Tier 2 supplementary sources admitted under stated justifications, including contextual Canadian and international surveys, one secondary-school field experiment, and one preprint mechanistic study. Experimental syntheses report medium to large short-term gains when ChatGPT is built into instruction. Survey work points the other way: frequent unstructured use tracks with procrastination, self-reported memory problems, and slightly lower grades, and unrestricted access during practice has been shown to depress later unaided performance. Purpose of use reconciles most of that disagreement, because a tool that scaffolds thinking behaves very differently from one that replaces it. Canadian peer-reviewed studies document heavy campus adoption and considerable student ambivalence about integrity and learning, yet almost none link purpose-differentiated use to measured performance. Closing that gap matters for assessment redesign, AI-literacy programming, and the credibility of the credentials Canadian universities issue.