Abstract
This systematic literature review synthesizes the rapidly expanding body of empirical research on generative artificial intelligence (GenAI) in lower division general education courses. Across disciplines and instructional settings, the review analyzes publication patterns, theoretical perspectives, methodological approaches, and recurring findings related to student learning. Evidence consistently suggests that GenAI can strengthen performance, engagement, confidence, and perceptions of usefulness, particularly when supported through intentional instructional design and opportunities to build AI literacy. At the same time, the literature is dominated by short-term evaluations of product quality, leaving the mechanisms of learning and longer-term development underexplored. In response, the review introduces a conceptual framework that calls for a developmental turn toward process-based inquiry, critical GenAI literacies, researcher positionality, and transfer beyond immediate coursework. This agenda supports more cumulative, theory-informed scholarship and better alignment between GenAI integration, early college readiness, and durable professional capabilities.
Publisher
South Dakota State University
Rights
© 2026 the authors
Recommended Citation
Childs, Michelle and Kilgore, Chris, "Generative AI in Early Higher Education: A Systematic Literature Review and Agenda for Future Studies" (2026). The Interdisciplinary Journal for Research on Pedagogy and Learning Design, Vol. 1, Issue 1., Fall 2026. 5.
https://openprairie.sdstate.edu/ijrpld/5
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License