ISEDJ

Information Systems Education Journal

Volume 24

V24 N1 Pages 4-18

Jan 2026


The Rapid Rise of Generative AI Adoption among First-Year College Students


Mark Frydenberg
Bentley University
Waltham, MA USA

Kevin Mentzer
Nichols College
Dudley, MA USA

Adam Patterson
University of Connecticut
Storrs, CT USA

Abstract: This paper explores the rapid adoption of Artificial Intelligence (AI) tools among 1,597 first-year college students at two New England college campuses during three semesters (Spring 2023, Fall 2023, and Spring 2024), with a specific focus on Generative AI (GAI) tools like ChatGPT. After a comprehensive literature review and empirical analysis, findings indicate a significant increase in GAI awareness and utilization, primarily for academic tasks such as homework assignments, with some use in quizzes and exams. Regression analysis reveals that strong data literacy skills, specifically those related to data discovery, collection, and analysis, are linked to the adoption of AI technologies, while general digital literacy skills such as ability to use productivity applications and databases were not found to have a similar correlation. These results amplify the importance of enhancing data literacy to facilitate effective AI tool integration in academic settings. The study highlights the need for targeted educational strategies to improve data literacy, thereby promoting equitable access to AI technologies and mitigating potential biases. A limitation of this study is that the scope is limited to incoming college students. This research contributes to the understanding of AI adoption dynamics in higher education, providing insights for educators and policymakers to support the ethical and effective use of GAI tools in academic settings.

Download this article: ISEDJ - V24 N1 Page 4.pdf


Recommended Citation: Frydenberg, M., Mentzer, K., Patterson, A., (2026). The Rapid Rise of Generative AI Adoption among First-Year College Students. Information Systems Education Journal 24(1) pp 4-18. https://doi.org/10.62273/HVNN2048