Generative AI Adoption and Self-Reported Academic Integrity Risk: A Student Taxonomy in Latin American Higher Education

Abstract

Generative Artificial Intelligence (GenAI) is reshaping technology-mediated learning environments in higher education, yet the structural heterogeneity of student adoption patterns—particularly across multimodal dimensions beyond text—remains empirically under-characterized. This study develops an empirically derived, data-driven taxonomy of GenAI adoption among university students, identifying distinct user profiles and their disciplinary and ethical risk implications. A quantitative, cross-sectional design was employed with a disciplinarily quota-balanced sample of 3415 students from eight Ecuadorian public universities, stratified across seven areas of knowledge according to the UNESCO classification. K-means cluster analysis on five continuous multimodal variables (text generation, mathematical problem-solving, programming, image generation, and music generation) yielded four distinct profiles: Passive (44.3%), Artist (24.4%), Technical (18.3%), and Comprehensive (13%). Profile membership showed a significant structural association with academic discipline (χ2 = 517.85; Cramér’s V = 0.225). Profiles differed substantially in their self-reported propensity for intellectual delegation to AI systems, with the Comprehensive profile reporting the highest levels (η2 = 0.114, 95% CI [0.092, 0.139]). These findings suggest that ethical risk in AI-mediated academic environments is not uniformly distributed but structurally associated with whether outputs are verifiable or directly presentable, with implications for differentiated AI literacy programs and institutional governance frameworks in higher education.