University Students’ Engagement with Artificial Intelligence: A Cluster Analysis of Learner Profiles in AI Literacy

Abstract

The rapid integration of artificial intelligence (AI) technologies in higher education has created new opportunities and challenges for student learning. This study examines how university students engage with AI in their learning processes by identifying distinct learner profiles based on their AI literacy, experiences, actions, and perceptions of faculty modeling. Using cluster analysis on a sample of 353 undergraduate students from a private university in Mexico, we identified three distinct profiles through principal component analysis and K-means clustering: “Critically Engaged Navigators” (32%), “Pragmatic Technicians” (37%), and “Emerging Users” (32%). The analysis reveals significant differences in learning exposure, social learning patterns, autonomous learning strategies, responsible AI use, and perceptions of faculty modeling across clusters. These findings have important implications for differentiated pedagogical design, faculty development programs, and the development of adaptive educational technologies that can support diverse learner needs in AI-enhanced educational environments. The study contributes to the growing literature on AI literacy while providing practical insights for educators seeking to optimize AI integration in higher education contexts.