AI Competency Assessment and Ranking: A Framework for Higher Education

Abstract

Generative artificial intelligence is reshaping students’ learning strategies, yet most definitions of “AI competency” rely on self-reports and prescriptive checklists. We analyzed data from N = 686 university students in Spain to uncover behavioral patterns of AI use and translate them into practical guidance for teaching and policy. Using unsupervised clustering (k-means) complemented with a topological summary (Mapper), we identified four coherent profiles along a continuum of engagement and self-regulation: Low-Engagement, Active–Cautious, Balanced–Confident, and High-Use–Vigilant. The profiles differ in how often students use AI, how they revise outputs, their reliance on AI, and their ethical awareness, and they show distinct emotional patterns (e.g., curiosity, motivation, stress). A continuous structure links profiles rather than separating them rigidly. Self-rated digital competence and studying in STEM fields were associated with higher-level profiles. Overall, the results support a layered, data-informed view of AI competency that prioritizes observed practices over single summary scores. We introduce AI CAR as a formative framework to help institutions (i) locate students on this continuum and (ii) design targeted supports that strengthen revision habits, ethical reflection, and self-regulation across the curriculum.