Artificial Intelligence Use Profiles and Academic Motivation in Higher Education: A Latent Profile and SEM Analysis Among Spanish Social Sciences Students

This study analyzes the relationship between patterns of artificial intelligence (AI) use and university students’ academic goals, also incorporating a reflection on educational sustainability and the responsible use of digital technologies in higher education. Through cluster analysis, ANCOVA, correlations, and structural equation modeling (SEM), three AI usage profiles (low, balanced, and high) were identified, along with their association with learning, achievement, and social reinforcement goals. The re-sults show significant differences between profiles, as well as positive relationships between AI support dimensions (educational, informational, and emotional) and aca-demic goals.The structural model explains between 29% and 47% of the variance. The R² value was 0.47. The SEM showed an adequate fit to the data (CFI = .96; TLI = .95; RMSEA = .048; SRMR = .041), indicating a satisfactory representation of relationships among variables. The findings suggest that a balanced and ethical use of AI can con-tribute to the development of more sustainable educational practices by fostering learning personalization, resource optimization, and the promotion of responsible dig-ital competencies aligned with sustainability objectives in education.