Latent profiles of instrumental and relational generative AI engagement among university students

Abstract

This study examines how university students engage with generative AI (GenAI) across instrumental and relational domains using two independent, domain-specific exploratory surveys of Chinese university students (Study 1: N = 517; Study 2: N = 508). Study 1 assessed instrumental indicators, including agency, delegation/offloading, trust/adoption, and perceived gains, whereas Study 2 assessed relational indicators, including anthropomorphism, emotional safety/control, disclosure/reliance, and displacement/withdrawal. Latent profile analysis identified two profiles in each study. In Study 1, “Augmented Users” showed higher endorsement of agency, trust, gains, and delegation than “Limited Users.” In Study 2, “Attached Users” showed higher endorsement of anthropomorphism, emotional safety, disclosure, and displacement/withdrawal than “Uninvolved Users,” and also reported higher loneliness and slightly lower social satisfaction. Because the two studies used independent samples and different indicators, the findings should be interpreted as separate domain-specific profile analyses rather than a direct test of a unified person-level typology. The study provides exploratory person-centered evidence for differentiating instrumental and relational patterns of GenAI engagement in higher education.