Acceptance and use of GenAI among medical and health sciences students in Saudi Arabia: an extended TAM study

Generative artificial intelligence (GenAI) is increasingly used by medical and health profession students. Despite this trend, most existing evidence remains descriptive, with limited theory-driven work explaining how students' evaluations translate into intention and actual use in high-stakes learning environments. This study investigated the acceptance and actual use of GenAI among medical and health profession students in Saudi Arabia using a Technology Acceptance Model (TAM). A cross-sectional survey was conducted across multiple public and private Saudi tertiary institutions, yielding 505 valid responses for descriptive analyses and 494 users for confirmatory factor analysis and structural equation modeling (SEM). The study tested the core TAM relationships among perceived ease of use, perceived usefulness, attitude toward use, behavioral intention, and actual use together with contextual factors such as social influence, trust, and perceived risk. Most core TAM relationships were supported: perceived ease of use strongly predicted perceived usefulness, perceived usefulness predicted both attitude and behavioral intention, and behavioral intention strongly predicted actual use. Perceived ease of use showed a small but significant negative association with attitude. Social influence and trust positively predicted behavioral intention, and trust also positively predicted perceived usefulness. Perceived risk was not significantly associated with behavioral intention and showed only a weak association with attitude. These findings indicate an intention-driven pattern of GenAI adoption grounded primarily in perceived educational value and reinforced by trust and social norms, while risk awareness appears to coexist with selective, self-regulated engagement.