Assessing Student Readiness for Generative Artificial Intelligence Battery: A Case Study of ChatGPT in a University Setting

Abstract

Research examining the multidimensional nature of AI adoption readiness within Saudi Arabia's Vision 2030 context remains limited. This study investigates ChatGPT adoption readiness among undergraduate students at a Saudi university, extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with personal innovativeness, trust, attention, and anxiety. A sample of 537 students from Jazan University completed an Arabic-language instrument developed through expert review (content validity index = 0.96) and pilot testing (N = 45). Confirmatory Factor Analysis revealed that a seven-factor measurement model demonstrated excellent fit (CFI = .964, TLI = .957, RMSEA = .051, SRMR = .035), with all factor loadings exceeding .70, composite reliability values ranging from .859 to .891, and average variance extracted estimates from .627 to .687, supporting convergent validity and internal consistency. Exploratory Graph Analysis (EGA) suggested an alternative four-dimensional structure, though the seven-factor model provided superior theoretical precision. Findings contribute to the theoretical refinement of technology acceptance models and provide practical guidance for designing targeted interventions to foster responsible AI integration in higher education within the cultural context of Saudi Arabia's Vision 2030.