Factors Shaping Pre-service Biology Teachers’ Acceptance of Generative Artificial Intelligence - Evy Noviana; Ammar Ahadi Putra; Ratih Dhamayyana Dwi Cinthami; Hilman Qudratuddarsi (JURNAL BIOSHELL, 2026)
The rapid diffusion of generative artificial intelligence (GAI) has introduced new opportunities and challenges for teacher education, particularly within STEM disciplines, specifically biology education. This study investigates the determinants of generative AI acceptance among Generation Z pre-service biology teachers by integrating constructs from the Technology Acceptance Model and Diffusion of Innovation theory with pedagogically grounded variables. Using a quantitative cross-sectional survey design, data were collected from 318 pre-service biology teachers enrolled at two Indonesian universities. Partial least squares structural equation modeling (PLS-SEM) was employed to examine the relationships among trialability, relative advantage, perceived compatibility, trust, feedback quality, perceived assessment quality, subjective norms, perceived ease of use, perceived usefulness, attitude, behavioral intention, and acceptance of generative AI. The results indicate that trialability, relative advantage, and compatibility significantly predict perceived ease of use, while relative advantage and trust significantly influence perceived usefulness. Feedback quality and subjective norms positively shape attitudes toward generative AI, whereas perceived assessment quality shows no significant effect. Perceived ease of use and attitude emerge as key predictors of behavioral intention, which strongly determines acceptance. The findings highlight the central roles of affective, social, and trust-related factors in shaping generative AI adoption among future biology teachers. This study contributes to the emerging literature on AI in STEM teacher education and offers practical implications for designing pedagogically meaningful and responsible AI integration in teacher preparation programs.