Akademik Veri Yönetim Sistemi
AERA OPEN, cilt.11, sa.1, ss.1-23, 2025 (SSCI, Scopus)
Understanding motivational drivers behind prospective teachers’ artificial intelligence (AI) integration intentions is critical. While prior models such as the technology acceptance model overlook motivational dynamics, this study used expectancy-value theory to assess how expectancy, attainment, utility, intrinsic value, and cost shape intentions. Data from 454 prospective teachers were analyzed via structural equation modeling using the Behavioral Intention Scale and the Questionnaire of Artificial Intelligence Use Motives. Utility value (β = .29, p < .001) was the strongest predictor, followed by cost (β = −.27), intrinsic value (β = .25), attainment (β = .21), and expectancy (β = .10). The model explained 62% of variance in behavioral intention. Control variables, including gender, class level, and AI usage frequency, significantly influenced intentions. Findings suggested that teacher education programs should enhance AI’s perceived utility, address implementation costs, and strengthen expectancy through training to foster adoption. Emphasizing AI’s practical relevance within supportive environments can bridge its potential with classroom integration.