Factors Predicting Pre‐Service Teachers' Intentions to Use Artificial Intelligence Software

ABSTRACT

This research investigates the factors affecting pre‐service teachers' (PSTs) behavioural intentions (BI) to use artificial intelligence software in their future classes. The study examined factors affecting PSTs' behavioural intentions using an extended UTAUT framework comprising Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and Hedonic Motivation (HM). The study included 432 PSTs from the Faculty of Education at a state university in Eastern Türkiye. “Pre‐service Teachers' Acceptance Scale for AI‐Supported Education”, a valid and reliable scale, was used for data collection. The data were analysed using multiple linear regression. The results revealed that PE, SI, and HM were significant predictors of PSTs' BI, whereas EE and FC were not significant predictors. Besides, the research revealed that the independent variables explained 67% of the variance in PSTs' BI.