Preservice English teachers' attitudes toward GenAI: A qualitative study based on UTAUT2 model
Abstract
This qualitative study examines the psychological attitudes of preservice English teachers toward generative artificial intelligence (GenAI) through the UTAUT2 model. Thematic analysis of semi-structured interviews with 15 undergraduate teacher candidates indicated that their perceptions exhibit psychological ambivalence, combining enthusiasm and caution, highlighting both the prospective advantages and constraints of GenAI. Specifically, core UTAUT2 constructs shaped attitudes: Performance expectancy (time-saving, quality improvement), effort expectancy (ease of use, learning adaptability), social influence (peer and instructor encouragement), facilitating conditions (availability, technical guidance), hedonic motivation (enjoyment, curiosity), price value (cost-effectiveness, subscription concerns), and habit (behavioral automaticity, routine integration). Beyond these established constructs, three additional psychological dimensions emerged: creativity, ethical awareness, and human-AI synergy and the irreplaceable human role. These findings extend UTAUT2 by highlighting the role of moral judgment, affective engagement, and identity-related meaning-making in technology acceptance. The study contributes to the psychology of technology adoption by illustrating how future teachers cognitively and affectively negotiate GenAI within an emerging human-AI educational context. • Preservice teachers display ambivalent attitudes toward GenAI. • UTAUT2 explains cognitive and motivational acceptance of GenAI. • Enjoyment and habit drive sustained GenAI use intentions. • Ethical reasoning shapes GenAI acceptance beyond usefulness. • Human-AI synergy reinforces teacher professional identity.
Fields of Study & Subjects
AI in Service Interactions
Educational Games and Gamification
Educational Strategies and Epistemologies
Publication details
- Journal / source
- Acta Psychologica
- Publisher
- Elsevier BV
- Volume
- 263
- Pages
- 106254–106254
- Publication date
- 16 January 2026
- Language
- EN