Determinants of pre-service teachers behavioral intention to use generative AI in research

Abstract

Generative artificial intelligence (GenAI) is increasingly being integrated into higher education, creating novel opportunities for pre-service teachers to enhance their educational experience. However, the factors influencing students’ behavioral intention to adopt these tools remain underexplored. To this end, a theoretical model to explain pre-service teachers’ behavioral intention integrating behavioral intention, performance expectancy, attitude, social influence and habit, is proposed in this work. A total of 360 senior students from the College of Education at a leading Philippine state university participated in the study. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to examine the predictive relationships between constructs. Results indicate that attitude and performance expectancy are the strongest predictors of behavioral intention, while social influence and habit, although statistically significant, show smaller effects. The model explains 78.7% of the variance in behavioral intention (R2 = 0.787) and 73% in performance expectancy (R2 = 0.730), demonstrating strong predictive power. These findings highlight the dominant role of cognitive and affective factors in shaping GenAI adoption for research purposes, suggesting that personal beliefs about usefulness and positive attitudes outweigh social or habitual influences. The study contributes to UTAUT-based literature by focusing on research-specific GenAI use among Philippine pre-service teachers a population and task domain that remain underrepresented and offers practical insights for supporting responsible AI adoption in teacher education.

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