Determining pre-service teachers’ intention to use generative artificial intelligence through expectancy–value theory

The rapid expansion of generative artificial intelligence (GenAI) has significantly transformed digital learning environments in higher education. Despite the increasing use of AI-assisted tools among university students, limited empirical research has examined how pre-service teachers evaluate and intend to use these technologies, particularly within developing higher education contexts. This study investigates the factors influencing pre-service teachers’ behavioral intention to use generative AI by applying expectancy–value theory (EVT), focusing on attainment value, intrinsic value, utility value, and perceived cost. A quantitative research design was employed using an online survey administered to 380 pre-service teachers enrolled in a state university in Cebu, Philippines. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among EVT constructs and behavioral intention. The study also explored whether sex moderates the relationship between perceived cost and behavioral intention. The findings indicate that attainment value and utility value significantly influence pre-service teachers’ intention to use generative AI tools, highlighting the importance of perceived usefulness and task relevance in shaping technology adoption. In contrast, intrinsic value and perceived cost did not demonstrate meaningful effects on behavioral intention. The moderating role of sex in the relationship between perceived cost and behavioral intention was likewise not supported. These results suggest that pre-service teachers’ intentions to engage with generative AI are primarily driven by perceived academic benefits rather than enjoyment or perceived barriers. The study contributes to the emerging literature on AI adoption in teacher education by providing an EVT-based explanation of motivational factors influencing GenAI use. The findings also offer practical implications for higher education institutions seeking to develop evidence-based policies, training programs, and pedagogical guidelines that support responsible and effective integration of generative AI in teacher preparation.