Recent reports associate teachers’ turnover intentions as a response to disruptions and transition of the teaching-learning process (e.g., flexible learning) due to the COVID-19 pandemic. This study explores the antecedents of teachers’ intention to teach in a flexible learning system (FLS) and their turnover intentions via an extended theory of planned behavior (TPB). Using a cross-sectional survey, 417 valid responses were analyzed to model this phenomenon, with self-efficacy and digital nativity as antecedent variables and job satisfaction and organizational commitment as predictors of teachers’ attitudes. We also examined whether the intention to teach in FLS is moderated by age. Results from Partial Least Squares-Structural Equation Modeling (PLS-SEM) generate insights from the 12 hypothesized paths of the proposed model. We uncover salient findings such as (1) the extended TPB model explains 61% of the overall variations of intention to teach in FLS, (2) attitude is positively explained by job satisfaction and organizational commitment, (3) digital nativity and self-efficacy positively influence the attitude, subjective norm, and perceived behavioral control in the context of teaching in FLS, with perceived behavioral control as the strongest predictor of teaching intention, (4) the negative relationship between intention to teach in FLS and turnover intention of teachers, and (5) age has no moderating effect on the teachers’ intention to teach in FLS. Some practical insights and future research works are outlined in light of these findings.

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.