Understanding academic performance of TLE pre-service teachers in AI-influenced learning environments

Abstract

This study investigated the determinants of academic performance among pre-service students in an AI-influenced learning environment by examining the roles of expectations about artificial intelligence and its impact on education, learning experience, and motivation. A quantitative research design was employed, utilizing an online survey distributed through Google Forms to undergraduate students enrolled in the Bachelor of Technical-Vocational Teacher Education (BTVTED) and Bachelor of Technology and Livelihood Education (BTLED) programs at a major state university in Cebu, Philippines. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to assess both the measurement model and the structural model. The findings showed that expectations about AI exerted the strongest positive influence on academic performance, followed by learning experience and motivation. The proposed model accounted for 78.1% of the variance in academic performance (R² = 0.781), indicating substantial explanatory power. Overall, the results suggest that strengthening students’ positive expectations toward AI, enriching their learning experiences, and fostering stronger motivation may significantly enhance academic outcomes in TLE teacher education. These findings highlight the importance of creating supportive AI-integrated learning environments that encourage engagement, confidence, and meaningful educational experiences among future teachers. The study also underscores the growing educational relevance of AI readiness in shaping student success within teacher preparation programs.