AI-based tools for enhancing reflective practice and self-efficacy in pre-service teachers

Abstract

The integration of artificial intelligence (AI) in teacher education is revolutionizing traditional assessment practices by providing personalized feedback, automated evaluations, and data-driven insights. This study explores the impact of AI-based assessment tools on reflective practice and self-efficacy among pre-service teachers. Using a pre-test and post-test experimental design, 100 integrated teacher education program (ITEP) 6th-semester students were assessed across gender and program types (BA, B.Ed and BSc B.Ed). Significant improvements were observed in both reflective practice and self-efficacy scores after the intervention, with female students outperforming males in reflective practice gains. BSc, B.Ed students demonstrated slightly higher improvements in both dimensions compared to BA, B.Ed students, reflecting the alignment of AI tools with science-oriented curricula. These findings highlight the potential of AI tools to foster critical professional skills and prepare pre-service teachers for the dynamic challenges of 21st-century classrooms. The study underscores the need for gender-sensitive interventions, subject-specific adaptations, and continued research into the scalability of AI technologies in teacher education.