AI-Powered Analysis of Teacher Self-Efficacy and Teaching Quality in Higher Vocational Education

Abstract

This study explores the intricate relationship between teacher self-efficacy (TSE) and teaching quality within higher vocational education. Utilizing advanced AI tools, we conducted a comprehensive analysis of how self-efficacy influences instructional practices and the overall learning environment. The research employed a mixed-methods approach, integrating quantitative data from teacher evaluations and qualitative interviews to identify key factors affecting TSE. ​Findings indicate that higher levels of self-efficacy correlate with improved teaching quality, characterized by effective classroom management, enhanced student engagement, and the implementation of innovative teaching strategies.​ Additionally, the research highlights the significance of context-specific assessments of TSE, emphasizing the need for tailored evaluation tools that reflect the unique challenges faced by vocational educators. Ultimately, this study provides valuable insights into fostering teacher development, suggesting that enhancing self-efficacy through targeted professional development can lead to better educational outcomes in vocational settings. The implications of these findings underline the necessity for educational policies that prioritize teacher support mechanisms, thereby aligning teacher capabilities with the demands of modern vocational education.