The purpose of this study is to examine the structural relationships among proactive personality, principal’s instructional leadership, teacher engagement, and effective teaching behavior of specialized subject teachers in vocational high schools (i...
The purpose of this study is to examine the structural relationships among proactive personality, principal’s instructional leadership, teacher engagement, and effective teaching behavior of specialized subject teachers in vocational high schools (including specialized high schools and Meister high schools), and to empirically verify the moderated mediating effect of Artificial Intelligence(AI) teaching self-efficacy. To achieve this purpose, the study set the following objectives: First, to establish a structural model among proactive personality, principal’s instructional leadership, teacher engagement, and effective teaching behavior. Second, to investigate the direct effects of proactive personality, principal’s instructional leadership, and teacher engagement on effective teaching behavior. Third, to examine the indirect effects of proactive personality and principal’s instructional leadership on effective teaching behavior via teacher engagement. Fourth, to verify whether the mediating effect of teacher engagement is moderated by AI teaching self-efficacy.
The study population consisted of approximately 12,618 specialized subject teachers in vocational high schools nationwide. Data were collected through an online survey using stratified random sampling. The survey instruments included measures for effective teaching behavior, proactive personality, principal’s instructional leadership, teacher engagement, AI teaching self-efficacy, and demographic characteristics. The scale for effective teaching behavior was developed and validated by the researcher, while other scales were adapted from previous studies to fit the research context. The reliability of all instruments was confirmed through pilot and main surveys.
Data collection was conducted from October 27 to November 3, 2025. A total of 489 questionnaires were retrieved. After excluding responses from ineligible participants, insincere responses, or outliers, 385 valid responses were used for the final analysis. Data analysis was performed using SPSS Statistics 29.0 and Mplus 9.0.
The key results are as follows: First, the structural model involving proactive personality, principal’s instructional leadership, teacher engagement, and effective teaching behavior showed a good fit to the empirical data, satisfying all fit indices except for the Chi-square value. Second, proactive personality and principal’s instructional leadership did not have significant direct positive effects on effective teaching behavior but had significant positive effects on teacher engagement. Teacher engagement was found to have a strong positive effect on effective teaching behavior. Third, teacher engagement fully mediated the relationships between proactive personality and effective teaching behavior, and between principal’s instructional leadership and effective teaching behavior. Fourth, AI teaching self-efficacy negatively moderated the indirect effects of proactive personality and principal’s instructional leadership on effective teaching behavior via teacher engagement. Specifically, the influence of teacher engagement on effective teaching behavior became less pronounced in groups with higher AI teaching self-efficacy.
Based on these findings, the study draws the following conclusions: First, the proposed model is suitable for explaining the mechanism of effective teaching behavior, suggesting that teaching behavior is determined by a complex interaction of psychological mechanisms (engagement) and technological efficacy, rather than solely by personal traits or environmental support. Second, proactive personality and principal’s instructional leadership are not direct determinants of effective teaching behavior but are essential antecedent resources that ignite teacher engagement. In other words, when teachers initiate change and principals support them, positive attachment to the profession is formed, leading to effective teaching behavior. Third, teacher engagement acts as a core factor that transforms potential competencies into actual performance and plays a role in concentrating teachers' energy on instructional improvement. Fourth, AI teaching self-efficacy functions as a professional substitute and a safety net, reducing reliance on the teacher's psychological state (engagement) and maintaining instructional quality. This implies that AI teaching self-efficacy is a key resource for reducing emotional exhaustion and increasing work efficiency in the era of digital transformation.
Based on these conclusions, the study suggests the following: First, interventions such as personal initiative training and the creation of psychological safety zones are needed to foster teacher proactivity. Second, principals should balance their roles as external managers focused on performance with their roles as instructional leaders supporting teacher growth. Third, systematic educational support to enhance teachers' AI utilization capabilities and tailored support strategies based on competency levels are required. Finally, future research should include comparative studies by school type and subject track, mixed-method research including qualitative approaches, and exploration of factors hindering teacher engagement.