This study aims to explore the mediating role of perceived organizational support(POS) in the relationship between university teachers' competence and job performance. Through a questionnaire survey of 968 undergraduate university teachers in China, 879 valid questionnaires were collected. The study employed quantitative methods, constructing a university teacher competence scale comprising foundational competence, teaching competence, research competence, and innovation competence, as well as a job performance scale encompassing task performance, relationship performance, and adaptive performance. Structural equation modeling and SOBEL tests were used for data analysis. The results showed that POS exhibited different mediating effect patterns between various competence dimensions and job performance dimensions: no significant mediating effect was found in task performance; partial mediating effects were observed in relational performance and adaptive performance; and a complete mediating effect was identified between foundational competence and adaptive performance. The study provides theoretical support and practical guidance for university teachers management, emphasizing the importance of establishing a competence-based human resources management system, strengthening teachers perceptions of organizational support, and establishing diverse evaluation standards. Future research could further explore the impact of different cultural backgrounds and organizational types on mediating effects.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.