Effects of Generative AI, Digital Leadership, and Teacher Professional Development on Multi-dimensional Student Development

Abstract

Generative artificial intelligence (GAI), digital leadership, and teacher professional development affect multi-dimensional student growth in Xinzheng, China, senior high schools. Although the level of digital infrastructure investment is high, a large number of inland schools suffer an input-output paradox, in which technological inputs are not enhanced by pedagogy or student performance. This paper develops and analyses a parallel-serial multiple mediation model grounded in an integrated theoretical framework that combines the Input–Process–Output (I-P-O) model, Digital Leadership Theory, Social Cognitive Theory, and UTAUT, this study explains how generative AI influences student development through organizational leadership, teacher learning, and technology acceptance mechanisms. An explanatory sequential mixed-methods design was used. Quantitative data were obtained by the administrators (n=40), instructors (n=102) and Grade 11 students (n=458) of 10 public and private schools. The quantitative results were placed within the context of semi-structured interviews with 50 stakeholders. SEM and Bootstrap resampling (5,000 iterations) were used to evaluate direct, indirect and mediation effects. The researchers concluded that the use of GAI is predictive of digital leadership and teacher professional development. Digital leadership is a predictor of teacher professional development and student growth, with teacher professional development having the strongest direct impact on student growth. The mediation analysis shows digital leadership and teacher professional development to be key mediators, and there is a serial mediation path. The model accounts for 58.6 percent of the variance in student development and fits well and is in varied between public and private schools. Qualitative results demonstrate that integration of AI is shallow, hardware-based investment, little pedagogical change, and uneven teacher competence in AI-enhanced teaching. Even though students become more successful students, the fear of overuse of AI and lack of critical thinking remains. This work contains an empirically tested model of AI-mediated education change in non-metropolitan settings and practical suggestions to policymakers, school administrations, and teachers to facilitate sustainable and quality digital change.