AI-Empowered Cloud Mentoring for Cross-Institutional Teacher Development: A Triadic Synergy Model and Its Effectiveness

The Collaborative Quality Improvement Program for Teacher Education in China, launched by the Ministry of Education in 2022, relies primarily on cross-institutional mentoring, a mechanism that is often constrained by geographical distance and insufficient individualized guidance, as well as a lack of systematic effectiveness evaluation. Focusing on AI-empowered “cloud mentoring”, this paper constructs a triadic synergy paradigm (expert teacher, AI system, and novice teacher) that reengineers cross-institutional mentoring into four data-driven phases: diagnosis, matching, mentoring, and reflection. To evaluate the effectiveness of this model, a quasi-experimental mixed-methods design with 23 mentoring pairs (12 in the experimental group and 11 in the control group, supported by 10 expert mentors) was employed over an 18-week intervention period. Data were collected through multimodal classroom video analysis, TPACK scale assessments, and coding of mentoring dialogue transcripts. The results demonstrate that the AI-enhanced model significantly improved novice teachers’ classroom effectiveness overall (F = 5.43, p = 0.029, η 2 p = 0.21) and deepened mentoring discourse quality, with higher-order cognitive engagement rising from 41.3% in the control group to 62.4% in the experimental group. Technological knowledge (TK) also showed significant improvement (F = 5.89, p = 0.023), teachers perceived the AI tools as useful (PUM = 4.31) but raised legitimate concerns regarding data privacy and algorithmic transparency. The study contributes a triadic synergy theoretical model that extends teacher professional development research and provides a scalable, empirically grounded blueprint for high-quality cross-institutional mentoring, while also discussing critical tensions between technological empowerment and humanistic care that must be managed for ethical implementation.