Strategies and Practices for Enhancing Higher Education Faculty Teaching Capabilities through Artificial Intelligence

A new wave of technologies—represented by generative AI, multimodal learning analytics, and human-machine collaborative systems—is driving a three-dimensional transformation of higher education faculty roles. Educators are evolving from “knowledge transmitters” into integrated “learning designers—value shapers—data decision-makers.” Guided by the Ministry of Education's policy framework “Digital Empowerment for Teacher Development,” this study employs field research across over 30 universities and fuzzy set qualitative comparative analysis (fsQCA) of 280 secondary colleges. Through in-depth comparative case studies across three disciplines (STEM, humanities, arts/sports), it systematically elucidates the theoretical mechanisms, core technical architectures, and differentiated implementation pathways for AI-enhanced teaching capabilities. (fsQCA) across 280 secondary colleges, and in-depth comparative case studies across three disciplinary categories (science/engineering, humanities, physical education/arts). It systematically elucidates the theoretical mechanisms, core technological architecture, and differentiated implementation pathways for AI-empowered teaching capabilities, establishing a new triadic paradigm of “technology-institutional-humanistic” synergy. Key findings include: (1) The multimodal classroom evidence-based system can enhance the precision of teaching diagnostics, but require edge computing and federated learning to safeguard data sovereignty; (2) Enhancements in teachers' teaching efficacy exhibit disciplinary heterogeneity: STEM disciplines rely on a closed-loop system of “teaching behaviors × student feedback” experimental data, while humanities disciplines depend on a dual-drive model of “teaching ethics × emotional interaction”; (3) Absent ethical governance poses the primary risk for deep AI application, necessitating a three-tier firewall comprising “data classification and grading + algorithmic ethics review + teacher digital rights catalog.” Based on these findings, the paper proposes an integrated teacher development model encompassing “AI tools—organizational support—humanistic reflection” and offers actionable policy recommendations for national resource repository development, discipline-differentiated training, and cross-border digital governance collaboration.