Key constraining factors and breakthrough paths for the implementation of the five-element synergistic model in higher education: An empirical study based on mixed methods

Against the backdrop of AI-driven higher education transformation and the global agenda of Sustainable Development Goal 4 (SDG 4), the effective implementation of synergistic educational models has become a core task for achieving equitable and high-quality education. However, the practical promotion of the "Faculty-Student-AI-Environment-Culture" five-element synergistic model is hindered by multiple constraints, and existing research lacks systematic, evidence-based identification of these barriers and targeted solutions. Based on digital inequality theory, organizational change resistance theory, and ethical governance theory, this study adopted a sequential explanatory mixed-methods design to explore the key constraining factors of the model’s implementation and their impact mechanisms. A total of 431 valid questionnaires (including 93 faculty and 338 students) and 42 semi-structured interviews were collected from Chinese universities. Quantitative data were analyzed using standard multiple regression, moderation effect testing, and correlation analysis, while qualitative data were analyzed via thematic analysis. The results show that: (1) The Digital Divide (β=-0.371, p<0.001) and Faculty Adaptation Anxiety (β=-0.336, p<0.001) are the most critical constraining factors, followed by marginally significant Ethical Concerns (β=-0.184, p=0.054), while Institutional Barriers (β=-0.098, p=0.306) have no significant negative impact; (2) The Digital Divide significantly moderates the positive relationship between AI Synergy and teaching effectiveness (β=-0.097, p=0.021), weakening the model’s synergistic effect; (3) Qualitative analysis reveals that the Digital Divide manifests as a "resource-access-capability" triple gap, while Faculty Adaptation Anxiety stems from skill gaps, identity crisis, workload pressure, and incentive deficiencies; (4) Ethical Concerns mainly focus on data privacy, algorithmic bias, and the erosion of independent thinking. Based on these findings, a "three-dimensional breakthrough path" including resource balancing, capacity building, and ethical governance is proposed to address the key constraints. This study systematically identifies the core barriers to the model’s implementation, enriches the theoretical system of educational model implementation constraints, and provides evidence-based guidance for global higher education institutions to promote AI-integrated sustainable transformation, which is of great significance for advancing SDG 4 and Education for Sustainable Development (ESD).