Multi-Factor Configurational Paths and Practical Strategies for Influencing the Effectiveness of GenAI Teaching Applications——Based on the Analysis of 29 Typical Cases in Colleges and Universities

Under the dual impetus of technological change and policy guidance, the integration practice of generative artificial intelligence (GenAI) into university teaching has been widely implemented, and related research on GenAI teaching applications has been continuously deepened. However, most of the studies focus on individual case analyses and ignore the combined effects of multiple factors, resulting in insufficient guidance for teaching practice. Therefore, this paper took 29 typical cases from colleges and universities as research objects, set coding categories according to research questions, and designed an analytical framework for factors influencing the effectiveness of GenAI teaching applications. Relying on this analysis framework, this paper adopted the fuzzy-set qualitative comparative analysis (fsQCA) method to conduct a multifactor configurational pathway analysis of GenAI teaching applications. The results showed that no single conditional variable constituted a necessary condition for influencing the effectiveness of GenAI teaching applications. The configurational pathways for high effectiveness in GenAI teaching applications can be categorized into two types: the subject-technology synergy type and the subject-technology-environment balanced influence type, among which subject factors and technology factors jointly served as the key drivers for the effectiveness of high GenAI teaching applications. Based on this conclusion, the paper put forward practical strategies to enhance the effectiveness of GenAI teaching applications, with the aim of providing references for the deep integration of GenAI into teaching, and facilitating innovation of teaching modes reform.