Perceived AI teaching integration and AI-related teaching innovative behavior among young and silver-aged faculty in Chinese universities: resource compensation, professional identity threat, and configurational evidence

Abstract

Introduction Artificial intelligence (AI) is increasingly embedded in university teaching, but the psychological processes through which faculty translate AI-related perceptions into AI-related teaching innovative behavior remain insufficiently understood. Drawing on the Job Demands–Resources model, this study examines AI-enabled teaching resource compensation and professional identity threat as competing pathways linking perceived AI teaching integration to AI-related teaching innovative behavior. It further compares young and silver-aged faculty and uses configurational analysis to complement the variable-centered model. Methods An anonymous online survey was administered through Wenjuanxing to university faculty in China. The analytical sample comprised young faculty aged 45 years or below and silver-aged faculty aged 60 years or above. Structural equation modeling tested direct and mediating relationships and multigroup differences. Fuzzy-set qualitative comparative analysis examined configurations associated with high AI-related teaching innovative behavior. Results Perceived AI teaching integration was positively associated with AI-related teaching innovative behavior. The resource-compensation pathway was the only statistically supported indirect pathway in the SEM model, whereas professional identity threat did not show a significant linear indirect association. Within the limits of a measurement model with modest absolute fit, the multigroup analysis did not provide evidence of significant between-cohort differences in the indirect effects. The exploratory fsQCA results suggested possible differences in the condition patterns associated with high AI-related teaching innovative behavior, but these patterns should be interpreted cautiously because the reported solution consistencies were moderate. Discussion These findings provide preliminary evidence that AI may be experienced as a teaching resource rather than as a direct professional identity threat in this sample. Universities may promote AI-supported teaching innovation by emphasizing pedagogical value, workload relief, and support conditions that are sensitive to faculty members' career-stage contexts.