Sustainable AI Integration in Education: Factors Influencing Pre-service Teachers' Continuance Intention to Use Generative AI

This dataset examines pre-service teachers' continuance intention toward artificial intelligence (AI) in teaching. An integrated framework incorporating psychological (AI anxiety, AI self-efficacy), contextual (facilitating conditions, social influence), and perceptual factors (perceived ease of use, perceived usefulness) was tested using survey data from 549 Chinese pre-service teachers. Structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were employed. Results indicate AI self-efficacy as the strongest predictor of technology perceptions, with fsQCA identifying six configurational pathways across three patterns: intrinsic value driven, efficacy capability driven, and external support driven. The dataset supports replication research on sustainable AI integration in teacher education.