Driving mechanisms of students’ continuance intention to use AIDTs in co-creation contexts: a multi-method analysis using SEM–ANN–NCA

With the continuous development of AIDTs, the relationship between students and AIDTs has shifted from one-way tool use to a more interactive and collaborative human–AI co-creation process. In co-creation contexts, the key factors influencing students’ continuance intention toward AIDTs, as well as the mechanisms through which these factors operate, remain insufficiently clarified. Based on this, the present study draws on the Motivation–Opportunity–Ability (MOA) framework and integrates Task–Technology Fit (TTF) and Information Systems Success (ISS) theories to develop a model of design students’ continuance intention toward AIDT-based co-creation. Using survey data collected from 371 Chinese university students, this study adopts a multi-method integration strategy and systematically validates the proposed model through CB-SEM, ANN, and NCA. The results indicate that, among the factors influencing students’ continuance intention in AIDT-based co-creation contexts, personal innovation and perceived utility exhibit particularly strong predictive effects. In addition, AI self-efficacy serves as a potential necessary condition for the development of a positive attitude, while attitude constitutes a necessary condition for students’ continuance intention. This study advances the understanding of human–AI co-creation in interactive learning environments and provides practical guidance for educators to foster more sustained, proactive, and high-quality human–AI co-creation.