An enhanced innovation resistance theory to measure barriers of al-based chatbots usage among Chinese teacher trainees

AI-based chatbots have the potential to transform the educational landscape, offering significant advantages for teacher trainees by enhancing learning efficiency and engagement. Despite these benefits, resistance to AI-based chatbot adoption remains prevalent among teacher trainees, yet existing research on the barriers is still insufficient. The number of teacher trainees using AI-based chatbots remains quite limited. The Innovation Resistance Theory (IRT), while widely applied in technology resistance studies, has theoretical limitations, especially to address the issue of AI-based chatbot resistance among teacher trainees in China. To address these gaps, this study aims to develop an enhanced IRT model to measure barriers to AI-based chatbot adoption among teacher trainees in China. A quantitative research approach was employed. The collected data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among key resistance factors. The results indicate that Job Relevance Barrier (JRB), Technology Anxiety (TA), and Social Influence Barrier (SIB) are the most influencing factors of AI-based chatbot resistance among teacher trainees. Furthermore, higher resistance is observed when teacher trainees perceive AI-based chatbots as failing to meet their information quality expectations. Electronic Wordof- Mouth Barrier (E-WOMB), such as negative comments and terrible experiences, also significantly contribute to technological resistance. Nevertheless, the direct impact of the Usage Barrier (UB) is less obvious. Finally, the moderating analysis reveals that Distrust (DT) significantly influences the path strength between Image Barrier (IB) and Resistance to AI-based Chatbots (RTAC). This study contributes to theoretical and practical advancements by expanding the boundaries of IRT and addressing psychological and technological resistance factors. The findings provide valuable insights into mitigating barriers (e.g. privacy concerns) and promote the adoption of AI-based chatbots in teacher training