Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural equation modeling-based multi-group analysis

Abstract

Artificial intelligence (AI) integration in education has grown significantly recently. However, the potential risks of AI have led to educators being wary of implementing AI systems. To discover whether AI systems can be effective in the classroom in the future, it is critical to understand how risk factors (e.g., perceived safety risks, perceived privacy risks, and urban/rural differences) affect pre-service teachers’ AI acceptance. Therefore, the study aimed to (1) explore the influence of perceived risks and AI trust on pre-service teachers’ intentions to use AI-based educational applications, and (2) investigate possible variations in potential determinants of their intentions to use AI based on urban–rural differences. In this study, data from 483 pre-service teachers in China (262 from rural areas) were obtained by survey and analyzed using confirmatory factor analysis (CFA) and structural equation modeling-based multi-group analysis. The study’s findings demonstrated that while AI trust influenced pre-service teachers’ AI acceptance, the effect was less pronounced than perceived ease of use and perceived usefulness. Most notably, findings showed that perceived privacy and safety risks negatively influence AI trust among pre-service teachers from rural areas, which was a trend not observed in pre-service teachers from urban areas. As a result, to integrate AI-based applications into educational settings, pre-service teachers believed that the AI system must be functionally robust, user-friendly, and transparent. In addition, urban–rural differences considerably affect pre-service teachers’ AI acceptance. This study provides further relevant recommendations for educators and policymakers.