The increasing integration of artificial intelligence (AI) in educational decision-making raises a critical question: how to design AI systems that can effectively support teachers while maintaining an appropriate level of trust. Addressing this question requires not only continuous improvements in the technical capabilities of AI systems but also an examination from a human-AI interaction perspective of how different system designs influence users’ cognitive performance and affective responses, thereby providing guidance for system optimization and design. Therefore, this study conducted a randomized controlled experiment with 120 pre-service teachers to investigate how AI-assisted decision-making paradigms and AI explainability jointly influence teachers’ task performance and trust in AI, and whether these effects transfer to subsequent independent tasks. The results indicate that the effect of explanatory interface on task performance is context dependent and yields an immediate positive impact. Under the concurrent paradigm, the explanatory interface of the AI system significantly improves immediate task performance, whereas no significant effect is observed under the sequential paradigm. Moreover, this improvement is confined to the task execution stage and does not transfer to subsequent independent tasks. In contrast, the effect of explanatory interface on trust exhibits a delayed and negative pattern. The explanatory interface has no significant impact on situational trust, while it exerts a negative effect on learned trust and suppresses the natural development of both cognitive trust and emotional trust. In addition, different AI-assisted decision-making paradigms exhibit distinct patterns of influence on task performance and trust. Although the concurrent paradigm performs worse than the sequential paradigm in terms of immediate task performance, it is more effective in promoting users’ emotional trust. Overall, these findings extend the theoretical understanding of the mechanisms of explainability in human-AI interaction and provide empirical evidence for the joint design of explainable AI systems and human-AI collaboration paradigms.
Wang et al. (Fri,) studied this question.