AI Knows Best? The Paradox of Expertise, AI-Reliance, and Performance in Educational Tutoring Decision-Making Tasks
Eason Chen, Jeffrey Ji-Peng Li, Scarlett Huang, Xinyi Tang, Jionghao Lin, Paulo Carvalho, Kenneth R. Koedinger · 2026
We present an empirical study examining how experienced tutors (experts) and non-tutors (novices) evaluate the correctness of tutor praise responses under different AI-assisted decision-support interfaces and explanation styles. We examine human-AI reliance patterns by decomposing interaction errors into over-reliance (accepting incorrect AI suggestions) and under-reliance (rejecting correct AI suggestions), together with time cost as a process-level indicator. Across conditions, human-AI collaboration improved accuracy compared to humans working alone, but consistently underperformed an AI-only baseline, indicating that human judgment introduced additional errors even when assisted by a highly accurate model. Novices benefited more from AI support since they tend to follow AI suggestions, whereas experts frequently overrode correct AI advice, resulting in lower overall performance, revealing a paradox of expertise in educational decision-making. We further compare two explanation modalities: textual reasoning and inline highlighting. Textual reasoning reduced under-reliance when the AI was correct but increased over-reliance when the AI was wrong, while inline highlighting exerted minimal influence on either behavior. Notably, neither explanation modality improved accuracy, and both increased time costs. As a contribution to learning analytics, we demonstrate how reliance patterns (over-reliance and under-reliance) and time cost function as process-level indicators that reveal how users integrate, or fail to integrate, AI recommendations. Our findings underscore the need for adaptive, trust-calibrated explanation strategies in tutor-facing decision support systems that balance accuracy, efficiency, and accountability in human-AI collaboration.