Abstract:

The growing integration of artificial intelligence （AI） into human workflows has given rise to a new paradigm of human–AI collaborative decision-making， in which human judgment and AI recommendations are combined to achieve superior performance. However， effective human–AI collaboration remains challenging because people often use AI advice inappropriately. Hence， understanding how to build effective human-AI teams has become a central question for both researchers and practitioners. Existing research has primarily approached this issue through the lens of trust calibration， seeking to improve collaboration by encouraging people to rely on AI systems to an appropriate degree. These approaches typically focus on subjective psychological factors， such as trust in AI， self-confidence， and perceived usefulness. While insightful， such affective factors are difficult to manage through organizational interventions， and therefore their practical value for AI deployment may be limited. Drawing on the knowledge-belief framework in cognitive science， we argue that two task-related factors—prior beliefs and task knowledge—play a fundamental role in shaping how individuals process and integrate external information， including AI advice. Unlike affective factors， these cognitive factors can be influenced through training and task design， making them particularly relevant for organizational practice. As such， the present study examines how prior beliefs and task knowledge influence human–AI collaboration. To investigate these relationships， we conducted a between-subjects experiment on academic performance prediction， in which prior bias was manipulated （biased vs. unbiased）， and task knowledge was measured as a continuous variable. The results yield three main findings. First， biased prior beliefs impair human-AI collaboration. This effect arises because individuals with biased priors are more likely to make false-positive errors， causing them to incorrectly reject valid AI recommendations. Second， greater task knowledge mitigates the negative effect of prior bias. As individuals have more task knowledge， they become better able to recognize and avoid false-positive errors induced by distorted prior beliefs. Third， the relationship between task knowledge and the effectiveness of collaboration follows an inverted U-shaped pattern. Collaboration improves as task knowledge increases， but only up to a certain point. Beyond that point， additional task knowledge reduces overall team performance. This decline is driven by an increase in false-negative errors： individuals with very high levels of task knowledge become more likely to accept incorrect AI recommendations. We argue that this effect reflects overconfidence， whereby highly knowledgeable decision-makers become less vigilant in monitoring AI outputs. Based on these findings， we provide practical guidance for the design， formation， and management of human-AI teams in organizational settings.

Key words: artificial intelligence, human-machine synergy, prior beliefs, task knowledge