Effects of Interaction and Explanation Type on Human–AI Collaboration for Fake News Detection

AI-assisted decision-making systems are increasingly used across domains, prompting research into design factors that influence their effectiveness. Prior work has examined elements such as explanation type, interface design, and cognitive support interventions; however, the complexity of real-world tasks makes the generalization of findings difficult. In particular, in real-world decision-making settings likeFake News Detectionstudies on human-AI collaboration remain underexplored. This study investigates how explanation type and system interactivity affect human-AI collaboration in the context of fake news detection. In a controlled online experiment (N = 161), we varied the explanation type and the interactivity of an AI system that assists with the fake news detection task. Results show that conversational systems increased user reliance, while feature importance explanations appeared to be more effective than counterfactual explanations. These findings highlight the importance of explanation design and interaction modality in shaping effective AI-assisted decision-making in real world settings where underlying regularities are difficult for non-expert users to detect.