Interactive XAI in AI-Augmented Decision-Making: A Persuasion Knowledge Perspective for Understanding the Effects of Interactive XAI on Appropriate Reliance

Artificial Intelligence (AI) systems are increasingly embedded in high-stakes human decision-making, yet achieving appropriate reliance remains elusive. Although Explainable AI (XAI) aims to address this, static explanations often fail to meet diverse user needs, while interactive natural-language explanations risk functioning as unintended persuasion agents by heightening perceptions of system humanness. Grounded in the Persuasion Knowledge Model (PKM), this study develops and tests a theoretical framework showing how explanation type (static versus interactive) influences perceived humanness, which shapes beliefs about the XAI agent’s assistive versus persuasive intent and ultimately affects reliance behavior. The hypotheses are tested in a between-subjects experiment (N=100) using a deception-detection task with hotel reviews, offering insights for the ethical design of conversational XAI that fosters complementary human-AI performance rather than over- or under-reliance.