LLM-Mediated XAI Explanations: An AI Advisor for Fast and Calibrated Judgments on Potential Misinformation

Abstract

This paper introduces an LLM-mediated AI Advisor that contextualizes and synthesizes heterogeneous explainable AI (XAI) outputs to support fast and calibrated misinformation judgments in time-sensitive social media settings. We define LLM-mediated XAI as a process in which a large language model aggregates, prioritizes, and translates heterogeneous XAI outputs into a context-sensitive explanation tailored to the user's decision situation. Semantic features, XAI modules and LLM-based summarization and synthesis enable the generation of explanations that are adapted in three ways: compressed for time-efficient decisions, translated into non-technical language, and progressively expandable for deeper inspection. Through a mixed-methods user study, including a quantitative study and a qualitative study, we analyze how users interpret, challenge and strategically rely on LLM-mediated explanations during real-world misinformation assessment tasks. The findings indicate that the approach reduces time-to-decision and supports critical inspection without inducing over-reliance. Progressive disclosure and different techniques to present information favored different user needs while conversational functionality was rarely used due to unclear benefits and fear of confusion.