A Human-Centric Evaluation of a Retrieval-Augmented Generation System for Explaining Quebec Insurance Contracts

With the rise of online insurance sales, consumers face a significant enquoteadvice gap, requiring them to navigate complex legal contracts without expert guidance. This paper presents a human-centric, extrinsic evaluation of a state-of-the-art Retrieval-Augmented Generation system, designed to make Quebec automobile insurance contracts more understandable. Through a user study with 154 participants from Laval University, we assess the agent's real-world utility by measuring system satisfaction, cognitive effort, perceived autonomy, and risk. Our results show the system is perceived as a enquotecognitive equalizer, receiving high ratings for satisfaction, trust, and clarity. Crucially, users value the sense of autonomy the system provides even more than the knowledge itself, with this effect being most pronounced among participants with lower financial literacy, demonstrating how such an agent can directly empower individuals. However, our study also highlights some limitations: participants expressed a strong preference for human agents in high-stakes or emotionally charged scenarios. This work highlights the importance of human-in-the-loop frameworks in ensuring the responsible implementation of AI in high-stakes consumer finance.