Towards Inclusive Retrieval-Augmented Generation: Challenges and Opportunities for Cognitively Impaired Users

Abstract

Current Information Retrieval (IR) systems, including conversational IR paradigms enhanced by Retrieval-Augmented Generation (RAG), assume that users can formulate coherent queries and reliably interpret retrieved information. In dementia care contexts, these assumptions fail: queries are unstable, feedback signals are unreliable, relevance fluctuates with cognitive state, and multi-user interaction is essential. Dementia impacts over 50 million people globally, yet current RAG research has yet to consider use cases where users have dementia or Mild Cognitive Impairment (MCI), and so RAG systems continue to be designed without considering people with dementia, MCI, or their caregivers as users, creating a fundamental mismatch between system capabilities and this population's needs. Our perspective outlines functional requirements, not optional features, for RAG systems serving cognitively diverse populations, including temporal user modelling that tracks cognitive trajectories over time, caregiver-in-the-loop retrieval that enables verification and oversight, consent-aware evidence access that respects fluctuating capacity, and cognitive-load-aware presentation that adapts complexity to comprehension level. These requirements would collectively transform RAG from a cognitively-stable user paradigm into a cognitively-adaptive one. Drawing on participatory workshops with 41 members of the public and dementia-care community, our perspective was shaped through empirical evidence from those with lived experience of dementia or MCI. We advocate for inclusive IR through our cognitively-adaptive RAG that supports cognitive independence in dementia care, and call on the IR community to advance accessibility and equity through inclusive retrieval system design.