Too Long to Read? Restructuring LLM Responses for Users with Attention-Related Challenges

Abstract

Large language models (LLMs) are common tools for learning, research, and productivity, yet their dense outputs disproportionately burden users who experience attention, working memory, and information-processing challenges. We present Adapt AI, a system that restructures LLM responses through content condensation, visual hierarchy, and an original/adapted toggle, intervening at the presentation layer rather than the model itself. Through semi-structured interviews with ten participants screened for self-reported cognitive-processing difficulties, all ten reported the adapted view was easier to read, and seven of ten spontaneously described it as making AI-generated content more accessible. We treat these preference results as exploratory: the study used no comparison group and is subject to demand characteristics. Our findings show that the value of Adapt AI lies not in shortening responses but in reorganizing them to support scanning, comprehension, and user agency, and that the appropriate level of restructuring depends on task context. We discuss how designing the presentation layer of AI outputs offers an underexplored pathway to making LLM-based tools more inclusive.