Exploring Large Language Models Through a Neurodivergent Lens: Use, Challenges, Community-Driven Workarounds, and Concerns

TL;DR

This study explores how neurodivergent individuals use, perceive, and adapt to large language models, revealing diverse use cases, challenges, and community-driven solutions to improve inclusivity and address concerns.

Contribution

It provides the first qualitative analysis of neurodivergent communities' interactions with LLMs, identifying specific use cases, challenges, and adaptive strategies.

Findings

20 specific LLM use cases among neurodivergent users

Challenges include neurotypical responses and interaction limitations

Community-developed workarounds and concerns about overreliance

Abstract

Despite the increasing use of large language models (LLMs) in everyday life among neurodivergent individuals, our knowledge of how they engage with, and perceive LLMs remains limited. In this study, we investigate how neurodivergent individuals interact with LLMs by qualitatively analyzing topically related discussions from 61 neurodivergent communities on Reddit. Our findings reveal 20 specific LLM use cases across five core thematic areas of use among neurodivergent users: emotional well-being, mental health support, interpersonal communication, learning, and professional development and productivity. We also identified key challenges, including overly neurotypical LLM responses and the limitations of text-based interactions. In response to such challenges, some users actively seek advice by sharing input prompts and corresponding LLM responses. Others develop workarounds by experimenting and modifying prompts to be more neurodivergent-friendly. Despite these efforts, users have significant concerns around LLM use, including potential overreliance and fear of replacing human connections. Our analysis highlights the need to make LLMs more inclusive for neurodivergent users and implications around how LLM technologies can reinforce unintended consequences and behaviors.