# "It's the only thing I can trust": Envisioning Large Language Model Use by Autistic Workers for Communication Assistance

arXiv: https://arxiv.org/abs/2403.03297 · HTML: https://mlsanity.com/paper/2403.03297/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2024.

## Built on by (most cited first, 11 of 11)

- [SocialMind: LLM-based Proactive AR Social Assistive System with Human-like Perception for In-situ Live Interactions](https://mlsanity.com/paper/2412.04036/) (2024)
- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation](https://mlsanity.com/paper/2502.02780/) (2025)
- [TwIPS: A Large Language Model Powered Texting Application to Simplify Conversational Nuances for Autistic Users](https://mlsanity.com/paper/2407.17760/) (2024)
- [Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMs](https://mlsanity.com/paper/2410.05684/) (2024)
- [The Role of Generative AI in Facilitating Social Interactions: A Scoping Review](https://mlsanity.com/paper/2506.10927/) (2025)
- [NeuroBridge: Using Generative AI to Bridge Cross-neurotype Communication Differences through Neurotypical Perspective-taking](https://mlsanity.com/paper/2509.23434/) (2025)
- [Chaplains' Reflections on the Design and Usage of AI for Conversational Care](https://mlsanity.com/paper/2602.04017/) (2026)
- [Exploring Implicit Perspectives on Autism in Large Language Models Through Multi-Agent Simulations](https://mlsanity.com/paper/2601.15437/) (2026)
- [How Frontier LLMs Adapt to Neurodivergence Context: A Measurement Framework for Surface vs. Structural Change in System-Prompted Responses](https://mlsanity.com/paper/2605.00113/) (2026)
- [Cripping AI: Reimagining AI Through Lived Disability Experiences](https://mlsanity.com/paper/2605.02080/) (2026)

## Abstract (arXiv)

Autistic adults often experience stigma and discrimination at work, leading them to seek social communication support from coworkers, friends, and family despite emotional risks. Large language models (LLMs) are increasingly considered an alternative. In this work, we investigate the phenomenon of LLM use by autistic adults at work and explore opportunities and risks of LLMs as a source of social communication advice. We asked 11 autistic participants to present questions about their own workplace-related social difficulties to (1) a GPT-4-based chatbot and (2) a disguised human confederate. Our evaluation shows that participants strongly preferred LLM over confederate interactions. However, a coach specializing in supporting autistic job-seekers raised concerns that the LLM was dispensing questionable advice. We highlight how this divergence in participant and practitioner attitudes reflects existing schisms in HCI on the relative privileging of end-user wants versus normative good and propose design considerations for LLMs to center autistic experiences.