Exploring Large Language Models Through a Neurodivergent Lens: Use, Challenges, Community-Driven Workarounds, and Concerns
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917798054199296 |
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| author | Carik, Buse Ping, Kaike Ding, Xiaohan Rho, Eugenia H. |
| author_facet | Carik, Buse Ping, Kaike Ding, Xiaohan Rho, Eugenia H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Large Language Models Through a Neurodivergent Lens: Use, Challenges, Community-Driven Workarounds, and Concerns Carik, Buse Ping, Kaike Ding, Xiaohan Rho, Eugenia H. Human-Computer Interaction 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. |
| title | Exploring Large Language Models Through a Neurodivergent Lens: Use, Challenges, Community-Driven Workarounds, and Concerns |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.06336 |