From promises to practice : unraveling users’ mental models about the role and implications of large language models (LLMs) for individuals and society
- Author
- Khalid Mehmood (UGent) , Katrien Verleye (UGent) , Arne De Keyser and Bart Larivière (UGent)
- Organization
- Abstract
- The growing prevalence of large language models (LLMs), such as ChatGPT, calls for a deeper understanding of their role and implications for individuals and society. This study explores users’ mental models to understand the role and implications of LLMs for individuals and society. Through qualitative research using the critical incident technique (CIT), insights were gathered from 76 LLM users, resulting in 234 narratives about their interactions and experiences with LLMs. The findings reveal a nuanced ambivalence, with users comprehending LLMs as offering both significant opportunities and considerable risks. At the individual level, users’ mental models depict LLMs as helpful assistants, tools for personal connection, conditional partners, suspicious and disempowering actors, and dangerous opponents. At the societal level, users hold mental models that characterize LLMs as societal aids, conditional allies, disruptors, ethical hazards, threats to human sociability, and forces dividing society. This study identifies the mental models users hold regarding LLMs, providing valuable insights that can inform human-centered AI design and robust regulatory frameworks. By uncovering these mental models, this research lays the groundwork for balancing the opportunities and risks of LLMs. It contributes to the AI adoption and use literature by offering practical recommendations for designing and integrating AI-enabled services into everyday life in a way that aligns with users' mental models.
- Keywords
- Large Language Models (LLMs), mental models, Al-enabled services, societal impact, Critical Incident Technique (CIT)
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01KMT622960X57VXSHZT9BBT1M
- MLA
- Mehmood, Khalid, et al. “From Promises to Practice : Unraveling Users’ Mental Models about the Role and Implications of Large Language Models (LLMs) for Individuals and Society.” AIRSI 2025, Proceedings Book, AIRSI, 2025, pp. 153–57.
- APA
- Mehmood, K., Verleye, K., De Keyser, A., & Larivière, B. (2025). From promises to practice : unraveling users’ mental models about the role and implications of large language models (LLMs) for individuals and society. AIRSI 2025, Proceedings Book, 153–157. AIRSI.
- Chicago author-date
- Mehmood, Khalid, Katrien Verleye, Arne De Keyser, and Bart Larivière. 2025. “From Promises to Practice : Unraveling Users’ Mental Models about the Role and Implications of Large Language Models (LLMs) for Individuals and Society.” In AIRSI 2025, Proceedings Book, 153–57. AIRSI.
- Chicago author-date (all authors)
- Mehmood, Khalid, Katrien Verleye, Arne De Keyser, and Bart Larivière. 2025. “From Promises to Practice : Unraveling Users’ Mental Models about the Role and Implications of Large Language Models (LLMs) for Individuals and Society.” In AIRSI 2025, Proceedings Book, 153–157. AIRSI.
- Vancouver
- 1.Mehmood K, Verleye K, De Keyser A, Larivière B. From promises to practice : unraveling users’ mental models about the role and implications of large language models (LLMs) for individuals and society. In: AIRSI 2025, Proceedings Book. AIRSI; 2025. p. 153–7.
- IEEE
- [1]K. Mehmood, K. Verleye, A. De Keyser, and B. Larivière, “From promises to practice : unraveling users’ mental models about the role and implications of large language models (LLMs) for individuals and society,” in AIRSI 2025, Proceedings Book, Online, 2025, pp. 153–157.
@inproceedings{01KMT622960X57VXSHZT9BBT1M,
abstract = {{The growing prevalence of large language models (LLMs), such as ChatGPT, calls for a deeper understanding of their role and implications for individuals and society. This study explores users’ mental models to understand the role and implications of LLMs for individuals and society. Through qualitative research using the critical incident technique (CIT), insights were gathered from 76 LLM users, resulting in 234 narratives about their interactions and experiences with LLMs. The findings reveal a nuanced ambivalence, with users comprehending LLMs as offering both significant opportunities and considerable risks. At the individual level, users’ mental models depict LLMs as helpful assistants, tools for personal connection, conditional partners, suspicious and disempowering actors, and dangerous opponents. At the societal level, users hold mental models that characterize LLMs as societal aids, conditional allies, disruptors, ethical hazards, threats to human sociability, and forces dividing society. This study identifies the mental models users hold regarding LLMs, providing valuable insights that can inform human-centered AI design and robust regulatory frameworks. By uncovering these mental models, this research lays the groundwork for balancing the opportunities and risks of LLMs. It contributes to the AI adoption and use literature by offering practical recommendations for designing and integrating AI-enabled services into everyday life in a way that aligns with users' mental models.}},
author = {{Mehmood, Khalid and Verleye, Katrien and De Keyser, Arne and Larivière, Bart}},
booktitle = {{AIRSI 2025, Proceedings Book}},
keywords = {{Large Language Models (LLMs),mental models,Al-enabled services,societal impact,Critical Incident Technique (CIT)}},
language = {{eng}},
location = {{Online}},
pages = {{153--157}},
publisher = {{AIRSI}},
title = {{From promises to practice : unraveling users’ mental models about the role and implications of large language models (LLMs) for individuals and society}},
url = {{https://airsi.es/wp-content/uploads/2025/06/Proceedings_AIRSI25_key.pdf}},
year = {{2025}},
}