Beyond Tools: Understanding How Heavy Users Integrate LLMs into Everyday Tasks and Decision-Making

Large language models (LLMs) are increasingly used for both everyday and specialized tasks. While HCI research focuses on domain-specific applications, little is known about how heavy users integrate LLMs into everyday decision-making. Through qualitative interviews with heavy LLM users (n=7) who employ these systems for both intuitive and analytical thinking tasks, our findings show that participants use LLMs for social validation, self-regulation, and interpersonal guidance, seeking to build self-confidence and optimize cognitive resources. These users viewed LLMs either as rational, consistent entities or average human decision-makers. Our findings suggest that heavy LLM users develop nuanced interaction patterns beyond simple delegation, highlighting the need to reconsider how we study LLM integration in decision-making processes.

Publication Details

Journal
ArXiv.org
Published
2025-02-21
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
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article

Beyond Tools: Understanding How Heavy Users Integrate LLMs into Everyday Tasks and Decision-Making

ArXiv.org
Artificial Intelligence in Healthcare and Education
article

Beyond Tools: Understanding How Heavy Users Integrate LLMs into Everyday Tasks and Decision-Making

article en

Abstract

Large language models (LLMs) are increasingly used for both everyday and specialized tasks. While HCI research focuses on domain-specific applications, little is known about how heavy users integrate LLMs into everyday decision-making. Through qualitative interviews with heavy LLM users (n=7) who employ these systems for both intuitive and analytical thinking tasks, our findings show that participants use LLMs for social validation, self-regulation, and interpersonal guidance, seeking to build self-confidence and optimize cognitive resources. These users viewed LLMs either as rational, consistent entities or average human decision-makers. Our findings suggest that heavy LLM users develop nuanced interaction patterns beyond simple delegation, highlighting the need to reconsider how we study LLM integration in decision-making processes.

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Artificial Intelligence in Healthcare and Education
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Beyond Tools: Understanding How Heavy Users Integrate LLMs into Everyday Tasks and Decision-Making · ArXiv.org (2025) | TGRS Research Map | TGRS