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

Fuente: arXiv
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Main Authors: Kim, Eunhye, Choe, Kiroong, Yoo, Minju, Chowdhury, Sadat Shams, Seo, Jinwook
Format: Preprint
Published: 2025
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author Kim, Eunhye
Choe, Kiroong
Yoo, Minju
Chowdhury, Sadat Shams
Seo, Jinwook
author_facet Kim, Eunhye
Choe, Kiroong
Yoo, Minju
Chowdhury, Sadat Shams
Seo, Jinwook
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2502_15395
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Beyond Tools: Understanding How Heavy Users Integrate LLMs into Everyday Tasks and Decision-Making
Kim, Eunhye
Choe, Kiroong
Yoo, Minju
Chowdhury, Sadat Shams
Seo, Jinwook
Human-Computer Interaction
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.
title Beyond Tools: Understanding How Heavy Users Integrate LLMs into Everyday Tasks and Decision-Making
topic Human-Computer Interaction
url https://arxiv.org/abs/2502.15395