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

TL;DR

This paper investigates how heavy users integrate LLMs into everyday decision-making, extending beyond domain-specific applications. Using a qualitative approach with seven diverse participants, it maps patterns of use, underlying needs, and evolving mental models. The findings reveal that users engage LLMs for social validation, self-regulation, and interpersonal guidance, employing cognitive offloading and nuanced delegation strategies rather than simple outsourcing. The work highlights the need to rethink over-reliance metrics, incorporate richer contextual inputs, and consider generational differences in LLM interaction to inform future design and evaluation of AI-assisted decision-making.

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.