Seeking Inspiration through Human-LLM Interaction

要旨

Large language model (LLM) systems have been shown to stimulate creative thinking among creators, yet empirical research on whether users can seek inspiration in their everyday lives through these technologies is lacking. This paper explores which attributes of LLMs influence inspiration-seeking processes. Focusing on use cases of travel, cooking, and self-care, we interviewed 20 participants as they explored scenarios of these use cases using LLMs. Thematic analysis revealed that the vast data of LLMs inspires users with unexpected ideas, many of which were highly personalized, and inspired participants towards being motivated to act. Participants were also sensitive to the deficiencies of LLMs, and noted how ethical issues associated with these technologies could negatively impact them applying inspirational ideas into practice. We discuss the behavioral patterns of users actively seeking inspiration via LLMs, and provide design opportunities for LLMs that make the inspiration-seeking process more human-centric.

著者
Xinrui Lin
Beijing Institute of Technology, Beijing, China
heyan huang
Beijing Institute of Technology, Beijing, China
Kaihuang Huang
OPPO, Shenzhen, China
Xin Shu
Newcastle University , NEWCASTLE UPON TYNE, United Kingdom
John Vines
University of Edinburgh, Edinburgh, United Kingdom
DOI

10.1145/3706598.3713259

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713259

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Co-ideation

G314+G315
7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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