Productive vs. Reflective: How Different Ways of Integrating AI into Design Workflows Affect Cognition and Motivation

要旨

An increasing number of tools now integrate AI support, extending the ability of users—especially novices—to produce creative work. While AI could play various roles within such tools, less is known about how the positioning of AI affects an individual's cognitive processes and sense of agency. To examine this relationship, we built a collaborative whiteboard plugin that integrates an LLM into design templates to facilitate reflective brainstorming activities. We conducted a between-subjects experiment with N=47 participants assigned to one of three versions of AI-support—No-AI, AI input provided incrementally (Co-led) and AI provided all at once (AI-led)—to compare the allocation of cognitive resources. Results show that the positioning of AI scaffolds shifts the underlying cognition: AI-led participants devoted more time to comprehension and synthesis, which yielded more topically diverse problems and solutions. No-AI and Co-led participants spent more time revising content and reported higher confidence in their process.

著者
Tone Xiaotong. Xu
University of California, San Diego, La Jolla, California, United States
Arina Konnova
University of California, San Diego, La Jolla, California, United States
Bianca Gao
University of California, San Diego, La Jolla, California, United States
Cindy Peng
University of California, San Diego, La Jolla, California, United States
Dave Vo
University of California, San Diego, La Jolla, California, United States
Steven P.. Dow
University of California, San Diego, La Jolla, California, United States
DOI

10.1145/3706598.3713649

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713649

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: AI-Assisted Creativity

Annex Hall F203
7 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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