Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Making

要旨

Our study of 20 knowledge workers revealed a common challenge: the difficulty of synthesizing unstructured information scattered across multiple platforms to make informed decisions. Drawing on their vision of an ideal knowledge synthesis tool, we developed Yodeai, an AI-enabled system, to explore both the opportunities and limitations of AI in knowledge work. Through a user study with 16 product managers, we identified three key requirements for Generative AI in knowledge work: adaptable user control, transparent collaboration mechanisms, and the ability to integrate background knowledge with external information. However, we also found significant limitations, including overreliance on AI, user isolation, and contextual factors outside the AI's reach. As AI tools become increasingly prevalent in professional settings, we propose design principles that emphasize adaptability to diverse workflows, accountability in personal and collaborative contexts, and context-aware interoperability to guide the development of human-centered AI systems for product managers and knowledge workers.

受賞
Honorable Mention
著者
Bhada Yun
University of California, Berkeley, Berkeley, California, United States
Dana Feng
University of California, Berkeley, Berkeley, California, United States
Ace S.. Chen
University of California Berkeley, Berkeley, California, United States
Afshin Nikzad
University of Southern California, Los Angeles, California, United States
Niloufar Salehi
UC, Berkeley, Berkeley, California, United States
DOI

10.1145/3706598.3713337

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713337

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: Knowledge Work

G416+G417
6 件の発表
2025-04-30 23:10:00
2025-05-01 00:40:00
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