TY - GEN
T1 - Inspo
T2 - 2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025
AU - Huang, Chieh Yang
AU - Gautam, Sanjana
AU - Brooks, Shannon Mc Clellan
AU - Lin, Ya Fang
AU - Knearem, Tiffany
AU - Huang, Ting Hao Kenneth
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/4/26
Y1 - 2025/4/26
N2 - The use of artificial intelligence (AI) to support creative writing has bloomed in recent years. However, it is less well understood how AI compares to on-demand human support. We explored how writers interact with both AI and crowd worker writing assistants in creative writing. We replicated the interface of the prior crowd-writing system, Heteroglossia, and developed Inspo, a text editor allowing users to request suggestions from AI models and crowd workers. In a one-week deployment study involving eight creative writers, we examined how often participants selected crowd workers when fluent AI text generators were also available. Findings showed a consistent decline in crowd worker usage, with participants favoring AI due to its faster responses and more consistent quality. We conclude by discussing how crowd-writing systems, in the large language model (LLM) era, can shift to fostering LLM-human collaboration.
AB - The use of artificial intelligence (AI) to support creative writing has bloomed in recent years. However, it is less well understood how AI compares to on-demand human support. We explored how writers interact with both AI and crowd worker writing assistants in creative writing. We replicated the interface of the prior crowd-writing system, Heteroglossia, and developed Inspo, a text editor allowing users to request suggestions from AI models and crowd workers. In a one-week deployment study involving eight creative writers, we examined how often participants selected crowd workers when fluent AI text generators were also available. Findings showed a consistent decline in crowd worker usage, with participants favoring AI due to its faster responses and more consistent quality. We conclude by discussing how crowd-writing systems, in the large language model (LLM) era, can shift to fostering LLM-human collaboration.
UR - https://www.scopus.com/pages/publications/105005790284
UR - https://www.scopus.com/pages/publications/105005790284#tab=citedBy
U2 - 10.1145/3706599.3720193
DO - 10.1145/3706599.3720193
M3 - Conference contribution
AN - SCOPUS:105005790284
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI EA 2025 - Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 26 April 2025 through 1 May 2025
ER -