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Cited by 29Personal Information Management and User BehaviorPerfectionism, Procrastination, Anxiety StudiesHuman-Automation Interaction and Safety

CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Models

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Arakawa, Riku, Yakura, Hiromu, Goto, Masataka·2023·Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3544548.3581133
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Abstract

CatAlyst uses generative models to help workers’ progress by influencing their task engagement instead of directly contributing to their task outputs. It prompts distracted workers to resume their tasks by generating a continuation of their work and presenting it as an intervention that is more context-aware than conventional (predetermined) feedback. The prompt can function by drawing their interest and lowering the hurdle for resumption even when the generated continuation is insufficient to substitute their work, while recent human-AI collaboration research aiming at work substitution depends on a stable high accuracy. This frees CatAlyst from domain-specific model-tuning and makes it applicable to various tasks. Our studies involving writing and slide-editing tasks demonstrated CatAlyst’s effectiveness in helping workers swiftly resume tasks with a lowered cognitive load. The results suggest a new form of human-AI collaboration where large generative models publicly available but imperfect for each individual domain can contribute to workers’ digital well-being.

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  • In the Academic context, reviewing support is delivered in qualitative form, such as suggestions or summaries.

    “Reviewing support is delivered in qualitative form, such as suggestions or summaries [7,114,122].”

    From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [7]

    The discussion of writing-support systems reports paragraphwise summaries that help writers gain an external perspective and polish their work. It does not establish that reviewing support is delivered this way specifically in an academic context; CatAlyst itself generates continuations of interrupted work rather than reviews. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Authors
Arakawa, R., Yakura, H., & Goto, M.
Affiliations
  • Carnegie Mellon University (US)
  • University of Tsukuba (JP)
  • National Institute of Advanced Industrial Science and Technology (JP)
ORCID
Venue
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2023-04-19
Pages
1-19
DOI
10.1145/3544548.3581133
OpenAlex
W4320838518
Cited by
29
References
80
Citations by year
2026: 5 · 2025: 14 · 2024: 9 · 2023: 1
FWCI
20.36
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Japan Science and Technology Agency (JPMJAX200R)
  • Japan Society for the Promotion of Science (JP21J20353)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
  • Social Sciences › Psychology › Clinical Psychology › Perfectionism, Procrastination, Anxiety Studies
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
Keywords
procrastination prevention, task resumption, human-AI collaboration, context-aware feedback, cognitive load, digital well-being, writing assignments

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