[2601.18033] An Experimental Comparison of Cognitive Forcing Functions for Execution Plans in AI-Assisted Writing: Effects On Trust, Overreliance, and Perceived Critical Thinking
Computer Science > Human-Computer Interaction
arXiv:2601.18033 (cs)
[Submitted on 25 Jan 2026]
Title:An Experimental Comparison of Cognitive Forcing Functions for Execution Plans in AI-Assisted Writing: Effects On Trust, Overreliance, and Perceived Critical Thinking
Authors:Ahana Ghosh, Advait Sarkar, Siân Lindley, Christian Poelitz
View a PDF of the paper titled An Experimental Comparison of Cognitive Forcing Functions for Execution Plans in AI-Assisted Writing: Effects On Trust, Overreliance, and Perceived Critical Thinking, by Ahana Ghosh and 3 other authors
Abstract:Generative AI (GenAI) tools improve productivity in knowledge workflows such as writing, but also risk overreliance and reduced critical thinking. Cognitive forcing functions (CFFs) mitigate these risks by requiring active engagement with AI output. As GenAI workflows grow more complex, systems increasingly present execution plans for user review. However, these plans are themselves AI-generated and prone to overreliance, and the effectiveness of applying CFFs to AI plans remains underexplored. We conduct a controlled experiment in which participants completed AI-assisted writing tasks while reviewing AI-generated plans under four CFF conditions: Assumption (argument analysis), WhatIf (hypothesis testing), Both, and a no-CFF control. A follow-up think-aloud and interview study qualitatively compared these conditions. Results show that the Assumption CFF most effectively reduced overreliance without increasing cognitive load, while participants perceived the WhatIf CFF as most helpful. These findings highlight the value of plan-focused CFFs for supporting critical reflection in GenAI-assisted knowledge work.
Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
Cite as: | arXiv:2601.18033 [cs.HC] (or arXiv:2601.18033v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2601.18033 Focus to learn more arXiv-issued DOI via DataCite
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From: Ahana Ghosh [view email] [v1] Sun, 25 Jan 2026 23:03:29 UTC (2,073 KB)
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