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More AI Assistance Reduces Cognitive Engagement: Examining the AI Assistance Dilemma in AI-Supported Note-Taking

Year: 2025

Type: article

Abstract: As AI tools become increasingly integrated into cognitively demanding tasks, like note-taking, questions remain about whether they enhance or compromise cognitive engagement. This paper examines the ''AI Assistance Dilemma'' in note-taking, investigating how varying levels of AI support affect user engagement and comprehension. In a within-subject experiment, we asked participants (N=30) to take notes during lecture videos under three conditions: Automated AI (high assistance with structured notes), Intermediate AI (moderate assistance with real-time summary, and Minimal AI (low assistance with transcript). Results reveal that Intermediate AI yields the highest post-test scores and Automated AI the lowest. Participants, however, preferred the automated setup due to its perceived ease of use and lower cognitive effort, suggesting a discrepancy between preferred convenience and cognitive benefits. Our study provides insights into designing AI assistance that preserves cognitive engagement, offering implications for designing moderate AI support in cognitive tasks.

Source: Proceedings of the ACM on Human-Computer Interaction

DOI: 10.1145/3757632

Authors: Xinyue Chen, Kunlin Ruan, Kexin Phyllis Ju, Nathan Yap, Xu Wang

Institution: University of Michigan

Language: English

Topic: Visual and Cognitive Learning Processes

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: hybrid

Funder: National Science Foundation

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