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

September 03, 2025 Β· Declared Dead Β· πŸ› Proc. ACM Hum. Comput. Interact.

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Authors Xinyue Chen, Kunlin Ruan, Kexin Phyllis Ju, Nathan Yap, Xu Wang arXiv ID 2509.03392 Category cs.HC: Human-Computer Interaction Citations 3 Venue Proc. ACM Hum. Comput. Interact. Last Checked 4 months ago
Abstract
As AI tools become increasingly embedded in cognitively demanding tasks such as note-taking, questions remain about whether they enhance or undermine 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.
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