Learning Behaviors Mediate the Effect of AI-powered Support for Metacognitive Calibration on Learning Outcomes

要旨

Students struggle with accurately assessing their own performance, especially given little training to do so. We propose an AI-powered training tool to help students improve “metacognitive calibration,” or the ability to accurately predict their own learning, potentially enhancing learning outcomes by enabling students’ use of metacognition-informed learning behaviors. We present results from a randomized controlled trial (N = 133) assessing the effectiveness of the tool in a college-level computer-based learning environment. The AI-driven tool significantly improved learning gains compared to the control group by 8.9% (t = -2.384, p = .019), and this effect was significantly mediated by learning behaviors. Overconfident students who received the intervention showed significantly greater metacognitive calibration improvement than the control group by 4.1% (t = 2.001, p = .049). These insights highlight the value of AI-powered metacognitive calibration training and the importance of promoting specific metacognition-informed learning behaviors in computer-based learning.

受賞
Honorable Mention
著者
HaeJin Lee
University of Illinois at Urbana Champaign, Champaign, Illinois, United States
Frank Stinar
University of Illinois Urbana-Champaign, Champaign, Illinois, United States
Ruohan Zong
University of Illinois Urbana-Champaign, Champaign, Illinois, United States
Hannah Valdiviejas
NA, Washington, District of Columbia, United States
Dong Wang
University of Illinois at Urbana-Champaign, Champaign, Illinois, United States
Nigel Bosch
University of Illinois at Urbana-Champaign, Champaign, Illinois, United States
DOI

10.1145/3706598.3713960

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713960

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: AI in the Classroom

G303
6 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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