Evidence map›Paper›PMID 42651542›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

Mapping the Distribution and Depth of Metacognitive Processes in Generative AI-Assisted Learning: Evidence from Interaction Logs and Concurrent Think-Aloud Protocols.

Shijin Li, Junjian Liu

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Shijin LiJing Hengyi School of Education, Hangzhou Normal University, No. 2318, Yu Hang Tang Road, Hangzhou 311121, China.ORCID 0000-0002-0974-399X
Junjian LiuJing Hengyi School of Education, Hangzhou Normal University, No. 2318, Yu Hang Tang Road, Hangzhou 311121, China.ORCID 0009-0006-1788-7404

Funding

Major Humanities and Social Sciences Research Projects in Zhejiang Higher Education Institutions 2024QN008Teaching Construction and Reform Project of Hangzhou Normal University JG2025124the Youth Fund Project of Humanities and Social Sciences of the Ministry of Education 25YJC880046Zhejiang Province Education Science Planning General Planning Project 2025SCG017
6 · The paper itself

Abstract

Generative artificial intelligence (GenAI) is increasingly used as a cognitive aid in education, yet no single process measure captures how learners monitor and control GenAI-assisted work. Guided by a reciprocal monitoring-control account of metacognition, this descriptive, exploratory study examined what interaction logs and concurrent think-aloud speech make observable during a 90 min instructional-design task. Seventy-nine undergraduates from 11 majors completed the task with DeepSeek support. The same two-dimensional rubric was applied separately to both records: each retained semantic unit received one primary operational category (planning, monitoring, or regulation) and one evidence-depth label (implicit, awareness, strategic, or reflective). Planning was treated as prospective control and regulation as adaptive control after evaluation or feedback. The interaction-log and think-aloud records yielded 274 and 193 retained instances, respectively. In both sources, regulation was the most frequent primary label and monitoring the least frequent, while evidence-depth ratings were concentrated at awareness and strategic. The combined strategic and reflective share was 25.4 percentage points higher in think-aloud than in interaction-log monitoring units and 9.9 points higher in think-aloud than in interaction-log planning units; for regulation, it was 14.0 points higher in interaction-log than in think-aloud units. These aggregate contrasts are descriptive because coded instances are nested within participants and the sources are paired within individuals. The central contribution is that verbalized monitoring information and enacted control leave different observable traces. Combining the sources therefore provides a fuller account of metacognitive activity than either record alone, without establishing monitoring accuracy, process effectiveness, or learning outcomes.

Indexed as

generative AIhuman–AI interactioninteraction logsmetacognitive processesmonitoring and controlthink-aloud protocol

Identifiers

PMID42651542
PMCPMC13509273

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.