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Effects of different AI-driven Chatbot feedback on learning outcomes and brain activity

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Yin, Jiaqi, Xu, Haoxin, Pan, Yafeng, Hu, Yi·2025·npj Science of Learning
DOI 10.1038/s41539-025-00311-8PMC PMC12000334

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Abstract

Artificial intelligence (AI) driven chatbots provide instant feedback to support learning. Yet, the impacts of different feedback types on behavior and brain activation remain underexplored. We investigated how metacognitive, affective, and neutral feedback from an educational chatbot affected learning outcomes and brain activity using functional near-infrared spectroscopy. Students receiving metacognitive feedback showed higher transfer scores, greater metacognitive sensitivity, and increased brain activation in the frontopolar area and middle temporal gyrus compared to other feedback types. Such activation correlated with metacognitive sensitivity. Students receiving affective feedback showed better retention scores than those receiving neutral feedback, along with higher activation in the supramarginal gyrus. Students receiving neutral feedback exhibited higher activation in the dorsolateral prefrontal cortex than other feedback types. The machine learning model identified key brain regions that predicted transfer scores. These findings underscore the potential of diverse feedback types in enhancing learning via human-chatbot interaction, and provide neurophysiological signatures.

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  • Metacognitive development requires explicit scaffolding rather than emerging naturally from AI interaction.

    “Metacognitive development requires explicit scaffolding rather than emerging naturally from AI interaction (Bauer et al., 2025; Yin et al., 2025; Zhou et al., 2024).”

    From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Yin et al., 2025

    The Results (Behavioral results; page unavailable) report higher metacognitive sensitivity with metacognitive feedback than with affective or neutral chatbot feedback. The Discussion says the other feedback types lacked cues for reflection. This supports the value of explicit guidance in the study, but not the categorical claim that metacognitive development requires it or cannot emerge from AI interaction without it. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 7

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Authors
Yin, J., Xu, H., Pan, Y., & Hu, Y.
Affiliations
  • East China Normal University (CN)
  • Zhejiang University (CN)
Corresponding author
  • Pan, Yafeng
  • Hu, Yi
ORCID
Venue
npj Science of Learning
Publisher
Springer Science and Business Media LLC
Published
2025-04-15
Volume
10
Issue
1
Pages
17-17
ISSN
2056-7936 (ISSN-L)
DOI
10.1038/s41539-025-00311-8
PMC
PMC12000334
OpenAlex
W4409442981
Cited by
41
References
102
Citations by year
2026: 30 · 2025: 11
FWCI
16.45
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Science of Learning (publishedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China (71942001)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience, Education and Cognitive Function
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
Keywords
educational chatbots, feedback types, metacognitive feedback, emotional feedback, metacognitive sensitivity, transfer of learning, knowledge retention, brain activation, frontopolar cortex, middle temporal gyrus, supramarginal gyrus, dorsolateral prefrontal cortex, human-chatbot interaction

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