ArticleNPJ science of learning2025
Effects of different AI-driven Chatbot feedback on learning outcomes and brain activity.
Article in NPJ science of learning, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Bibliometric analysis of gamification in teacher education during the generative AI era: historical trends, present status, and future trajectories.F1000Research · 2026Pooled it
- Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth.Frontiers in psychology · 2026Pooled it
- How volunteer engagement experiences relate to the social responsibility of university students: the roles of emotional intelligence and relational embeddedness.Frontiers in psychology · 2026Article
- Neural mechanisms of video learning influenced by pedagogical agents' image and voice: an fNIRS study.Frontiers in psychology · 2026Article
- Generative AI in psychiatric education: balancing risks and benefits for responsible integration.Frontiers in psychiatry · 2026Article
- Warmth and competence in affective companion virtual humans: differential effects on behavioral intention via hedonic benefits.Frontiers in psychology · 2026Article
- The impact of AI precision feedback on college students' thinking shaping ability: mediating effect of intrinsic value identification and moderating role of critical consciousness transformation.Frontiers in psychology · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
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.