NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910024081604608 |
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| author | Baradari, Dünya Kosmyna, Nataliya Petrov, Oscar Kaplun, Rebecah Maes, Pattie |
| author_facet | Baradari, Dünya Kosmyna, Nataliya Petrov, Oscar Kaplun, Rebecah Maes, Pattie |
| contents | Generative AI is transforming education by enabling personalized, on-demand learning experiences. However, current AI systems lack awareness of the learner's cognitive state, limiting their adaptability. Meanwhile, electroencephalography (EEG)-based neuroadaptive systems have shown promise in enhancing engagement through real-time physiological feedback. This paper presents NeuroChat, a neuroadaptive AI tutor that integrates real-time EEG-based engagement tracking with generative AI to adapt its responses. NeuroChat continuously monitors a learner's cognitive engagement and dynamically adjusts content complexity, tone, and response style in a closed-loop interaction. In a within-subjects study (n=24), NeuroChat significantly increased both EEG-measured and self-reported engagement compared to a non-adaptive chatbot. However, no significant differences in short-term learning outcomes were observed. These findings demonstrate the feasibility of real-time cognitive feedback in LLMs, highlighting new directions for adaptive learning, AI tutoring, and deeper personalization in human-AI interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07599 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences Baradari, Dünya Kosmyna, Nataliya Petrov, Oscar Kaplun, Rebecah Maes, Pattie Human-Computer Interaction Artificial Intelligence Emerging Technologies H.5.2; I.2.7; K.3.1; I.2.6 Generative AI is transforming education by enabling personalized, on-demand learning experiences. However, current AI systems lack awareness of the learner's cognitive state, limiting their adaptability. Meanwhile, electroencephalography (EEG)-based neuroadaptive systems have shown promise in enhancing engagement through real-time physiological feedback. This paper presents NeuroChat, a neuroadaptive AI tutor that integrates real-time EEG-based engagement tracking with generative AI to adapt its responses. NeuroChat continuously monitors a learner's cognitive engagement and dynamically adjusts content complexity, tone, and response style in a closed-loop interaction. In a within-subjects study (n=24), NeuroChat significantly increased both EEG-measured and self-reported engagement compared to a non-adaptive chatbot. However, no significant differences in short-term learning outcomes were observed. These findings demonstrate the feasibility of real-time cognitive feedback in LLMs, highlighting new directions for adaptive learning, AI tutoring, and deeper personalization in human-AI interaction. |
| title | NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences |
| topic | Human-Computer Interaction Artificial Intelligence Emerging Technologies H.5.2; I.2.7; K.3.1; I.2.6 |
| url | https://arxiv.org/abs/2503.07599 |