NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences
This work addresses the need for more adaptive AI tutors in education by enabling real-time cognitive feedback, though it is incremental as it builds on existing neuroadaptive systems and generative AI.
The paper tackled the problem of AI tutors lacking real-time cognitive state assessment by developing NeuroChat, a neuroadaptive AI chatbot that integrates EEG-based engagement tracking with generative AI to dynamically adjust learning content. In a pilot study with 24 participants, NeuroChat enhanced cognitive and subjective engagement but did not show immediate effects on learning outcomes.
Generative AI is transforming education by enabling personalized, on-demand learning experiences. However, AI tutors lack the ability to assess a learner's cognitive state in real time, limiting their adaptability. Meanwhile, electroencephalography (EEG)-based neuroadaptive systems have successfully enhanced engagement by dynamically adjusting learning content. This paper presents NeuroChat, a proof-of-concept neuroadaptive AI tutor that integrates real-time EEG-based engagement tracking with generative AI. NeuroChat continuously monitors a learner's cognitive engagement and dynamically adjusts content complexity, response style, and pacing using a closed-loop system. We evaluate this approach in a pilot study (n=24), comparing NeuroChat to a standard LLM-based chatbot. Results indicate that NeuroChat enhances cognitive and subjective engagement but does not show an immediate effect on learning outcomes. These findings demonstrate the feasibility of real-time cognitive feedback in LLMs, highlighting new directions for adaptive learning, AI tutoring, and human-AI interaction.