mlsanityData to 7 Oct 2026

NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences

arXiv 2503.07599 ↗First posted 10 Mar 2025cs.HC cs.AI cs.ETcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 9 of its 104 references that are in the map. First posted on arXiv 10 Mar 2025.

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9 of 104 references.

References · 9

  1. 2024NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signalscited by 83
  2. 2023Neuro-GPT: Towards A Foundation Model for EEGcited by 69
  3. 2024EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretationcited by 26
  4. 2024Exploring Large-Scale Language Models to Evaluate EEG-Based Multimodal Data for Mental Healthcited by 13
  5. 2024EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record Generationcited by 6
  6. 2024Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleepcited by 4
  7. 2024Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audiocited by 3
  8. 2018Breeze: Sharing Biofeedback Through Wearable Technologiescited by 2
  9. 2025AdaptiveCoPilot: Design and Testing of a NeuroAdaptive LLM Cockpit Guidance System in both Novice and Expert Pilotscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 7.

11 papers.

  1. 2025Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologiescited by 5
  2. 2025Neural-Driven Image Editingcited by 4
  3. 2025Foundation Models for Cross-Domain EEG Analysis Application: A Surveycited by 4
  4. 2026NeuroSkill(tm): Proactive Real-Time Agentic System Capable of Modeling Human State of Mindcited by 2
  5. 2026BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understandingcited by 2
  6. 2025Detecting Reading-Induced Confusion Using EEG and Eye Trackingcited by 0
  7. 2026Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligencecited by 0
  8. 2026Multi-Modal Multi-Agent Robotic Cognitive Alignment enabled by Non-Invasive Consumer Brain Computer Interfaces: A Proof of Concept Explorationcited by 0
  9. 2026BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understandingcited by 0
  10. 2026Socrates went Nuclear: Comparing Interaction Strategies for AI systems in a Learning Context using Brain Sensingcited by 0
  11. 2026Gricea: An Open Science Platform for Conversational AI Researchcited by 0