NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences

• URL: http://arxiv.org/abs/2503.07599v1
• Date: Mon, 10 Mar 2025 17:57:20 GMT
• Title: NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences
• Authors: Dünya Baradari, Nataliya Kosmyna, Oscar Petrov, Rebecah Kaplun, Pattie Maes,
• Abstract summary: NeuroChat is a proof-of-concept neuroadaptive AI tutor that integrates real-time EEG-based engagement tracking with generative AI.<n>Results indicate that NeuroChat enhances cognitive and subjective engagement but does not show an immediate effect on learning outcomes.
• Score: 20.413397262021064
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning [59.35015431695172]
  We propose a novel biomimetic continual learning framework that integrates semi-parametric memory and the wake-sleep consolidation mechanism. For the first time, our method enables deep neural networks to retain high performance on novel tasks while maintaining prior knowledge in real-world challenging continual learning scenarios.
  arXiv  Detail & Related papers  (2025-04-20T19:53:13Z)
• Meta-Representational Predictive Coding: Biomimetic Self-Supervised Learning [51.22185316175418]
  We present a new form of predictive coding that we call meta-representational predictive coding (MPC) MPC sidesteps the need for learning a generative model of sensory input by learning to predict representations of sensory input across parallel streams.
  arXiv  Detail & Related papers  (2025-03-22T22:13:14Z)
• Rhythmic sharing: A bio-inspired paradigm for zero-shot adaptive learning in neural networks [0.0]
  The brain rapidly adapts to new contexts and learns from limited data, a coveted characteristic that artificial intelligence (AI) algorithms struggle to mimic.<n>We developed a learning paradigm utilizing link strength oscillations, where learning is associated with the coordination of these oscillations.<n>Link oscillations can rapidly change coordination, allowing the network to sense and adapt to subtle contextual changes without supervision.
  arXiv  Detail & Related papers  (2025-02-12T18:58:34Z)
• Shifting Attention to You: Personalized Brain-Inspired AI Models [3.0128071072792366]
  We introduce personalized brain-inspired modeling that integrates human behavioral embeddings and neural data to align with cognitive processes.<n>Fine-tuning on behavioral data enhances its ability to predict human similarity judgments while indirectly aligning it with dynamic representations captured via MEG.<n>We trained individualized models on participant-specific neural data, effectively capturing individualized neural dynamics.
  arXiv  Detail & Related papers  (2025-02-07T04:55:31Z)
• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfer [0.0]
  We suggest that environmental enrichment (EE) can be used as a biological model for studying forward transfer. EE refers to animal studies that enhance cognitive, social, motor, and sensory stimulation. We discuss how artificial neural networks (ANNs) can be used to predict neural changes after enriched experiences.
  arXiv  Detail & Related papers  (2024-05-12T14:33:50Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• NeuroCERIL: Robotic Imitation Learning via Hierarchical Cause-Effect Reasoning in Programmable Attractor Neural Networks [2.0646127669654826]
  We present NeuroCERIL, a brain-inspired neurocognitive architecture that uses a novel hypothetico-deductive reasoning procedure. We show that NeuroCERIL can learn various procedural skills in a simulated robotic imitation learning domain. We conclude that NeuroCERIL is a viable neural model of human-like imitation learning.
  arXiv  Detail & Related papers  (2022-11-11T19:56:11Z)
• Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment [52.07473934146584]
  We guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process. Our system is highly parallelizable, making it possible for a human to train large-scale reinforcement learning applications. It shows reinforcement learning performance can successfully adjust in sync with the human desired difficulty level.
  arXiv  Detail & Related papers  (2022-08-04T23:53:51Z)
• An Unsupervised STDP-based Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connections [10.188771327458651]
  Spike-timing-dependent plasticity (STDP) is a general learning rule in the brain, but spiking neural networks (SNNs) trained with STDP alone is inefficient and perform poorly. We design an adaptive synaptic filter and introduce the adaptive spiking threshold to enrich the representation ability of SNNs. Our model achieves the current state-of-the-art performance of unsupervised STDP-based SNNs in the MNIST and FashionMNIST datasets.
  arXiv  Detail & Related papers  (2022-07-06T14:53:32Z)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.