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Nested Learning in Higher Education: Integrating Generative AI, Neuroimaging, and Multimodal Deep Learning for a Sustainable and Innovative Ecosystem

Submitted:

07 December 2025

Posted:

09 December 2025

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Abstract
Industry 5.0 challenges higher education to integrate human-centred and sustainable uses of artificial intelligence, yet current deployments rarely connect generative AI, neuroadaptive sensing and governance in a single framework. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We present an exploratory two-phase mixed-methods study as an early empirical illustration of this proposal. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of self-regulated learning, emotional experience and ethical concerns, and semi-structured interviews. In the lab phase, P300 dynamics aligned with key instructional events, providing preliminary evidence that the neuro-adaptive pipeline is sensitive enough to justify larger-scale studies. In the field phase, 87% of students reported higher engagement and 73% perceived improved learning outcomes, while qualitative data highlighted greater clarity, adaptive support and cognitive safety, alongside concerns about privacy and data sovereignty. Perceived Nested Learning and neuro-adaptive adjustments were moderately associated with enhanced self-regulatory strategies (correlations up to r=0.57, p<0.001). We argue that, under robust ethical, data-protection and sustainability frameworks, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education.
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Subject: 
Social Sciences  -   Education
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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