Adaptive NeuroPathway Model (NVA): A Neurobiologically-Based Proposal for Learning Design
DOI:
https://doi.org/10.62059/LatArXiv.preprints.588Keywords:
Neuroeducation, Synaptic plasticity, Deep learning, Flexible cognitionAbstract
Higher education is currently immersed in a technologically accelerated ecosystem characterized by artificial intelligence, intensive information flows and high cognitive demands, requiring flexible cognition and continuous adaptive learning. However, traditional teaching practices highly predictable and focused on content reproduction limit synaptic reorganization and the development of higher-order cognitive skills. This theoretical documentary study synthesizes recent neurobiological, cognitive and neuroeducational evidence (2019–2024) to propose the NeuroVía Adaptativa Model (NVA), a conceptual framework designed to promote deep learning in university students. Using an inductive content analysis approach and a systematic search in Web of Science, twelve articles were selected that address synaptic plasticity, predictive processing, cortical reorganization, cognitive load, and neuroeducation-informed teaching. The analysis reveals that deep learning emerges when students face moderated prediction errors, balanced cognitive load, contextual variability, and neuroinformed instructional regulation. The NVA articulates five key principles optimal microchallenge, cognitive load regulation, contextual diversification, neuroinformed scaffolding, and transfer-oriented evaluation offering a coherent theoretical basis for guiding pedagogical practices aligned with contemporary cognitive demands. The study concludes that activating continuous synaptic reorganization requires instructional designs that intentionally modulate effort, variability, and prediction error, positioning the teacher as a key regulator of cognitive and emotional engagement in higher education
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