Abstract
Abstract
This paper proposes the Neuro–Pedagogical Alignment Model (NPAM), a meso-level theoretical framework integrating neuroscience and education. Drawing on recent findings in developmental, emotional, and cognitive neuroscience, NPAM explains learning effectiveness as the alignment between neural mechanisms, instructional design, and contextual conditions. The model identifies three core mechanisms—Match, Tuning, and Consolidation—linking prefrontal executive control, emotional regulation, and hippocampal memory processes to task structure, affective safety, and learning rhythm. Through a critical synthesis, the paper challenges the “pseudo-neuralization” of education, the empathic bias of teaching practice, and equity issues arising from neural diversity. NPAM reframes pedagogy from “what works” to “what aligns,” offering biologically feasible, ethically responsible, and practically testable principles for future educational design and research.
References
Adolphe, M., Pech, M., Sawayama, M., Maurel, D., Delmas, A., Oudeyer, P.-Y., & Sauzéon, H. (2025). Exploring the potential of artificial intelligence in individualized cognitive training: A systematic review. PLOS ONE, 20(6), e0316860. https://doi.org/10.1371/journal.pone.0316860
Arnsten, A. F. T. (2017). Stress weakens prefrontal networks: Molecular insults to higher cognition. Nature Neuroscience, 20(3), 333–343. https://doi.org/10.1038/nn.4476
Azevedo, R., Bouchet, F., Duffy, M., Harley, J., Taub, M., Trevors, G., … Cerezo, R. (2022). Lessons learned and future directions of MetaTutor: Leveraging multichannel data to scaffold self-regulated learning with an intelligent tutoring system. Frontiers in Psychology, 13, 813632. https://doi.org/10.3389/fpsyg.2022.813632
Azevedo, R., & Aleven, V. (Eds.). (2013). International handbook of metacognition and learning technologies. Springer. https://doi.org/10.1007/978-1-4419-5546-3
Best, J. R., & Miller, P. H. (2010). A developmental perspective on executive function. Child Development, 81(6), 1641–1660. https://doi.org/10.1111/j.1467-8624.2010.01499.x
Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences, 8(12), 539–546. https://doi.org/10.1016/j.tics.2004.10.003
Cavanagh, J. F., & Frank, M. J. (2014). Frontal theta as a mechanism for cognitive control. Trends in Cognitive Sciences, 18(8), 414–421. https://doi.org/10.1016/j.tics.2014.04.012
Chen, S., Cheng, H., & Huang, Y. (2024). Emotion recognition in self-regulated learning: Advancing metacognition through AI-assisted reflections. In D. Kourkoulou, A. O. Tzirides, B. Cope, & M. Kalantzis (Eds.), Trust and inclusion in AI-mediated education: Where human learning meets learning machines (pp. 185–212). Springer. https://doi.org/10.1007/978-3-031-64487-0_9
Craig, A. D. (2015). How do you feel? An interoceptive moment with your neurobiological self. Princeton University Press.
Crone, E. A., & Steinbeis, N. (2017). Neural perspectives on cognitive control development during childhood and adolescence. Trends in Cognitive Sciences, 21(3), 205–215. https://doi.org/10.1016/j.tics.2016.12.005
Dweck, C. S. (2017). From needs to goals and representations: Foundations for a unified theory of motivation, personality, and development. Psychological Review, 124(6), 689–719. https://doi.org/10.1037/rev0000082
Escolano-Pérez, E., & Losada, J. L. (2024). Using artificial intelligence in education: Decision tree learning based on cold and hot executive functions. Humanities and Social Sciences Communications, 11, 1563. https://doi.org/10.1057/s41599-024-04040-y
Farb, N. A. S., Segal, Z. V., & Anderson, A. K. (2015). Attentional modulation of primary interoceptive and exteroceptive cortices. Cerebral Cortex, 23(1), 114–126. https://doi.org/10.1093/cercor/bhr373
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain Sciences, 15(2), 203. https://doi.org/10.3390/brainsci15020203
Howard-Jones, P. A. (2014). Neuroscience and education: Myths and messages. Nature Reviews Neuroscience, 15(12), 817–824. https://doi.org/10.1038/nrn3817
Ienca, M., & Andorno, R. (2017). Towards new human rights in the age of neuroscience and neurotechnology. Life Sciences, Society and Policy, 13(1), 5. https://doi.org/10.1186/s40504-017-0050-1
Karmakar, S., & Das, S. (2024). The dynamic impact of neuroscience and artificial intelligence on education. In T. Singh, S. Dutta, S. Vyas, & Á. Rocha (Eds.), Explainable AI for education: Recent trends and challenges (pp. 229–246). Springer. https://doi.org/10.1007/978-3-031-72410-7_13
Liston, C., McEwen, B. S., & Casey, B. J. (2009). Psychosocial stress reversibly disrupts prefrontal processing and attentional control. Proceedings of the National Academy of Sciences, 106(3), 912–917. https://doi.org/10.1073/pnas.0807041106
Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.
McGaugh, J. L. (2018). Emotional arousal and enhanced memory: Neurobiological mechanisms. Annual Review of Neuroscience, 41, 1–21. https://doi.org/10.1146/annurev-neuro-080317-061400
Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function, 214(5–6), 655–667. https://doi.org/10.1007/s00429-010-0262-0
Murphy, J., Catmur, C., & Bird, G. (2017). Interoception and the prediction of bodily states. Cognitive Neuroscience, 8(1), 1–10. https://doi.org/10.1080/17588928.2016.1247300
Murty, V. P., & Adcock, R. A. (2017). Emotional modulation of memory encoding: Cognitive neuroscience mechanisms. Trends in Cognitive Sciences, 21(9), 725–737. https://doi.org/10.1016/j.tics.2017.06.002
Ngo, H. V. V., Staresina, B. P., & Born, J. (2019). Sleep spindles mediate hippocampal–neocortical coupling in memory consolidation. Neuron, 102(5), 1070–1085. https://doi.org/10.1016/j.neuron.2019.03.039
Nielsen, J. A., Zielinski, B. A., Ferguson, M. A., et al. (2013). An evaluation of the left-brain vs. right-brain hypothesis with resting-state data. PLOS ONE, 8(8), e71275. https://doi.org/10.1371/journal.pone.0071275
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x
Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2), 59–63. https://doi.org/10.1016/j.tics.2005.12.004
Sakai, K. (2020). Task set and prefrontal cortex. Annual Review of Neuroscience, 43, 145–164. https://doi.org/10.1146/annurev-neuro-101419-011429
Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences, 17(11), 565–573. https://doi.org/10.1016/j.tics.2013.09.007
Shenhav, A., Cohen, J. D., & Botvinick, M. M. (2017). Dorsal anterior cingulate cortex and the value of control. Nature Reviews Neuroscience, 18(10), 761–772. https://doi.org/10.1038/nrn.2017.115
Shiwlani, A., Hasan, S. U., & Kumar, S. (2024). Artificial intelligence in neuroeducation: A systematic review of AI applications aligned with neuroscience principles for optimizing learning strategies. Journal of Development and Social Sciences, 5(4), 578–593. https://doi.org/10.47205/jdss.2024(5-IV)50
Sirisha, N., Mageswari, P., Raj, V. M., Kumar, S., Priya, R. V., & Ananthi, S. (2025). Emotion centric artificial intelligence driven engagement systems for adaptive learning environments. In Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024) (pp. 528–540). Atlantis Press. https://doi.org/10.2991/978-94-6463-718-2_46
Swargiary, K. (2025). The impact of AI-driven personalized learning and intelligent tutoring systems on engagement and achievement. SSRN. https://doi.org/10.2139/ssrn.4897241
Tambini, A., & Davachi, L. (2019). Awake reactivation of prior experiences consolidates memories and biases cognition. Trends in Cognitive Sciences, 23(10), 876–890. https://doi.org/10.1016/j.tics.2019.07.007
Thomas, M., & Porayska-Pomsta, K. (2022). Neurocomputational methods: From models of brain and cognition to artificial intelligence in education. In O. Houdé & G. Borst (Eds.), The Cambridge handbook of cognitive development (pp. 662–687). Cambridge University Press. https://doi.org/10.1017/9781108399838.037
Wassum, K. M., & Izquierdo, A. (2015). The basolateral amygdala in reward learning and addiction. Neuroscience & Biobehavioral Reviews, 57, 271–283. https://doi.org/10.1016/j.neubiorev.2015.08.017
Williamson, B., Pykett, J., & Kotouza, D. (2025). Learning brains: Educational neuroscience, neurotechnology and neuropedagogy. Pedagogy, Culture & Society, 33(1), 1–20. https://doi.org/10.1080/14681366.2025.2438752
Xu, H., Pan, Y., Yin, J., & Hu, Y. (2025). Effects of AI-driven chatbot feedback on learning outcomes and brain activity. npj Science of Learning, 10(1), Article 17. https://doi.org/10.1038/s41539-025-00311-8
Yuan, X., Li, H., Feng, S. Y., & Sun, M. Y. (2025). Enhancing action recognition in educational settings through exercise-induced neuroplasticity. Frontiers in Neuroscience, 19. https://doi.org/10.3389/fnins.2025.1588570

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 HaoZhaohui (Author); Ha YougHo (Co-Authors)

