The use of brain-computer interfaces in education: potential and risks
Keywords:
brain–computer interfaces, neuroeducation, neurorights, digital ethics, higher educationAbstract
Introduction: Higher education faces a convergence between neuroscience and artificial intelligence. In this framework, brain-computer interfaces (BCIs) promise personalization, real-time feedback, and inclusion, but they raise dilemmas about mental privacy and neurodata governance. Method: Quantitative, non-experimental and cross-sectional study with 80 participants (40 teachers, 40 students) from the areas of education, health and engineering. An Osgood semantic differential questionnaire (20 bipolar pairs; scale 1–7) was applied in four dimensions: educational potential, usability/adoption, pedagogical integration, and perceived risks. Descriptive statistics, Student's t and Pearson's correlation were used. Results: The means in potential, usability and integration were between 5.5 and 6.4, revealing high pedagogical evaluation. Risk perception was moderate (teachers 3.2; students 3.8). The student group showed slightly higher overall scores. A negative correlation was observed between educational potential and perceived risk, indicating that greater technological trust is associated with less ethical threat. Conclusions: BCIs are viable for adaptive learning and inclusion, but their adoption requires neurodigital governance, mental privacy safeguarding, consent protocols, and teacher training in neuroethics and neurodigital literacy. Clear institutional policies that articulate innovation and cognitive rights are recommended for responsible implementation in higher education.
References
Ban, S., Chong, D., Kwon, J., Lee, S., Huang, Y., Yoo, S., & Yeo, W.-H. (2026). Advances in flexible high-density microelectrode arrays for brain–computer interfaces. Biosensors and Bioelectronics, 292, 118102. https://doi.org/10.1016/j.bios.2025.118102
Boonstra, J. T. (2025). Ethical imperatives in the commercialization of brain–computer interfaces. IBRO Neuroscience Reports, 19, 718–724. https://doi.org/10.1016/j.ibneur.2025.10.004
Caiado, F., & Ukolov, A. (2025). The history, current state and future possibilities of the non-invasive brain computer interfaces. Medicine in Novel Technology and Devices, 25, 100353. https://doi.org/10.1016/j.medntd.2025.100353
Chen, J., Yin, H., Zhang, K., Ren, Y., & Zeng, H. (2025). Integration of neural networks in brain–computer interface applications: Research frontiers and trend analysis based on Python. Engineering Applications of Artificial Intelligence, 151, 110654. https://doi.org/10.1016/j.engappai.2025.110654
Deng, Q., Fu, Z., Ma, N., & Wang, B. (2025). Application and future directions of brain–computer interfaces in neurological disorders: Technological advances, clinical practices, and challenges. Brain Hemorrhages. https://doi.org/10.1016/j.hest.2025.09.002
Kapsetaki, M. E. (2026). Brain–computer interfaces for memory enhancement: Scientometric analysis and future directions. Biomedical Signal Processing and Control, 112, 108904. https://doi.org/10.1016/j.bspc.2025.108904
Kim, K.-T., Lee, J., & Lee, S. J. (2025). Convolutional neural network approach for motor imagery and steady-state somatosensory evoked potential-based hybrid brain–computer interface using dry electrodes. Biomedical Signal Processing and Control, 110, 108343. https://doi.org/10.1016/j.bspc.2025.108343
Latha, A. M., & Ramesh, R. (2025). A comprehensive review of AI-based brain–computer interface with prefrontal cortex and sensory–motor rhythms systemization for rehabilitation. Results in Engineering, 27, 106483. https://doi.org/10.1016/j.rineng.2025.106483
Lee, H., Shin, Y., Moon, H., Choi, Y., & Lee, A. (2025). Effects of digital interventions on neuroplasticity and brain function of individuals with developmental disabilities: A systematic review. Internet Interventions, 41, 100850. https://doi.org/10.1016/j.invent.2025.100850
Li, J., Zhang, W., Liao, Y., Qiu, Y., Zhu, Y., Zhang, X., & Wang, C. (2025). Neural decoding reliability: Breakthroughs and potential of brain–computer interfaces technologies in the treatment of neurological diseases. Physics of Life Reviews, 55, 1–40. https://doi.org/10.1016/j.plrev.2025.08.007
López Bernal, S., Quiles Pérez, M., Martínez Beltrán, E. T., Martínez Pérez, G., & Huertas Celdrán, A. (2025). When Brain–Computer Interfaces meet the metaverse: Landscape, demonstrator, trends, challenges, and concerns. Neurocomputing, 625, 129537. https://doi.org/10.1016/j.neucom.2025.129537
Maiseli, B., Abdalla, A. T., & Massawe, L. V. (2023). Brain–computer interface: Trend, challenges, and threats. Brain Informatics, 10, 20. https://doi.org/10.1186/s40708-023-00199-3
Mateen, N., Naeem, M., Khan, M. J., Yousaf, T., Ali, A., Altabey, W. A., Noori, M., & Kouritem, S. A. (2025). Subject based feature selection for hybrid brain computer interface using genetic algorithm and support vector machine. Results in Engineering, 27, 105649. https://doi.org/10.1016/j.rineng.2025.105649
Moreno-Calderón, S., Martínez-Cagigal, V., Martín-Fernández, A., Santamaría-Vázquez, E., & Hornero, R. (2025). Toward the integration of mixed reality and brain–computer interfaces based on code-modulated visual evoked potentials. Biocybernetics and Biomedical Engineering, 45(3), 528–538. https://doi.org/10.1016/j.bbe.2025.06.003
Ng, J. Y. (2025). Exploring the intersection of brain–computer interfaces and traditional, complementary, and integrative medicine. Integrative Medicine Research, 14(2), 101142. https://doi.org/10.1016/j.imr.2025.101142
Ognard, J., El Hajj, G., Verma, O., Ghozy, S., Kadirvel, R., Kallmes, D. F., & Brinjikji, W. (2025). Advances in endovascular brain computer interface: Systematic review and future implications. Journal of Neuroscience Methods, 420, 110471. https://doi.org/10.1016/j.jneumeth.2025.110471
Papanastasiou, G., Drigas, A., Skianis, C., & Lytras, M. (2020). Brain computer interface based applications for training and rehabilitation of students with neurodevelopmental disorders: A literature review. Heliyon, 6(9), e04250. https://doi.org/10.1016/j.heliyon.2020.e04250
Park, S., Ha, J., & Kim, L. (2025). Improving single-trial detection of error-related potentials by considering the effect of heartbeat-evoked potentials in a motor imagery-based brain–computer interface. Computers in Biology and Medicine, 195, 110563. https://doi.org/10.1016/j.compbiomed.2025.110563
Schippers, A., Berezutskaya, J., Vansteensel, M. J., Freudenburg, Z. V., Crone, N. E., & Ramsey, N. F. (2025). The effect of perceived auditory feedback on speech Brain–Computer Interface decoding performance. Clinical Neurophysiology, 180, 2111403. https://doi.org/10.1016/j.clinph.2025.2111403
Wireko Andrew Awuah, A., Ahluwalia, A., Darko, K., Sanker, V., Tan, J. K., Tenkorang, P. O., Ben-Jaafar, A., Ranganathan, S., Aderinto, N., Mehta, A., Shah, M. H., Chun, K. L. B., Abdul-Rahman, T., & Atallah, O. (2024). Bridging minds and machines: The recent advances of brain–computer interfaces in neurological and neurosurgical applications. World Neurosurgery, 189, 138–153. https://doi.org/10.1016/j.wneu.2024.05.104
Wu, B. (2025). A brain–computer-interface driven forearm exoskeleton with adaptive neuroregulation-based feedback for stroke rehabilitation. Alexandria Engineering Journal, 131, 199–208. https://doi.org/10.1016/j.aej.2025.09.069
Zhang, Y., Gao, Y., Zhou, J., Zhang, Z., Feng, M., & Liu, Y. (2025). Advances in brain–computer interface controlled functional electrical stimulation for upper limb recovery after stroke. Brain Research Bulletin, 226, 111354. https://doi.org/10.1016/j.brainresbull.2025.111354
Published
Issue
Section
License
Copyright (c) 2025 Wilson Ángel Gutiérrez Rodríguez, Graciela Mamani Torres, Javier Mamani Acarapi (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.