Brain-to-AI Adaptive Feedback Systems: The Next Frontier of Human–Machine Symbiosis
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Brain-to-AI adaptive feedback systems refer to a class of technologies in which neurophysiological signals from human brains are used in real time to adapt the behavior of artificial intelligence systems, creating closed‐loop feedback that can adjust according to the mental, emotional, or cognitive state of the user. These systems sit at the intersection of brain-computer interfaces (BCIs), neurofeedback, affective computing, adaptive learning, and AI, and promise to transform domains ranging from education and rehabilitation to human–machine collaboration and mental health. But with great promise come profound technical, ethical, and societal challenges: issues of signal fidelity and latency; interpretability and trust; individual variability; data privacy and autonomy; potential for bias and misuse. In this opinion piece I explore the potential benefits of brain-to-AI adaptive feedback systems, the key obstacles they face, and the governance, design, and value judgments that must guide their development if they are to enhance human well-being rather than undermine it.
##plugins.themes.bootstrap3.article.details##
Brain–Computer Interface, Adaptive Artificial Intelligence, Neurofeedback, Human–AI Interaction, Cognitive Augmentation
No funding source declared.
Angulo, J. A., Dapena, E., & Aguilar, J. (2024). Emotions as implicit feedback for adapting difficulty in tutoring systems based on reinforcement learning. Education and Information Technologies, 29(16), 21015. DOI: https://doi.org/10.1007/s10639-024-12699-8
Baradari, D., Kosmyna, N., Petrov, O. V., Kaplun, R., & Maes, P. (2025). NeuroChat: A neuroadaptive AI chatbot for customizing learning experiences. ACM Transactions on Interactive Intelligent Systems, 1, 1–29. DOI: https://doi.org/10.1145/3719160.3736623
Belwafi, K., & Ghaffari, F. (2024). Thought-controlled computer applications: A brain–computer interface system for severe disability support. Sensors, 24(20), 6759. DOI: https://doi.org/10.3390/s24206759
Jwa, A. S., & Poldrack, R. A. (2022). Addressing privacy risk in neuroscience data: From data protection to harm prevention. Journal of Law and the Biosciences, 9(2), lsac025. DOI: https://doi.org/10.1093/jlb/lsac025
Koelewijn, A. D., Audu, M. L., del-Ama, A. J., Colucci, A., Font-Llagunes, J. M., Gogeascoechea, A., Hnat, S. K., Makowski, N., Moreno, J. C., Nandor, M. J., Quinn, R. D., Reichenbach, M., Reyes, R.-D., Sartori, M., Soekadar, S. R., Triolo, R. J., Vermehren, M., Wenger, C., Yavuz, U. Ş., … Beckerle, P. (2021). Adaptation strategies for personalized gait neuroprosthetics. Frontiers in Neurorobotics, 15, 750519. DOI: https://doi.org/10.3389/fnbot.2021.750519
Kostas, D., & Rudzicz, F. (2020). Thinker invariance: Enabling deep neural networks for BCI across more people. Journal of Neural Engineering, 17(5), 056008. DOI: https://doi.org/10.1088/1741-2552/abb7a7
Laitinen, A., & Sahlgren, O. (2021). AI systems and respect for human autonomy. Frontiers in Artificial Intelligence, 4, 705164. DOI: https://doi.org/10.3389/frai.2021.705164
Valeriani, D., Santoro, F., & Ienca, M. (2022). The present and future of neural interfaces. Frontiers in Neurorobotics, 16, 953968. DOI: https://doi.org/10.3389/fnbot.2022.953968
Weld, D. S., & Bansal, G. (2019). The challenge of crafting intelligible intelligence. Communications of the ACM, 62(6), 70–79. DOI: https://doi.org/10.1145/3282486
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.