As education adopts AI and adaptive technologies, their potential to augment learning raises ethical questions around privacy, bias, and transparency. We must develop ethical frameworks guiding their implementation, especially emerging neural interfaces directly interfacing with the brain that profoundly blurs technology and biology.
As education becomes increasingly digitized and augmented by technologies like artificial intelligence and big data analytics, there is tremendous potential to transform learning in revolutionary ways. However, these technologies also raise significant ethical questions around data privacy, bias, transparency, and the potential enhancement of human cognition. This presentation will explore the vision and promise of using AI and adaptive learning platforms to create personalized, engaging education experiences. It will also discuss emerging neural interfaces technologies, like brain-computer interfaces (BCIs), that could profoundly augment human learning and blur the lines between technology and biology (Mamun et al., 2021 & Zhang et al., 2020). Mapping the ethics frontier in these converging fields is crucial.
This presentation will articulate principles and guidelines for the ethical implementation of AI in education. It will also make the case for developing a strict ethical framework aligned with the use of digital technologies that directly interface with the human brain and nervous system. As these technologies become more integrated into how we teach and learn, we must consider their social impacts and build ethics into their design. This presentation aims to spur thought and discussion around this vital issue.
Strategies for interaction: Group Discussion and Framework Development Activity
Key Words: Brain-Computer Interfaces, Ethics, Human Cognition, Digital Education
Reference
Saha, S., Mamun, K. A., Ahmed, K., Mostafa, R., Naik, G. R., Darvishi, S., Khandoker, A. H., & Baumert, M. (2021). Progress in Brain-Computer Interface: Challenges and Opportunities. Frontiers in Systems Neuroscience, 15. https://doi.org/10.3389/fnsys.2021.578875
Zhang, X., Ma, Z., Zheng, H., Li, T., Chen, K., Wang, X., Liu, C., Xu, L., Wu, X., Lin, D., & Lin, H. (2020). The combination of brain-computer interfaces and artificial intelligence: applications and challenges. Annals of Translational Medicine, 8(11), 712–712. https://doi.org/10.21037/atm.2019.11.109