With our understanding of neuroscience changing rapidly, it is challenging to predict where this technology will take us in Education. While many strides have been made in imaging the brain, there are still many challenges in understanding exactly how the brain processes information. In their work, Ansari et al. (2011) point to a peak and eventual loss in interest in the partnership between Education and Neuroscience due to the gap in practical applications of the research in teaching.  Ultimately, what researchers and educators would like to be able to achieve is to be able to understand causal relationships between learning environments and the brain (Gkintoni et al., 2025, p. 4). That way, tools and learning environments could be designed to improve efficacy in teaching practice. Despite the earlier hype and subsequent let-down, there is some renewed excitement lately due to advancements in mobile imaging technologies and artificial intelligence. In this section, we'd like to help you to imagine a world where we can impact learning in a practical way using technology and neuroscience. 

Cognitive Load Theory (CLT) posits that efficacious instructional design means that the learner feels challenged, without feeling overwhelmed. If the learning is too complex and demands too many cognitive resources, this will negatively affect the learner's ability to retain knowledge (Gkintoni et al., 2025, p. 3). There is some common ground between this theory and Educational Neuroscience. Both subscribe to the notion that understanding the brain to optimize cognitive processing can improve learning (Gkintoni et al., 2025). 

Next, explore the difference between intrinsic, extraneous, and germane load by watching the video (3 minutes).

A well understood element from CLT to teaching practice is that reducing extraneous cognitive load is important for learning. However, there is also a recognition in the research that managing cognitive load needs to be adaptive and individualized to each student to be fully effective (Gkintoni et al., 2025). In a practical sense, it can be challenging for teachers to individualize learning in a way where students maintain an optimal level of cognitive demand. There is some empirical proof that some neuroscience-based strategies do help with learning. For example, integrating new information with existing schemas improves knowledge retention and transfer (Gkintoni et al., 2025). However, in other cases, there is still more research that needs to be done to better understand the neural mechanisms to be able to generalize their findings (Gkintoni et al., 2025). 

In their systemic review of 103 studies involving ML, AI, Cognitive Load Theory, and Educational Neuroscience, Gkintoni et al. (2025) conclude that "...AI-driven interventions substanially affect knowledge retention, learner engagement, and cognitive efficiency across diverse educational settings" (p. 79). Therefore, we can conclude with a high degree of confidence that artificial intelligence holds a lot of promise for assisting in the application of neuroscience to teaching practice. Creating learning environments that can intuit learner cognitive load and behavioural changes and adapt the learning to the learner is incredibly promising. However, there are still many practical barriers to implementing these technologies into learning settings at scale. 

In the video (7 minutes), Dr. Alice Albrecht discusses future applications of AI, ML, and neuroscience through the use of wearables. 

It seems that we know this approach to learning is effective across many different cross-sections of learners. Why are all workplaces and schools not deploying these technologies then? While, we are not far away from achieving many of the benefits described here, Gkintoni et al. (2025) highlight the following issues related to widespread adoption of adaptive technologies that utilize neuroscience, understandings of CLT, AI, and ML :

• Many of the AI tools lack clear data privacy policies which raises concerns about surveillance and data security

• Students who come from underrepresented populations are especially at risk of having educational inequalities reinforced by biased AI system assessments.

•  More ethical frameworks are needed to address these ethical concerns

• The wide scale deployment of these systems requires high speed internet and relatively up-to-date devices, which creates equity issues in under-resourced schools.

• More long-term studies are needed to better understand the effects on learners socially, emotionally, and cognitively.