Research

The Use of Artificial Intelligence in the Advancement of Individualized Learning in Children

Grace Wellington


Introduction

The coincidence of neuroscience and AI has largely been commented on by cognitive neuroscientists as synergistic and mutually beneficial in aid of educational development and enrichment in children. Within the last century, the use of neuroscientific research and AI to meet governmental, educational standards, have been reported in current literature to increase awareness as an attempt to “fill the gap many deem a bridge too far” [1]. Neuroeducation proposes to transform how researchers deepen our understanding of the complex learning processes in children by utilizing neuroimaging techniques, and then implement findings into teacher-training and collaborations. Building the bridge is challenging. However, this research paper proposes ways in which neuroscientists and educators can enhance the experience of learning through learner-centered pedagogies designed by AI that motivate and engage children of all cultures and ages.

 

Background

Neuroeducation research sparked controversy in the late 1970s when advocating twenty components related to learning styles to demonstrate individual variation [2]; which criticized classrooms lacking insight into cognitive psychology and neuroscientific research. This led to a consensus amongst district teachers and spokespersons on how education combines art and science to increase awareness and innovation in developing neuroimaging techniques. [3] This proved the influence of brain research upon learning-processes: long-term memory is activated from the movement of blood to the brain [3]; emotions are an important tool to learning [2]. This research warrants a change in how current secondary schools continue to overload students (94% of education providers demand counseling services) despite lacking emotional support. [4] 

Conversely, current studies highlight the need for educational intervention for children with neurological disorders. By comparing brain activation of dyslexic children when reading before and after the intervention, researchers found some underactive areas and stimulated areas not typically associated with reading. Such studies identify how neuroscience can transform what we know surrounding cognitive processes in learning as well as establish new training strategies to benefit children [5]. Likewise, the role of machine-based learning has proven necessary when transforming education through the application of neuroimaging techniques to understand brain function in children. An MRI scanned one hundred and eighty-four children with learning difficulties for connectivity processing; AI established links between poor reading skills and sound processing that can lead to alternative educational methods [6].

 

Significance of Neuroeducation in Relation to AI

Without a clear understanding of a child's individual educational development (i.e. the science behind learning), children are potentially neglected due to nonspecific pedagogies as described, like “designing a glove with no sense of what a hand looks like” [7]. This concept was shown by analyzing cerebral activity, via EEG, of 8 students during visual and textual learning whereby all students responded uniquely, and 6 students required extra time when learning visually to textually [8]. This research suggests a need for integrative teaching over current pedagogies, especially when considering students of varying socio-economic backgrounds. However, an exercise in personalized teaching can prove both financially and realistically unobtainable without technology. Incorporating AI, large global data sets where children's behaviors are translated into algorithms, can help educators tailor their approach to a child's learning in an easily producible and time sufficient management.

 

Current Issues 

The fields of neuroscience and AI have differing perspectives and biases that perpetuate a disconnect; neuroscience is largely driven to identify disorders for potential treatments and so require“s” defining system components, yet AI produces solutions using algorithms [9]. To generate neural-inspired AI, researchers must tackle issues surrounding AI, i.e. data protection and enhancing diversity alongside incorporating AI into neuroscience practices. Additionally, educators lack insight into neuroscientific theories behind pedagogies, while neuroscientists remain unaware of educational practices today. The lack of communication between both disciplines fails to ask questions that govern how we can transform neuroeducation, leaving neuromyths and methodological limitations to prevail. Without exposure to each other's disciplines, it is futile to believe research can be possible in classroom-settings upon child participants. 

 

Research Direction

Neuroeducation focuses on building the bridge between educational and neuroscientific research. However, educators may benefit from an increased understanding of the principles surrounding cognitive neuroscience/learning through online courses, in turn, developing more efficient pedagogies. Similarly, neuroscientists may benefit from increased knowledge of neuroeducation as taught in the program, 'Mind, Brain and Education' at Harvard University, which stands as the only institution to offer courses on neuroeducation. Furthermore, the research could highlight possible benefits from educating children, specifically on matters of brain activity and other learning platforms. This could potentially empower individuals to adapt their learning style. Secondly, emerging AI as a tool for neuroeducation is crucial and requires awareness. If the problem of interdisciplinary communication is solved, then the only way researchers could tackle adopting new methodologies and research is through the global impacts of AI. Neuroscientists have boundaries when designing experiments, yet technology is limitless with financial assistance. Through integrating AI into neuroscience and educational programs, data can be more far-reaching to cultures. This will only happen if students are trained to work with new technologies and taught how AI could wreak benefits.

 

Conclusion

When considering a child's learning journey, it is found that 63% of students are disengaged and feel constrained due to the lack of personalized education [10]. The current climate of education is confined to a universal standardization of classroom-teaching due to a noticeable lack of communication of findings between neuroscientists and educators. However, AI has the potential to personalize every child's learning journey with e-learning platforms that provide the student with the right material at the right time; further assisting educators in adopting a more flexible role when teaching. To achieve this goal, further research into the coincidence of neuroscience with education and the execution of AI-assisted learning in classrooms is vital to bridge the gap [1].


References


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  9. Chance, F., Aimone, J., Musuvathy, S., Smith, M., Vineyard, C. and Wang, F. (2020). Crossing The Cleft: Communication Challenges Between Neuroscience And Artificial Intelligence. Frontiers. https://doi.org/10.3389/fncom.2020.00039. Retrieved: 04/06/2020.

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Grace Wellington

Grace Wellington


Hi! My name is Grace Wellington and I am currently studying a Masters in Neuroscience at the University College of London. Originally, I am from Cardiff, South Wales and grew up alongside two older brothers. I have a keen interest in a diversity of neuroscientific research such as neuroeducation and neurodegenerative disorders to which I regularly submit / collaborate on manuscripts for publication. Likewise, I volunteer for both Alzheimer's and Diabetes UK. In my spare time, I love to play the guitar, compete in running challenges and write music. As a mentor, I look forward to showing future neuroscientists just how exciting and rewarding the field is!