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Self-Evolving Neuromorphic Systems for Predictive Learning Interventions
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Author(s): Hamed Nozari (Azad University UAE, Dubai, UAE)
Copyright: 2026
Pages: 18
Source title:
AI-Augmented Creativity in Learning Analytics
Source Author(s)/Editor(s): Stavroula Kalogeras (Heriot-Watt University, UAE), Sepideh Samadi (Heriot-Watt University, UAE)and Hamed Nozari (Independent Researcher, Australia)
DOI: 10.4018/979-8-3373-5117-9.ch006
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Abstract
This research presents a novel framework for learning analysis based on self-evolving neuromorphic systems that is capable of real-time prediction and intervention in the learning process. By combining impulse neural networks and evolutionary algorithms, the proposed model was able to significantly increase the accuracy of prediction and personalization of interventions. Simulation results showed that this approach has a more stable performance in detecting cognitive disorders and adjusting intervention compared to static models. This system can be used as a basis for the development of future intelligent educational tools.
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