NEURAL INSIGHT EXTRACTION FRAMEWORK FOR PERSONALIZED COGNITIVE ASSESSMENT
DOI:
https://doi.org/10.29121/granthaalayah.v14.i1.2026.6566Keywords:
Cognitive Assessment, Neural Insight Extraction, NLP, Machine Learning, Adaptive Intelligence, Neural Networks, Offline AI, Personalized Learning, Cognitive ProfilingAbstract [English]
In the age of artificial intelligence, there is a growing need for smart, adaptive, and privacy-focused systems that can evaluate human cognition more accurately and personally. Traditional assessment methods depend on standardized tests and manual grading, often overlooking creativity, reasoning depth, and emotional understanding. The proposed Neural Insight Extraction Framework for Personalized Cognitive Assessment combines Natural Language Processing (NLP), Machine Learning (ML), and Neural Networks to analyze handwritten and digital responses in real time. Using Optical Recognition Systems (ORS), it evaluates cognitive domains such as comprehension, reasoning, memory, and analytical ability through lexical and contextual understanding. A key feature of the system is its adaptive intelligence, which adjusts question difficulty based on each user’s performance, providing a personalized cognitive profile. Unlike many existing AI tools, it operates fully offline—ensuring data privacy, security, and accessibility even in low-connectivity areas. Experimental results show a 94.7% accuracy in cognitive classification and a 97.5% correlation with established psychometric standards. The system also generates detailed analytical reports highlighting individual strengths and weaknesses, supporting educators and researchers in personalized training and evaluation. Future development will include multilingual support, LMS integration, and multimodal analysis (speech, emotion, and behavior) to deliver deeper insights into human cognition and learning.
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Copyright (c) 2026 Janhavi Vijay Asware, Dr. Ankita Karale, Naresh Thoutam
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