AI POWERED PERSONALIZED LEARNING PATH GENERATOR

The AI-Powered Personalized Learning Path Generator successfully demonstrates how artificial intelligence can enhance traditional education by making learning more personalized, efficient, and engaging. 5.2 Strategic Summary and Impact Assessment The implementation of the AI-Powered Personalized Learning Path Generator signifies a transition from passive content consumption   to   active   cognitive optimization. By moving beyond the limitations of "Instructional Homogeneity," the framework establishes a new standard for data-driven academic support. 1.        Optimization of the Learning Trajectory The system’s core success lies in its ability to perform Inference-Based Path Correction. By synthesizing multi- dimensional data—including prior knowledge baselines and sensory modality preferences—the engine constructs an instructional roadmap that is uniquely calibrated to the individual. Frictionless Progression: The platform effectively eliminates the "cognitive gap" caused by uniform teaching speeds. High- velocity learners are challenged with advanced modules, while those facing conceptual hurdles receive immediate remedial scaffolding. This project is 30% Accuracy  

1.P. Brusilovsky (2001) – Presented foundational concepts of adaptive learning and personalized education systems, explaining methods for tailoring learning content based on user behavior and performance. Published in: User Modeling and User-Adapted Interaction Journal. 1.   J. Elen & R. E. Clark (2006) – Discussed the effectiveness of personalized learning environment and challenges of traditional instruction models. Published in: Educational Technology Research and Development. 2.   G. Siemens (2013) – Introduced the idea of learning analytics and the role of data in improving individual learning paths. Published by: EDUCAUSE Review. 3.  T. Mitchell (1997) This work presents fundamental concepts of machine learning and explains how intelligent algorithms learn from data. The book provides theoretical and practical foundations that support the development of recommendation systems used in personalized learning environments. Book: Machine Learning, McGraw- Hill. 4.  Y. LeCun, Y. Bengio & G. Hinton (2015) This publication discusses core principles of deep learning and highlights neural network techniques that form the basis of modern artificial intelligence systems.  These  methods  are essential for building adaptive learning models used in AI-driven educational platforms. Published in: Nature. ·        6. D. Koller & A. Ng (2012) – Explained the use of MOOCs and automated  recommendation algorithms in scalable online learning systems. ·        Coursera Founders – Research in Online Education. ·        7. R. Reilly & A. Affouneh (2018) – Examined the use of personalized e- learning systems and adaptive platforms in higher education settings.