Digital learning platforms are now expanding quickly while facing crucial problems with efficient personal resource delivery and retrieval to students. General-purpose search engines prove inadequate for academic purposes because they fail to match the necessary context-awareness and individual learning profiles. At the same time, current frameworks exhibit weakness in combining educational principles with modern search methods. An innovative web-based framework for educational search has been developed to use artificial intelligence (AI) alongside natural language processing (NLP) and machine learning (ML) in enhancing search relevance and improving user engagement as well as content credibility. The framework incorporated three main components: the query processing module, a personalization engine, and content validation defenses. This study evaluated over 10,000 educational resources alongside feedback from 500 testers. An evaluation yielded framework results demonstrating Precision at 92% and Recall operation reaching 88%, resulting in an F1-score measurement of 90% that surpassed baseline educational search methods. A predictive reliability assessment of recommendations manifests through 0.35 Mean Absolute Error (MAE) and 0.45 Root Mean Squared Error (RMSE), representing their accuracy levels. The user satisfaction rating was 4.7 out of 5, and users spent an average of 15.2 min on each session in the system, according to engagement metrics. The developed framework presents a flexible methodology that delivers tailored and efficient digital learning improvements by solving existing system problems. Research endeavors will expand to study multilingual resource integration and wider scalability while integrating emerging technologies for optimizing adaptive educational search functions.
Jian Li (2025) studied this question.