Magnetic Resonance Imaging (MRI) education often suffers from limited access to physical scanners and the complexity of MRI parameter interdependencies. This paper presents a web-based MRI simulator integrated with a knowledge-based AI assistant to enhance medical and radiography training to bridge the gap between theoretical learning and practical experience in MRI procedures. The simulator offers medical and radiography students an intelligent, interactive platform accessible via any web browser. The simulator enables interactive MRI parameter adjustments and real-time imaging feedback, while the AI assistant employs structured knowledge representation and rule-based reasoning to provide personalized recommendations on parameter optimization, artifact reduction, and protocol selection. The system was developed using modern web technologies including Node.js as a backend solution, JavaScript-driven frontend to scalable with smooth navigation and MongoDB for database. The system’s intelligent assistant leverages domain-specific ontologies and rule-based reasoning to deliver personalized recommendations on parameter optimization, artifact reduction, and protocol selection. The usability testing with 30 medical, system development students and instructors demonstrated high satisfaction, achieving average usability and learning effectiveness scores of 85.1% and 84.7%, respectively. These results indicate the system’s potential to bridge theoretical learning and practical skills in MRI education. Future work will expand anatomical coverage and integrate deep learning to further enhance the AI assistant’s adaptability.

This paper presents an intelligent digital concordance system powered by Natural Language Processing (NLP) to advance the study of Arabic scripts and support the development of faith-based intelligent applications. It addresses the limitations of traditional concordance methods by applying advanced computational techniques to analyze the linguistic and thematic structures of Arabic texts. A comprehensive NLP framework was developed, incorporating Arabic morphological analysis, semantic similarity detection, thematic clustering, and cross-referencing algorithms. The system processes the full Arabic corpus (comprising 6,236 verses across 114 chapters of the Quran) using transformer-based models fine-tuned for Classical Arabic, alongside traditional linguistic tools. The proposed framework enables automated indexing, semantic search, and bilingual alignment between Arabic and English texts. Experimental evaluations show strong results, with over 94.7% classification accuracy, 89.3% clustering precision, and a 92% effectiveness rating by domain experts. This research highlights the effective integration of modern NLP techniques for sacred text analysis. By combining linguistic integrity with computational intelligence, the framework offers a robust foundation for faith-aware AI systems and provides scalable, context-sensitive, and semantically rich access to religious knowledge which enhancing academic research and digital scholarship in Arabic script studies.