{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:38:51Z","timestamp":1760060331968,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>With the proliferation of online learning platforms, selecting appropriate artificial intelligence (AI) courses has become increasingly complex for learners. This study proposes a novel hybrid AI course recommendation framework that integrates Term Frequency\u2013Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers (BERT) for robust textual feature extraction, enhanced by a Random Forest classifier to improve recommendation precision. A curated dataset of 2238 AI-related courses from Udemy was constructed through multi-session web scraping, followed by comprehensive data preprocessing. The system computes semantic and lexical similarity using cosine similarity and fuzzy matching to handle user input variations. Experimental results demonstrate a high recommendation accuracy = 91.25%, precision = 96.63%, and F1-Score = 90.77%. Compared with baseline models, the proposed framework significantly improves performance in cold-start scenarios and does not rely on historical user interactions. A Flask-based web application was developed for real-time deployment, offering instant, user-friendly recommendations. This work contributes a scalable and metadata-driven AI recommender architecture with practical deployment and promising generalization capabilities.<\/jats:p>","DOI":"10.3390\/computers14090353","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T07:43:16Z","timestamp":1756366996000},"page":"353","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Intelligent Hybrid AI Course Recommendation Framework Integrating BERT Embeddings and Random Forest Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4371-9993","authenticated-orcid":false,"given":"Armaneesa Naaman","family":"Hasoon","sequence":"first","affiliation":[{"name":"Computer Science Department, College of Computer Science and Mathematics, Tikrit University (TU), Tikrit 34001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2094-1142","authenticated-orcid":false,"given":"Salwa Khalid","family":"Abdulateef","sequence":"additional","affiliation":[{"name":"Computer Science Department, College of Computer Science and Mathematics, Tikrit University (TU), Tikrit 34001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5783-3357","authenticated-orcid":false,"given":"R. S.","family":"Abdulameer","sequence":"additional","affiliation":[{"name":"Computer Science Department, College of Computer Science and Mathematics, Tikrit University (TU), Tikrit 34001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6257-7874","authenticated-orcid":false,"given":"Moceheb Lazam","family":"Shuwandy","sequence":"additional","affiliation":[{"name":"Cybersecurity Department, College of Computer Science and Mathematics, Tikrit University (TU), Tikrit 34001, Iraq"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34019","DOI":"10.1109\/ACCESS.2024.3369901","article-title":"A digital recommendation system for personalized learning to enhance online education: A review","volume":"12","author":"Gm","year":"2024","journal-title":"IEEE Access"},{"key":"ref_2","first-page":"97","article-title":"Using Online IT-Industry Courses in Computer Sciences Specialists\u2019 Training","volume":"21","author":"Yurchenko","year":"2021","journal-title":"Int. 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