{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T12:28:53Z","timestamp":1773577733290,"version":"3.50.1"},"reference-count":67,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Teacher performance evaluation is essential for improving instructional quality and guiding professional development, yet traditional observation-based methods can be subjective, labor-intensive, and inconsistently reliable. This study proposes an AI-powered framework to objectively assess classroom interactions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We developed and evaluated a computer-vision framework using three state-of-the-art object detectors\u2014YOLOv8, Faster R-CNN, and RetinaNet\u2014to identify eleven classroom interaction categories. A labeled dataset of 7,259 images collected from real classroom settings was annotated and used for training and evaluation. Performance was assessed using mean Average Precision (mAP).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>YOLOv8 achieved the best performance among the evaluated models, with an mAP of 85.8%, indicating strong accuracy in detecting diverse classroom interactions. Faster R-CNN and RetinaNet performed competitively but were outperformed by YOLOv8.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion\/Conclusion<\/jats:title><jats:p>The results demonstrate that modern deep learning\u2013based detection can provide more objective and reliable insights into teacher\u2013student interactions than traditional approaches. The proposed framework supports evidence-based evaluation and has the potential to enhance feedback and outcomes in educational practice.].<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2025.1553051","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T14:28:06Z","timestamp":1756909686000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["An AI-powered framework for assessing teacher performance in classroom interactions: a deep learning approach"],"prefix":"10.3389","volume":"8","author":[{"given":"Arwa","family":"Almubarak","sequence":"first","affiliation":[]},{"given":"Wadee","family":"Alhalabi","sequence":"additional","affiliation":[]},{"given":"Ibrahim","family":"Albidewi","sequence":"additional","affiliation":[]},{"given":"Eaman","family":"Alharbi","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"4107","DOI":"10.3390\/rs14164107","article-title":"IoT enabled deep learning based framework for multiple object detection in remote sensing images","volume":"14","author":"Ahmed","year":"2022","journal-title":"Remote Sens"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/s12065-022-00731-0","article-title":"Artificial intelligence techniques to predict the performance of teachers for kindergarten: Iraq as a case study","volume":"17","author":"Ali","year":"2024","journal-title":"Evol. 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