Abuelmaatti, AishaAlahdal, Ashwaq2026-03-292026file:///C:/Users/ash20/Desktop/%D8%AF%D8%B1%D8%A7%D8%B3%D8%A9%20%D8%A7%D9%84%D8%AF%D9%83/%D8%A8%D8%AD%D8%AB%20%D8%A7%D9%84%D8%AA%D8%AE%D8%B1%D8%AC2025/%D8%A8%D8%AD%D8%AB%20%D8%A7%D9%84%D8%AA%D8%AE%D8%B1%D8%AC%20%D8%A7%D9%84%D8%AC%D8%AF%D9%8A%D8%AF%20%D9%85%D8%B9%20%D8%A7%D9%84%D8%AF%D9%83%D8%AA%D9%88%D8%B1%D8%A9%20%D8%B9%D8%A7%D8%A6%D8%B4%D8%A9/%D8%A7%D9%84%D9%83%D9%88%D8%AF%20%D8%A7%D9%84%D9%85%D8%B9%D8%AF%D9%84/%D8%A7%D9%84%D8%A8%D8%AD%D8%AB%20%D8%A8%D8%B9%D8%AF%20%D8%A7%D9%84%D8%AA%D8%B9%D8%AF%D9%8A%D9%84%20%D9%88%D8%B1%D9%81%D8%B9%D9%87/Dissertation_Paper_Template%202.pdfhttps://hdl.handle.net/20.500.14154/78530Identifying gifted students poses a considerable challenge in educational research, especially in contexts that require extensive data collection. This study introduces a data-driven method for identifying gifted students, employing machine learning models that integrate both simulated and actual datasets. A simulated dataset was developed to reflect the traits of gifted Saudi students, based on genuine academic patterns and educational research, covering academic, creative, and cultural dimensions. By utilizing a randomized classifier, gifted students were classified based on indicators from various disciplines. The model attained 96% predictive accuracy on the dataset examined and 98% on the global Cagle dataset. The findings revealed that academic and creative variables were the most significant predictors of giftedness. This research provides a practical framework for educational systems to identify gifted students in contexts where detailed data are limited, thereby enhancing equity and effectiveness in programs for gifted students. Keywords: Artificial intelligence in education, gifted students, machine learning, Random Forest classifier, classification, educational data analysis.14en-USArtificial intelligence in educationgifted studentsmachine learningRandom Forest classifierclassificationeducational data analysisThesis