Feasibility of a Multi-Dimensional AI System for Gifted Student Identification in Saudi Education

Abstract

Identifying 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.