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Artificial intelligence in gifted and talented education: a scoping review
Omar Abdullah Alsamani1, Yasir Ayed Alsamiri2
1Department of Special Education, College of Education, University of Ha'il, Hail, Saudi Arabia.
Introduction:
Artificial intelligence (AI) is being increasingly adopted across gifted and talented education. However, the emerging literature remains dispersed across instructional, developmental, assessment, and ethical domains.
Methods:
This scoping review maps how AI has been studied in gifted and talented education, including the forms of AI examined, the populations and contexts represented, the educational purposes addressed, and the opportunities, risks, and gaps reported across the literature. Following a scoping review approach informed by PRISMA-ScR and the Population-Concept-Context framework, database searches and screening procedures identified 26 eligible studies.
Results:
The review identified two broad trajectories. The first is instructional and developmental, where generative AI, AI tutoring, and AI-generated materials are used or proposed to support differentiation, writing, questioning, creativity, mentoring, and personalized learning. The second is assessment-oriented, where machine learning and related models are applied to gifted and talented identification, twice-exceptional identification, and decision support. Across both trajectories, the literature positions AI less as a replacement for educators than as a tool whose value depends on task design, teacher judgment, and human oversight. Reported opportunities include support for differentiated preparation, advanced learning, creative production, and the recognition of complex learner profiles. The reported risks include bias in identification data, overreliance, threats to originality, privacy concerns, limited transparency, and insufficient preparation among educators and institutions.
Discussion:
Overall, the evidence base on AI in gifted and talented education remains at an early stage. The review suggests that AI holds potential to advance gifted and talented education. Future research should move toward longitudinal, comparative, and equity-centered studies, particularly in AI-supported identification and twice-exceptional education.
