Beyond Identification: A Neurodiversity-Based Developmental Framework for Gifted Education in the Age of Artificial Intelligence

Gifted education is entering a decisive conceptual shift as artificial intelligence reshapes the meaning, measurement, and development of human potential. Traditional identification models, rooted in norm-referenced testing, fixed ability constructs, and categorical selection, are increasingly misaligned with an era in which AI may perform many algorithmic cognitive operations more efficiently than humans. This paper proposes a Neurodiversity-Based Developmental Profiling Framework that replaces static identification with dynamic mapping of cognitive profiles, intrinsic drive, and environmental fit. Drawing on contemporary neurodiversity research, the model interprets giftedness as a heterogeneous, often asynchronous pattern of strengths and sensitivities rather than a singular trait. It emphasizes intrinsic motivation, deep interest, and human-AI collaborative functioning as central drivers of talent in the emerging techno-cultural landscape. By integrating ecological assessment, profile-based analysis, and GenAI-supported developmental tools, the framework offers a future-oriented, inclusive, and context-responsive paradigm for gifted education in the age of AI.