Experimenting With Generative AI to Create Personalized Learning Experiences for Twice-Exceptional and Multi-Exceptional Neurodivergent Students

In many general education classrooms across the world, educators struggle to meet the educational needs of twice-exceptional and multi-exceptional neurodivergent learners, with their confluence of exceptional strengths and exceptional challenges. This article reports the process, findings, and implications of research that implemented a series of small-scale experiments conducted by two neurodivergent researchers and one neurotypical researcher in collaboration with generative artificial intelligence (GenAI) large language models (LLM). These experiments explored and developed human-centered, GenAI-informed approaches for teachers to rehearse pedagogy for these students. Using evidence-informed fictional, synthetic learner profiles (synthetic research participants), the researchers explored how GenAI could support educators in rehearsing pedagogy for multi-exceptional students through targeted prompting and design of personalized learning approaches. Findings reveal some potentially promising implications for GenAI application in generating adaptive, personalized educational approaches. The findings demonstrate the possibility for using GenAIs to understand and address the complex needs of these learners, offering potentially innovative solutions for educators. However, the study also identified challenges in ensuring consistent, context-appropriate, and unbiased outputs, underscoring the necessity of human oversight. The study contributes to the discourse on the potential applications of GenAI in the field of gifted education and advanced academics, advocating for a nuanced integration of expert knowledge and GenAI capabilities to meet the unique educational requirements of twice-exceptional and multi-exceptional students.