Personalising Learning for Gifted and Twice-Exceptional Students: Leveraging Generative Artificial Intelligence for Strengths-Based, Neuroaffirming Education

Twice-exceptional students—those who are both gifted and have one or more disabilities—and gifted learners, more broadly, represent persistently underserved populations within educational systems. Gifted learners frequently encounter provision that does not adequately engage their potential, such as standardised approaches that neither recognise nor respond to their learning requirements. Traditional identification and programming approaches often rely on deficit-based approaches that pathologise neurodivergence and frequently neglect the complex, asynchronous learning profiles characteristic of twice-exceptional students. This article advances a functional alignment framework proposing that generative artificial intelligence’s processing patterns may align with the cognitive characteristics of some gifted and twice-exceptional learners. The proposed functional alignment spans five dimensions: conceptual movement, knowledge integration, topic continuity, working memory, and pacing and temporal flexibility; this positions GenAI as a potentially compatible interactive platform for personalised, strengths-based learning. The functional alignment framework is explicitly theoretical, advancing propositions rather than demonstrated effects, and requires empirical validation. Positioning GenAI as a mediating platform has the potential to disrupt longstanding barriers to evidence-informed educational provision for gifted and twice-exceptional students. Through examining the intersection of gifted education, special education, and educational technology, this theoretical work outlines a trajectory for the field, characterised by flexible, personalised, strengths-based approaches that can be responsive to the student in front of the teacher, instead of the all-too-often default to one-size-fits-all approaches. Critical considerations of equity, teacher capability, and ethical implementation are addressed, theorising that GenAI’s transformative potential may only be realised through deliberate, theoretically informed application grounded in deep understanding of learner neurodivergence and a proposed pivot from GenAI literacy to GenAI fluency. This work contributes to reconceptualising gifted education as inherently inclusive, responsive, and oriented towards actualising potential for gifted and twice-/multi-exceptional learners.