Inferring Gifted Potential from AI-Assisted Work: An Attributional Validity Framework for Gifted Education
Abstract
Generative artificial intelligence (AI) complicates a basic inference in gifted education: whether an exceptional product created with AI warrants a conclusion about the learner’s exceptional potential. An AI-assisted product may arise from different combinations of learner competence, AI capability, learner direction and evaluation, and human–AI fit; therefore, product quality does not transparently reveal the learner’s contribution or competence. This conceptual article frames the problem as one of attributional validity when human–AI co-produced work informs judgments of gifted potential and educational need. The framework distinguishes four targets of inference: independent competence, intellectual agency, hybrid capability, and developmental carryover. It then shows how AI-assisted work can lead educators to attribute collaborative success to the learner alone, mistake supported performance for durable learning, or overlook meaningful differences among learners. On this basis, the article offers principles for judging AI-assisted accomplishment according to how the work was produced, what the learner can later demonstrate independently, and which capability a gifted program seeks to identify or develop.
