Applying the Attributional Validity Framework to AI-Assisted Work for Gifted Identification, Selection, and Placement
Year: 2026
Type: preprint
Abstract: Work assisted by artificial intelligence (AI) may reveal reasoning relevant to gifted potential, yet product quality alone cannot establish what the learner contributed. This article applies the attributional validity framework to gifted identification, selection, and instructional placement through a five-stage assessment procedure. Decision criteria and an independent baseline guide structured learner–AI collaboration and reassessment of independent performance and developmental carryover. Evaluation keeps four inference targets distinct: independent competence, intellectual agency, hybrid capability, and developmental carryover. Optional retrospective AI self-evaluations would examine how learner input shaped the AI’s expressed reasoning and joint products. A fictional modeling example illustrates how independent confirmation can support admission and provisional advanced placement while gifted identification remains unresolved. Following preliminary evaluation of tasks and descriptive anchors, a proposed assessor study would compare targeted and fixed-sequence evidence requests under equal limits, using previously collected responses and an independent criterion of advanced-entry readiness. The procedure offers a testable basis for providing suitable challenge and support when applicable program requirements are met, while broader identification questions remain open.
Source: EdArXiv (OSF Preprints)
Author: UGUR SAK
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7225454204