Re-Evaluating AI Nativeness: Competence Foundations, GenAI Use/Access Profiles, and Institutional Mediation

Abstract

The notion of “AI natives” is increasingly used to describe young learners’ presumed ease with generative artificial intelligence (GenAI), but the label often turns age and exposure into proxies for competence. This study re-examines that assumption through an exploratory secondary analysis of two open student datasets and proposes a practice-based relational framework for AI nativeness. In this framework, AI nativeness is not a generational identity, but a testable configuration of competence foundations, GenAI use practices, access conditions, and institutional mediation. Because the two datasets do not measure all four dimensions within the same learners, the framework is motivated rather than fully tested here. The results show that basic operational skills do not, on their own, explain AI readiness; critical information literacy is the most consistent positive predictor in the regression models. Student GenAI use and access conditions are also heterogeneous, forming four interpretable GenAI use/access profiles: low-adoption learners, high-intensity multi-taskers, mobile-dependent moderate users, and balanced cognitive adopters. These findings do not establish who is or is not an AI native. They show why the age-based label is analytically insufficient and why future research should examine AI nativeness through competence, practice, access, and institutional mediation together.