Work

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

Year: 2026

Type: article

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.

Source: Information

DOI: 10.3390/info17090857

Authors: Jun Peng, Weichen Jia, Nuan Wen, Ling Wang

Institutions: Ningbo University, Ningbo University of Technology, NingboTech University, City University of Macau

Language: English

Topic: Generational Differences and Trends

Subfield: Life-span and Life-course Studies

Field: Social Sciences

Domain: Social Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $1,930