Dispositional foundations for AI-mediated learning: evidence from a latent profile analysis of grade-8 students in ICILS 2023

Abstract

As generative artificial intelligence (GenAI) becomes increasingly integrated into education, understanding whether students possess the foundational conditions needed to benefit from AI-mediated learning has become an important educational challenge. This study conceptualizes dispositional foundations for AI-mediated learning as the configuration of ICT self-efficacy, attitudes toward technology and expectations of future ICT use that may support meaningful engagement with AI-mediated learning environments, and examines how these configurations relate to objectively assessed digital competence and to student characteristics. ICILS 2023 contains no measure of AI literacy or AI use, so these are candidate foundations rather than a direct measure of preparedness for AI. Using data from 118,764 Grade-8 students across 35 education systems participating in the International Computer and Information Literacy Study (ICILS 2023), we conducted sampling-weighted latent profile analyses on four internationally comparable indicators. Computer and information literacy (CIL) was examined as an external criterion using the BCH approach, and profile membership was predicted by socioeconomic background, gender, educational aspirations and ICT access. Four profiles emerged: average (55.9%), attitude-oriented (25.2%), low-disposition (11.0%) and efficacy-dominant (7.9%). Differences in CIL across profiles were modest, and students in the efficacy-dominant profile, who reported ICT self-efficacy more than two standard deviations above the mean, did not outperform the average profile in objectively assessed competence, a pattern we interpret as a confidence-competence mismatch rather than as high preparedness. On a common metric of average marginal effects, gender showed larger and more consistent contrasts than socioeconomic background. These findings suggest that the dispositional foundations for AI-mediated learning are distinct from computer and information literacy and should be understood as a multidimensional configuration of learner characteristics whose relevance to actual AI-mediated learning remains to be tested directly.