AI attitudes, motivation and academic literacy: a PLS-SEM study of Indonesian EFL postgraduate students

Purpose The growing integration of artificial intelligence (AI) in higher education has raised questions about its role in shaping academic literacy, particularly among English as a Foreign Language (EFL) learners. This study examined how AI attitudes, motivation, genre knowledge, research competence and academic reading ability are interconnected among Indonesian postgraduate students. Design/methodology/approach A quantitative design was employed using partial least squares structural equation modelling and importance–performance map analysis (IPMA) to analyse data from 217 postgraduate students across five universities. This study integrates the technology acceptance model, self-determination theory and socio-cognitive perspectives on academic literacy. Findings AI attitudes significantly predicted motivation, which in turn influenced genre knowledge and research competence. Academic reading ability was primarily predicted by research competence, followed by genre knowledge and reading strategies. Mediation analysis confirmed that AI attitudes affect literacy indirectly through motivational and competence-based pathways. The IPMA results identified genre knowledge as a relatively underperforming yet important construct. Originality/value This study proposes a mediated capability model of AI engagement, demonstrating that AI contributes to academic literacy through the development of motivational and disciplinary competence. These findings have implications for curriculum design, pedagogical practice and AI integration in multilingual higher education contexts.