Artificial Intelligence for Learning: Bridging Students’ Digital Competence and Literacy in Universities
Introduction: The rapid diffusion of artificial intelligence (AI) tools into higher education has reshaped how students search, process, and produce knowledge, yet universities in decentralized regions of Indonesia continue to debate whether these tools strengthen or erode the digital competence and digital literacy that underpin responsible learning. Objective: This conceptual paper examines how AI-mediated learning can be understood as a bridge, rather than a substitute, between students’ digital competence and their digital literacy, and proposes an integrative framework for higher education contexts such as South Sulawesi, Indonesia. Methodology: Adopting a narrative and conceptual review approach, the paper synthesizes peer-reviewed literature published between 2020 and 2026 on AI in learning, digital competence frameworks (including DigCompEdu and related models), and digital literacy scholarship, triangulating these strands with recent Indonesian empirical studies on AI adoption in higher education. Results: The synthesis indicates that AI functions as a bridging mechanism when its use is scaffolded by explicit competence-building and critical literacy practices, but functions as a displacement mechanism when adopted without pedagogical structure, producing convenience without comprehension. Four bridging pathways are identified: procedural scaffolding, critical verification, reflective self-regulation, and ethical co-authorship. Discussion and Conclusions: The paper argues that Indonesian public universities, illustrated through the higher education landscape of South Sulawesi, are well positioned to operationalize this bridge through curriculum-embedded AI literacy modules, lecturer digital-competence development, and institutional policy anchored in Pancasila values of responsible, collective knowledge use. The proposed AI-Bridged Digital Competence and Literacy (ABDCL) framework offers a conceptual tool for future empirical validation rather than a report of field data.