A Layered Digital Literacy Transformation Model through Coding and Artificial Intelligence at PKBM GEMA

Abstract

This study aimed to formulate a layered model of digital literacy development through coding and artificial intelligence (AI) learning in equivalency education. A descriptive qualitative study was conducted at PKBM GEMA, Tasikmalaya, from March to June 2026. Seven informants were purposively selected, comprising the PKBM head, an instructor, and school-age and working learners from Packages A, B, and C. Data were generated through semi-structured interviews, learning observations, and document review and analyzed using the interactive model of data condensation, data display, and conclusion drawing with verification. The analysis identified recurring patterns across learners’ experiences, learning practices, and learning products, which were organized into five interconnected layers: access and confidence, conceptual understanding, productive skills, critical and ethical practice, and application in learning, work, and entrepreneurship. Rather than representing fixed or strictly sequential stages, the layers describe an interconnected developmental pattern in which access and confidence provide an entry condition, conceptual understanding supports productive practice, productive skills create opportunities for critical and ethical use, and these competencies become meaningful when applied to real-life contexts. The observed development was supported by staged instruction, hands-on practice, projects, mentoring, peer support, and ethical reflection. However, limited devices, unstable internet connectivity, diverse initial competencies, restricted learning time, uneven tutor capacity, and incomplete contextual learning materials constrained continuity. The resulting model therefore provides a qualitative framework for understanding how coding and AI learning can be organized to support differentiated digital literacy development in equivalency education, while its effectiveness and long-term outcomes require further empirical testing.