The algorithmic classroom divide: language, infrastructure, and unequal access to generative AI in urban and rural Indonesian schools - Matsaini (CV Narasi Khatulistiwa Indonesia, 2026)
Background: Generative AI is entering Indonesian schooling amid uneven infrastructure, multilingual classroom realities, and differentiated teacher readiness across urban, rural, and remote educational settings. Objective: This study aims to examine how infrastructure, language, and pedagogical governance shape unequal access to generative AI in Indonesian schools. Method: Using a qualitative documentary and corpus-assisted discourse design, this study analyzed 24 public and verifiable items from policy documents, statistical reports, academic literature, intergovernmental guidance, and contextual digital-access sources published between 2023 and 2026. Results: Findings show that infrastructural access is framed primarily through territorial inequality, with urban–rural and remote-school readiness dominating coded evidence. Findings also indicate that linguistic access is concentrated in formal Bahasa Indonesia policy discourse, while multilingual and translanguaging dimensions receive limited explicit attention. Implication: Findings further reveal that governance, safeguards, and child protection are more visible than teacher training, curriculum enactment, and classroom-level mediation. Novelty: This study introduces the algorithmic classroom divide as a conceptual lens for understanding generative AI inequality as a convergence of material access, linguistic usability, and pedagogically governed participation.