Mapping In-Service Teacher AI Literacy: A Systematic Review of Empirical Studies

Abstract

AI literacy has become a core dimension of contemporary teacher professionalism, yet empirical evidence on how in-service teachers develop and enact this competence remains fragmented. This systematic review synthesizes 43 empirical studies published between 2020 and 2025 to examine how teacher AI literacy is conceptualized, developed, and supported in K-12 contexts. The review establishes conceptual boundaries between AI literacy, competence, readiness, and technology adoption. Findings indicate a rapidly expanding but theoretically fragmented field, with technology acceptance models and TPACK-based frameworks dominating. Teacher AI literacy is consistently associated with self-efficacy, attitudes, and contextual variables, while professional development programs vary widely, with sustained, practice-oriented approaches yielding stronger outcomes. The review proposes a three-layer integrative perspective, comprising individual competence, psychological mediation, and institutional context, for understanding how in-service teachers' AI literacy develops and functions within professional practice.