Assessing teachers’ AI Literacy for lifelong learning: A systematic review and framework alignment

Abstract

As AI tools spread across education and professional environments, AI literacy has become vital for teachers’ initial training, ongoing professional development, and continuous skills enhancement within the teaching workforce. This systematic review documents teacher-focused AI literacy and competency assessment methods and aligns them with UNESCO’s AI Competency Framework for Teachers (AI CFT) and the OECD-EC AILit Framework, making explicit the coverage and gaps in constructs for use in teacher education and lifelong learning systems. Searches in Web of Science™ following PRISMA 2020 resulted in 13 relevant studies and 16 distinct assessment methods (including self-report scales, objective tests, and qualitative approaches), with the landscape mainly dominated by self-assessments. Alignment analysis indicates frequent coverage of AI fundamentals and pedagogy, but significant gaps in human-centred mindset and AI for professional development within the AI CFT. The AILit Framework’s knowledge and skills are commonly evaluated, whereas attitudes are often reduced to or conflated with ethics. Notably, important theoretical aspects (such as attitudes and professional development) are sometimes omitted during factor analytic validation, revealing a tension between the framework’s scope and empirical fit that limits the diagnostic and evaluative usefulness of assessments for teacher development pathways. Common methodological weaknesses include limited cross sample revalidation, little evidence of test–retest reliability and measurement invariance, inconsistent sample size justifications, incomplete demographic reporting, and narrow language and geographic coverage, further complicated by terminological drift. The review offers a dual mapping to the AI CFT and AILit Framework, identifies key psychometric challenges, and proposes an agenda for framework-aligned, performance-based, and potentially AI-enhanced assessments to support teacher education, professional learning, and workforce development decision-making. Future efforts should retain human-centred mindset and professional development elements, combine self-report with performance and scenario tasks, develop cross-cultural adaptations and invariance, and explore AI and big-data–driven individualized assessments to improve comparability, relevance for credentialing, and policy utility across lifelong learning ecosystems.