A Landscape Review of Artificial Intelligence (AI)-Enabled Teacher Professional Development at Scale

Teacher Professional Development (TPD) significantly influences student learning, yet implementing equitable, quality, and efficient TPD at scale remains a persistent challenge. Emerging technologies—particularly Artificial Intelligence (AI)—offer promising avenues for addressing tensions between localisation and personalisation, but a comprehensive understanding of AI’s role in scaling in-service TPD remains lacking. Guided by the overarching question of how AI enables in-service TPD to be implemented at scale with equity, quality, and efficiency, this landscape review examines three research questions: how AI-enabled TPD programmes address equity, quality, and efficiency at scale; what roles AI plays in enabling TPD at scale; and what promising practices and lessons emerge from such programmes. The review draws conceptually on the World Bank Group’s Technology for Teaching (T4T) framework, which delineates three sequential stages of technology-enhanced TPD: Access, Engage, and Apply. Sources included peer-reviewed studies, policy documents, and grey literature with a pre-defined typology. From an initial pool of 576 articles, 504 were screened after removing duplicates; 34 articles directly addressing the research questions underwent full-text review, supplemented by 59 partially aligned sources. Findings indicate that AI-enabled TPDs address equity, quality, and efficiency at scale by expanding equitable access for teachers of less mainstream K-12 subjects and geographically distant populations while supporting teacher retention by fostering meaningful learning and reducing administrative burden. Regarding roles that AI plays in enabling TPD at scale, AI’s principal affordances include expanding teachers’ knowledge base and learning assets, functioning as a personalised learning coach, and supporting classroom learning management beyond the TPD itself. Common practices that are tentatively effective emerged from the findings include AI chatbots, AI-assisted evaluation reports, and AI-enabled automation, with strong AI-capable leadership emerging as a potentially critical enabler. This review paper also provided practical implications for TPD practitioners, as well as directions for future research. The review’s primary limitations include the scarcity of global research on the topic, the threat of knowledge coloniality, and language constraints that affect the availability and validity of documentation.