Teacher Workforce Challenges and the Rise of AI-Supported Professional Learning: A Systematic Review
Abstract
This systematic literature review synthesizes evidence from 46 peer-reviewed studies to examine teacher workforce challenges and the growing use of AI-supported professional learning as a potential solution. The review investigates four research questions: the nature of contemporary workforce challenges, the AI-supported tools and approaches available, the effectiveness of these systems, and the gaps, risks, and opportunities associated with their integration. A PRISMA-guided search and screening process was conducted across major academic databases. Studies meeting the inclusion criteria were analysed thematically to identify cross-study patterns, implementation mechanisms, and outcome-related insights. The results show that teachers continue to face substantial workload burdens, skill gaps, and limited access to sustained professional development. AI-supported learning tools, including intelligent tutoring systems, analytics dashboards, generative AI assistants, and personalized learning models, are increasingly used to address these challenges. Across studies, AI-supported professional development improves teachers’ AI literacy, instructional competence, confidence, and efficiency, while also supporting differentiated pedagogy and reducing administrative tasks. However, effectiveness varies widely and is influenced by infrastructural readiness, teacher attitudes, ethical preparedness, and institutional support. Major gaps and risks include limited empirical testing, ethical vulnerabilities, digital inequality, bias, and concerns about professionalization. Opportunities lie in structured AI literacy programs, ethical design frameworks, and scalable professional learning and development models that integrate pedagogy, technology, and ongoing support. The review concludes that AI-supported professional learning has strong potential to alleviate teacher workforce pressures and strengthen teaching capacity when implemented within an equitable, ethically grounded, and pedagogically aligned ecosystem. Further research is needed to evaluate long-term impacts, address ethical risks, and develop robust human–AI collaboration models for teacher professional growth.
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