Artificial Intelligence Driven Educational Transformation: A Systematic Literature Review of Learning Innovation, Teacher Professional Development and Digital Transformation in K–12 Education (2020–2026)

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in contemporary education, reshaping teaching, learning, assessment, and institutional management practices. The rapid advancement of generative AI technologies, intelligent tutoring systems, adaptive learning platforms, and learning analytics has accelerated digital transformation across educational settings, creating new opportunities for instructional innovation and educational improvement. Despite increasing adoption, evidence regarding the broader impact of AI on learning innovation, teacher professional development, and institutional transformation remains fragmented. This study aims to systematically synthesize current research on AI implementation in K–12 education through a Systematic Literature Review (SLR) guided by the PRISMA 2020 framework. Literature published between 2020 and 2026 was retrieved from Scopus, Web of Science, ScienceDirect, and SpringerLink databases using predefined inclusion and exclusion criteria. From 487 identified records, 42 studies met the eligibility requirements and were analyzed through thematic analysis. The findings reveal four dominant themes characterizing AI adoption in education: AI-supported personalized learning, AI-assisted assessment and feedback, teacher professional development and AI literacy, and AI-driven educational transformation. The review indicates that AI contributes significantly to individualized learning pathways, real-time feedback, learning analytics, instructional innovation, and data-informed decision-making. Teacher capacity emerged as the most influential factor affecting successful implementation, highlighting the importance of AI literacy, digital pedagogy, ethical awareness, and continuous professional development. The review also identifies major challenges, including teacher readiness, digital inequality, ethical concerns, data privacy, and policy gaps. Based on the synthesis, this study proposes the Artificial Intelligence Educational Transformation Framework (AI-ETF), which integrates governance, technological infrastructure, teacher capacity, and pedagogical innovation as key dimensions of sustainable AI implementation. The findings provide theoretical, practical, and policy implications for advancing responsible and effective AI integration in K–12 education.