Artificial Intelligence (AI) Adoption Among Teachers: A Systematic Review and Agenda for Future Research
Liangyong Xue, Norliza Ghazali, Jazihan Mahat
Abstract
The rapid integration of Artificial Intelligence (AI) in education has led to a surge in research on teachers' adoption of these technologies. However, there is a noticeable lack of comprehensive reviews synthesizing the current state of research on this topic. This study addresses this gap by systematically reviewing 33 studies published since 2015. Our analysis reveals a significant increase in research from 2019 to 2024, with Asian countries, especially China, leading in research output. The majority of these studies focus on in-service teachers and aim to enhance teaching practices. The Technology Acceptance Model (TAM) is the most commonly used theoretical framework, with performance expectancy and effort expectancy identified as key factors influencing AI adoption. Quantitative research methods dominate the current literature. Despite the progress, this review highlights several research gaps. There is a lack of qualitative studies, limited focus on pre-service teachers, and insufficient attention to AI adoption for professional development. Additionally, there is a need for education-specific acceptance models and a deeper exploration of factors unique to the educational context. By providing a comprehensive overview of the current research landscape, this study sets an agenda for future research.