Artificial intelligence adoption in education: A systematic review of trends, theoretical models, and influencing factors (2021-2025)

Abstract

The integration of Artificial Intelligence (AI) in education is rapidly transforming teaching and learning practices; however, its adoption remains challenging and is influenced by multiple technological, behavioral, and contextual factors. This study conducts a systematic literature review (SLR) following the PRISMA 2020 framework to synthesize empirical research on AI adoption in educational settings. A total of 153 peer-reviewed journal articles published between 2021 and 2025 were analyzed to examine research trends, theoretical foundations, key influencing factors, applications, and emerging directions in the field. The findings reveal a substantial growth in AI adoption research, particularly after 2023, driven by the emergence of generative AI technologies such as ChatGPT. The Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) dominate the theoretical landscape, collectively underpinning the majority of studies. Core determinants of AI adoption include perceived usefulness, perceived ease of use, performance expectancy, trust, and behavioral intention, highlighting the continued reliance on established information systems (IS) frameworks. The results also show that research is heavily concentrated in higher education contexts, with students as the primary participants and a strong dominance of quantitative methodologies. In terms of applications, generative AI tools, particularly ChatGPT, represent the most widely studied technologies, reflecting a shift toward interactive and content-generating systems in education. Despite these advancements, significant gaps remain, including limited geographical diversity, underrepresentation of educators and institutional stakeholders, and insufficient focus on qualitative insights, ethical considerations, and long-term learning outcomes.