Factors influencing the adoption of generative AI in education
A systematic review, proposed framework and future research agenda
Bibliographic Data
| ID | 14620855 |
|---|---|
| Authors | Qi Tan (0000-0002-7354-2025, School of Mathematical Sciences East China Normal University Shanghai China, corresponding author) |
| Year | 2025 |
| Volume | 52 |
| Issue | 2 |
| Pages | 1219-1247 |
| Publication date | 2025-11-09 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | British Educational Research Journal (JOURNAL) |
| Journal identifiers | ISSN: 0141-1926 • E-ISSN: 1469-3518 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/berj.70059 |
| OpenAlex | W4416059665 |
| Language | EN |
| References cited | 90 |
Generative AI is revolutionizing various industries, particularly in education. However, its adoption in education is still limited, with several factors yet to be systematically analysed. This systematic literature review seeks to identify and categorize the key factors influencing the adoption of generative AI among educational stakeholders, such as students and teachers. To this end, a search was conducted across three databases, namely, Scopus, Web of Science Core Collection and ERIC. Finally, 43 empirical studies were included in the final review. The findings indicate increasing scholarly focus on the factors influencing generative AI adoption in education, especially in higher education, with most studies focusing on students and varying in sample sizes. The Technology Acceptance Model (TAM) was the most commonly used model for studying the adoption of generative AI in education. The factors identified were grouped into categories such as psychological and behavioural, technological, social, conditional, quality, task‐related and inhibiting factors. Additionally, some key impactful moderators were found, including gender, educational level, experience with generative AI and technological proficiency. Based on these findings, a framework for generative AI adoption in education is proposed, alongside a future research agenda. This review offers valuable theoretical insights and practical recommendations for educators, policymakers and generative AI developers in the educational context
Categorization · Educational research · Empirical research · Generative grammar · Generative model · Key (lock) · Sample (material) · AI in Service Interactions · Artificial Intelligence in Healthcare and Education · Educational Games and Gamification
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| Highly cited | No |
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