# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 5 Conclusions
### Directions for Future Research  
Building on the findings and limitations identified in this review, several key areas warrant further exploration to refine theoretical models, inform policy, and guide practical implementation. These research directions focus on underexplored predictors, cultural and contextual variation, evolving technologies, and interdisciplinary perspectives. Collectively, they aim to ensure the continued relevance and robustness of the proposed Three-Tier Framework for GenAI adoption in education. * Refining the Three-Tier Framework and longitudinal studies: Future research should refine and expand the Three-Tier Framework by incorporating emerging predictors and examining their stability over time. As GenAI technologies mature, predictors such as trust, performance expectancy, and social influence may evolve in significance. Longitudinal studies are particularly valuable for capturing temporal shifts in adoption dynamics and understanding how sustained exposure to GenAI tools reshapes user attitudes and behaviors. * Cultural, contextual, and demographic variations in GenAI adoption: The review highlights that GenAI adoption is shaped by regional, cultural, and institutional contexts. Studies across regions such as East Asia, Europe, and Africa suggest differences in the conceptualization of adoption drivers, reflecting variations in educational values, technological infrastructure, and policy environments. Future research should investigate how cultural dimensions--such as collectivism vs. individualism, or institutional centralization--affect perceptions and adoption. In parallel, studies should examine how contextual factors (e.g., educational level) and demographic characteristics (e.g., age, gender, socio-economic status) influence adoption behavior. Special attention should be given to teacher-specific factors, such as AI literacy, pedagogical readiness, and access to professional development, particularly in under-resourced settings. Ethical considerations and adoption barriers: Ethical concerns, including data privacy, algorithmic bias, and fairness, remain insufficiently addressed in the existing literature. These issues can significantly influence trust, perceived risk, and ultimately, adoption intentions. Future studies should explore how ethical considerations interact with predictors such as perceived usefulness, trust, and social influence. Additionally, research should identify barriers such as GenAI-related anxiety, fear of obsolescence, and lack of infrastructure to develop interventions that promote equitable and responsible adoption. * Student engagement and learner-centered adoption: Student-related factors--such as engagement, autonomy, and motivation--are underrepresented in current GenAI adoption studies. As GenAI tools increasingly support personalized and student-centered learning, research should focus on how these technologies influence learner engagement, self-regulation, and educational outcomes. Understanding these dynamics will be essential for maximizing the pedagogical potential of GenAI while mitigating risks such as over-reliance or reduced critical thinking. * Technological advancements and predictor evolution: Rapid advancements in GenAI technologies, such as multimodal interfaces and adaptive AI systems, may alter the relevance and impact of existing adoption predictors. Research should investigate how these new technological affordances reshape user experiences and expectations, and whether they give rise to new predictors not yet captured by current models. This continuous adaptation is essential to ensure that theoretical frameworks remain relevant in a fast-evolving landscape. * Interdisciplinary collaboration and model development: The complexity of GenAI adoption necessitates interdisciplinary collaboration across fields such as education, computer science, psychology, and sociology.