# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 4 The Three-Tier Framework for GenAI Adoption in Education  
Building on the synthesized findings of this review, this section introduces the _Three-Tier Framework for GenAI Technology Adoption in Education_. Designed to offer conceptual clarity and practical utility, the framework organizes the 11 key predictors into three interconnected tiers--_Core Predictors_, _Moderate Predictors_, and _Emerging Predictors_. This tiered structure enables a nuanced understanding of the diverse and interdependent factors that influence educators' and learners' acceptance and integration of generative AI tools in educational settings. The framework provides a holistic perspective by clustering predictors based on their conceptual centrality, empirical support, and frequency of appearance across the reviewed studies. Each tier reflects a different level of influence and maturity in the research landscape, thereby offering both theoretical grounding and practical insight into the dynamics of GenAI adoption. 1. Core Predictors (consistently significant across studies): These factors consistently exert a strong influence on the adoption of GenAI technologies, serving as essential drivers rooted in individual perceptions and socio-technical dynamics. They are the most widely recognized and stable determinants of how individuals perceive and engage with AI technologies: \(\circ\) Performance Expectancy (Perceived Usefulness) \(\circ\) Trust
2. Moderate Predictors (context-dependent): These predictors vary in their influence depending on specific educational or technological contexts, emphasizing the importance of task and environmental factors in shaping GenAI adoption. While significant, their impact is less consistent and often contingent on situational factors: \(\circ\) Effort Expectancy (Perceived Ease of Use) \(\circ\) Facilitating Conditions \(\circ\) Social Influence \(\circ\) Perceived Behavioral Control \(\circ\) Perceived Compatibility
3. Emerging Predictors (increasing relevance): Reflecting evolving individual and socio-technical dimensions, these factors are gaining prominence as GenAI technologies mature and expand their applications in education. Although less established than core and moderate predictors, they are increasingly supported by empirical research and are expected to grow in relevance: \(\circ\) Habit \(\circ\) AI Literacy \(\circ\) Anxiety \(\circ\) Playfulness (Hedonic Motivation)  
Each predictor is analyzed in terms of its definition, underlying rationale, and empirical evidence, as synthesized from the 27 studies included in this review (see Table 1). This structured approach ensures the framework's relevance across contexts and its usefulness as a foundation for future research, policy formulation, and institutional implementation. The _Three-Tier Framework for GenAI Adoption in Education_ is both adequate and credible, grounded in established models such as TAM, UTAUT, and UTAUT2, while integrating emerging predictors specific to educational contexts. Core Predictors such as Performance Expectancy and Trust consistently demonstrate strong influence on adoption. Moderate Predictors emphasize the context-dependent nature of factors like infrastructure, social dynamics, and compatibility with existing practices. Meanwhile, Emerging Predictors--including Habit, AI Literacy, and Anxiety--are gaining relevance as GenAI tools become increasingly embedded in educational settings. Although further exploration is needed to understand the interplay and relative influence of these predictors, the framework offers a sound foundation for investigating GenAI adoption in education. As research progresses and technologies evolve, Emerging Predictors are expected to gain greater prominence, calling for ongoing refinement of the framework to accommodate new insights and address emerging challenges.