# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 4 The Three-Tier Framework for GenAI Adoption in Education

Table 1: The Three-Tier Framework for GenAI Technology Adoption in Education. \begin{table}
\begin{tabular}{l} \hline
**The Three-Tier Framework for GenAI Technology Adoption in Education** \\ \hline Effort Expectancy/Perceived Ease of Use \\ \(\bullet\) Definition \\ Effort Expectancy, also referred to as Perceived Ease of Use, represents the degree to which users believe that using GenAI technologies will be easy and effortless. This factor reflects the perceived simplicity and learnability of the technology, influencing users' willingness to adopt and use it. \\ \(\bullet\) Empirical Evidence \\ Studies demonstrate that when users perceive GenAI as easy to use, they are more likely to adopt it, as evidenced by its significant influence on Behavioral Intention (BI) [33, 45, 48, 65]. Effort Expectancy also indirectly shapes perceptions of usefulness and control; for instance, [PERSON] et al. [49] found that ease of use enhances users’ views of GenAI’s utility, while [PERSON] et al. [35] emphasized the adoption benefits of simpler interfaces. Additionally, [PERSON] et al. [28] linked Effort Expectancy to both BI and actual use, highlighting its role in sustaining long-term engagement. \\ Conversely, several studies suggest that Effort Expectancy has limited significance in driving BI for GenAI technologies. [PERSON] et al. [31], [PERSON] [42], and [PERSON] et al. [50] observed no significant impact of Effort Expectancy on BI in educational contexts, where users prioritize functionality and performance over ease of use. Similarly, [PERSON] et al. [47] and [PERSON] et al. [43] reported weak or negligible correlations, suggesting that simplicity plays a minor role when trust or perceived benefits dominate decision-making. \\ These findings suggest that Effort Expectancy’s influence on GenAI adoption is moderate and context-dependent, particularly in settings where users possess baseline digital skills or prioritize performance over usability. \\ \(\bullet\) Rationale \\ Effort Expectancy is a moderate but important predictor of GenAI adoption, particularly in contexts where ease of use reduces cognitive load and supports broader adoption. For novice users or those with lower digital literacy, intuitive designs and simplified interfaces are critical for ensuring a smooth onboarding process and effective engagement with the technology. In educational settings, ease of use enhances users’ ability to achieve learning outcomes efficiently, making it a valuable attribute. However, for experienced users or those focused on performance and functionality, the importance of Effort Expectancy diminishes, as they prioritize utility over usability. Despite this variability, ensuring the technology is user-friendly remains vital for fostering adoption among diverse user groups with varying levels of proficiency. \\ \hline Facilitating Conditions \\ \(\bullet\) Definition \\ Facilitating Conditions refers to the availability of resources, infrastructure, and support necessary for users to effectively adopt and utilize GenAI technologies. This includes access to devices, platforms, training, and institutional support. \\ \(\bullet\) Empirical Evidence \\ [PERSON] et al. [45] emphasize the critical role of Facilitating Conditions (FCs) in both shaping Behavioral Intention (BI) and sustaining actual use, highlighting their prominence across the adoption lifecycle. Similarly, [PERSON] and [PERSON] [33] and [PERSON] and [PERSON] [48] find that FCs significantly influence actual usage behavior, demonstrating their impact in translating intention into action. [PERSON] et al. [35] and [PERSON] [29] further validate FCs as a determinant of both initial adoption and continued use, stressing the worth of accessible resources, infrastructure, and institutional support in ensuring successful GenAI integration in educational contexts. \\ Conversely, other studies suggest that Facilitating Conditions may have a limited role in predicting BI for GenAI technologies.