# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 4 The Three-Tier Framework for GenAI Adoption in Education
\begin{table}
\begin{tabular}{p{343.0 pt}} \hline
**The Three-Tier Framework for GenAI Technology Adoption in Education** \\ \hline Performance Expectancy/Perceived Usefulness \\ \(\bullet\) Definition \\ Performance Expectancy, or Perceived Usefulness, refers to the degree to which users believe that using GenAI technologies will enhance their performance and effectiveness in educational tasks. It encompasses users’ perceptions of how GenAI can improve productivity, learning efficiency, task quality, and overall educational outcomes, making it a consistently significant predictor of technology adoption. \\ \(\bullet\) Empirical Evidence \\ Studies highlight the strong role of Performance Expectancy, or Perceived Usefulness, in shaping Behavioral Intention (BI), with users more likely to adopt GenAI when they perceive it as beneficial for their educational goals [31; 33; 42; 45; 50]. Numerous studies further validate the significance of Performance Expectancy in predicting BI and usage intentions, including [PERSON] et al. [32], [PERSON] and [PERSON] [65], [PERSON] and [PERSON] [48], and [PERSON] et al. [41]. Additionally, [PERSON] et al. [28] and [PERSON] et al. [44] demonstrate its influence not only on BI but also on actual use, highlighting its pivotal role in both initial adoption and sustained engagement. These findings position Performance Expectancy as a foundational element due to its consistent significance in driving GenAI adoption in educational contexts. \\ \(\bullet\) Rationale \\ Performance Expectancy, or Perceived Usefulness, is a universally recognized core predictor of GenAI adoption in education. Both students and educators prioritize technologies that deliver tangible benefits, such as enhancing academic performance, improving productivity, or increasing instructional effectiveness. Empirical evidence consistently highlights that users who perceive GenAI as capable of improving learning outcomes or task quality are more likely to develop positive attitudes and behavioral intentions toward its adoption. This factor not only drives initial acceptance but also supports sustained usage, as users are intrinsically motivated by tools that align with their goals for academic and instructional improvement. \\ \hline Trust \\ \(\bullet\) Definition \\ Trust refers to users’ confidence in the reliability, security, accuracy, integrity, and fairness of GenAI technologies. It encompasses the belief that these technologies will perform as expected, provide unbiased results, and handle sensitive data responsibly. Trust also includes transparency regarding the technology’s functioning and adherence to ethical guidelines, particularly in terms of privacy, security, and avoiding misuse. \\ \(\bullet\) Empirical Evidence \\ [PERSON] et al. [40] underscore the crucial role of perceived trust in GenAI awareness, acceptance, and adoption in higher education. Their study reveals that perceived trust significantly moderates the relationship between GenAI awareness and its perceived ease of use, usefulness, and intelligence. Furthermore, perceived trust directly influences Behavioral Intention (BI) to use GenAI, highlighting its central role in fostering adoption [29; 46]. In addition, trust also emerges as a critical factor in other studies, albeit implicitly. [PERSON] et al. [47] link trust to user confidence by addressing concerns related to privacy, ethics, and originality. [PERSON] et al. [64] implicitly tie trust to transparency and accountability through their focus on AI ethics. Similarly, [PERSON] et al. [62] identify AI confidence, interactivity, and content design as factors fostering positive attitudes, further underscoring trust’s implicit role in GenAI acceptance. These findings collectively position trust as a foundational element due to its consistent significance in fostering adoption across various contexts. \\ \(\bullet\) Rationale \\ Trust is consistently recognized as a core predictor of GenAI adoption, particularly in educational settings where the technology is relied upon for decision-making, content generation, and sensitive processes. Users’ confidence in the reliability, security, transparency, and ethical integrity of GenAI directly influences their willingness to adopt and integrate it into their workflows. In education, where AI is increasingly used for personalized learning, assessments, and content creation, it is essential for users to trust that the technology will deliver accurate, unbiased results and safeguard privacy. Establishing trust mitigates skepticism and concerns about misuse or misinformation, thereby lowering adoption barriers and supporting the long-term success and integration of GenAI tools in educational environments. \\ \(\bullet\) The three-tier Framework for GenAI Technology Adoption in Education. \\ \hline \end{tabular}
\end{table}