# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 3 Results and Discussion
### Adoption Models and Theoretical Frameworks Used in GenAI Research  
The review highlights the application of various technology acceptance and adoption models in the selected studies, reflecting a diverse approach to understanding the factors influencing the adoption of Generative AI (GenAI) in education. Among the 27 studies analyzed, the most frequently applied models were the Technology Acceptance Model (TAM) [52; 53], the Unified Theory of Acceptance and Use of Technology (UTAUT) [54], and the extended UTAUT (UTAUT2) [55]. Other frameworks, such as the Innovation Diffusion Theory (IDT) [56; 57] and the Expectation-Confirmation Model (ECM) [58], were also applied in individual studies. Specifically, TAM was employed in seven studies, UTAUT/UTAUT2 in eight studies, and the Theory of Planned Behavior (TPB) [59; 60] in three studies. Additionally, seven studies combined multiple theoretical frameworks to enhance the explanatory power of individual models. The breakdown of model utilization is as follows (in alphabetical order):  
* Expectation-Confirmation Model (ECM) [58], a model based on [PERSON]'s [61] leading cognitive theory in the field of consumer satisfaction that explains post-adoption satisfaction and behavior through Expectation-Confirmation Theory (ECT), was applied in one study [38]. * Innovation Diffusion Theory (IDT), proposed by [PERSON] [56; 57] as one of the most popular models for studying the adoption and use of innovations, was adopted in a single study [39]. * Technology Acceptance Model (TAM), introduced by [PERSON] [52; 53], a leading established theory and a parsimonious yet powerful model for explaining, predicting, and enhancing user acceptance in diverse technological deployments, was utilized in seven studies [28; 29; 37; 40; 44; 62]. * Integration of TAM and Self-Determination Theory (SDT) [63], a theoretical approach widely used in studies of motivational behavior that examines relationships among the social environment, psychological needs, motivation, outcomes, and well-being, was explored in two studies [41; 47]. * Combination of TAM and Theory of Planned Behavior (TPB) [59; 60], a theory stating that behavior is a direct function of behavioral intention and perceived behavioral control, was investigated in one study [49]. * Integration of TAM and UTAUT, a rich theoretical approach to explaining behavioral intentions and technology use, was analyzed in one study [35]. * TPB was employed in three studies [36; 51; 64]. * Combination of TPB and Behavioral Reasoning Theory (BRT), a theory that explores the cognitive processes and reasoning that bridge the gap between intentions and actual behavior, was examined in one study [30]. Figure 2: Participant types in studies on GenAI adoption in education. UTAUT was utilized in three studies [33; 48; 50]. * UTAUT2, an extension of the original UTAUT that emphasizes the context of consumer use, was applied in five studies [31; 32; 34; 42; 43]. * Combination of UTAUT and SDT was investigated in two studies [45; 65]. It is also important to note that the studies using TAM, UTAUT, UTAUT2, and TPB did not employ the core models in their original forms but instead extended or modified them to address specific aspects of GenAI adoption. These adjustments were made to improve the models' relevance and accuracy in the context of education and technology integration:  
* Extended Models: These models expand the original framework by adding new constructs or pathways while maintaining the core structure to address gaps or new challenges specific to the context. * Modified Models: These models fundamentally alter or redefine the original framework by removing constructs, changing relationships, or integrating elements from other models to better fit the research focus. These adjustments in the use of models reflect the dynamic nature of GenAI adoption research, where traditional technology acceptance models are often reshaped to capture the unique factors at play in the context of education.