# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 3 Results and Discussion
### Regional and Cultural Dimensions of GenAI Adoption in Education  
A review of the publication timeline shows that 26 of the 27 included studies were published in 2024, with only 1 study--[PERSON] et al. [28]--appearing earlier. This concentration underscores both the recency of GenAI integration in education and the rapid academic response to its emerging significance. It also reflects the novelty and dynamism of the field, still in the early stages of theoretical consolidation and empirical investigation. Since the review was conducted in mid-December 2024, it is likely that additional relevant publications have since emerged, indicating a continually evolving research landscape. Geographically, the studies reflect broad international engagement. The majority originated from China (11 studies), reflecting the country's strong emphasis on AI research and its integration in education. Other active contributors include India, Thailand, the United Arab Emirates (UAE), and Vietnam, each with two studies. Additional countries represented by single studies include Malaysia, Norway, Poland, Singapore, South Korea, and Tanzania. One study involved a cross-national comparison between Egypt and Poland, and another encompassed multiple countries. This geographical diversity underscores the global relevance of GenAI in education and signals growing international interest in its adoption across varied educational systems. These trends are visualized in Figure 1, which maps the geographic distribution of the reviewed studies. The figure illustrates China's leading position, followed by a broader representation across Asia, Europe, and Africa, confirming the widespread attention GenAI is receiving worldwide. Beyond geographic distribution, the reviewed studies reflect diverse educational, cultural, and socio-technical contexts that shape how GenAI is perceived and adopted in practice. A substantial proportion of studies were conducted in East and Southeast Asia, particularly China (11 studies), followed by India (2), Thailand (2), and Vietnam (2). These  
Figure 1: Geographic distribution of empirical studies on GenAI adoption in education. studies frequently emphasize performance-oriented educational goals, AI literacy, and institutional support, aligning with national policies focused on digital transformation. For example, studies from China and Malaysia (e.g., [29; 30]) incorporate predictors such as trust in AI, personal innovativeness, and perceived learning performance, suggesting an adoption discourse oriented toward technological efficacy and learner outcomes. In contrast, European studies, such as those from Norway [31] and Poland [32; 33], often integrate predictors like habit, hedonic motivation, and facilitating conditions, and in some cases challenge established assumptions (e.g., non-significance of effort expectancy or social influence). These patterns may reflect greater emphasis on individual autonomy, user experience, and long-term engagement within European educational settings. Studies from Africa (e.g., Tanzania [34]) emphasize factors such as information quality and social influence, which are particularly salient in contexts where access to reliable digital resources and peer validation play a key role in shaping technology use. These findings highlight the importance of adapting GenAI tools to local infrastructure and educational needs, though direct discussion of systemic infrastructural constraints is limited in the reviewed literature. In the Middle East, research from the United Arab Emirates (e.g., [35]) reflects a hybrid integration of global acceptance frameworks (e.g., TAM, UTAUT) with regionally relevant constructs such as user satisfaction, perceived efficiency, and learning performance. However, another UAE-based study [36] focuses more on perceived anthropomorphism and social sustainability, demonstrating how local values and social perceptions of AI can shape the design of adoption models.