# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 3 Results and Discussion
### What Influences GenAI Adoption in Education? A Closer Look
[45] find that Social Influence significantly impacts BI, particularly in environments where peer or societal expectations drive behavior. [PERSON] and [PERSON] [65] find SI to significantly influence both BI and usage intentions. However, studies like [PERSON] et al. [43] and [PERSON] et al. [32] report minimal effects, suggesting that intrinsic motivators often outweigh external pressures. * _Perceived Behavioral Control_: Confidence in one's ability to use GenAI effectively is an important determinant of BI. [PERSON] et al. [49], [PERSON] et al. [51], and [PERSON] et al. [30] underscore its role in empowering users, while [PERSON] et al. [36] observe that its significance may diminish when external enablers, such as of facilitating conditions or social pressures, are strong. * _Perceived Compatibility_: Compatibility between GenAI and users' needs, workflows, and values significantly foster adoption. [PERSON] et al. [39] demonstrate that seamless integration into educational practices enhances BI, while [PERSON] et al. [46] validate its role in making GenAI intuitive and relevant for users. * _Habit_: Habit strongly predicts both BI and continued use. [PERSON] et al. [45], [PERSON] et al. [31], and [PERSON] et al. [32] emphasize that routine engagement with GenAI fosters sustained adoption. [PERSON] et al. [43] further highlight the role of prior usage experiences in enhancing the chance of continued engagement and long-term use. * _AI Literacy_: Greater AI literacy equips users with the knowledge needed to engage confidently with GenAI. [PERSON] [42] and [PERSON] et al. [64] find that understanding AI principles enhances user confidence and perceived benefits, leading to positive adoption intentions. * _Anxiety_: Concerns about job displacement, ethical implications, or technological complexity can act as barriers to adoption. [PERSON] et al. [47], [PERSON] et al. [62], and [PERSON] et al. [50] show that anxiety negatively impacts BI by reducing perceptions of usefulness and ease of use. Addressing these fears is essential for fostering acceptance. * _Playfulness_ (_Hedonic Motivation_): Enjoyment and intrinsic satisfaction derived from GenAI usage strongly influence BI. [PERSON] et al. [45] and [PERSON] et al. [46] highlight that enjoyment and playfulness foster user engagement and enhance the likelihood of continued use, while [PERSON] et al. [32] emphasize the enjoyment and pleasure of GenAI use as strong predictors of BI. These findings collectively underscore the multifaceted nature of GenAI adoption in education, shaped by a dynamic interplay of technological, individual, and social factors. The relative weight of these predictors often varies by context, depending on institutional type, learner demographics, or resource availability. This variation points to the importance of context-aware approaches when planning and evaluating GenAI adoption initiatives. To support both research and practice, a systematic categorization of these predictors may prove valuable. Such a framework can guide future investigations, enabling researchers to extend existing models, accommodate emerging evidence, and identify new variables. Ultimately, this structured approach will help develop a more comprehensive and adaptable understanding of GenAI adoption across diverse educational settings. To address the complexity and contextual variability of the identified predictors, a more structured representation is essential. Accordingly, the following section proposes a conceptual framework that organizes these predictors into coherent categories. This framework aims not only to clarify the relationships among key adoption factors but also to support researchers and practitioners in systematically analyzing and applying them within diverse educational environments.