# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 1 Introduction  
The integration of generative artificial intelligence (GenAI) into education represents a transformative shift in pedagogical practices and learning experiences. As educational institutions increasingly explore its potential to support personalization, enhance learner engagement, and optimize instructional strategies, understanding the drivers of its acceptance and adoption becomes a critical academic and practical pursuit. However, despite growing interest, the current discourse remains fragmented, lacking a systematic framework that comprehensively consolidates the factors influencing GenAI uptake in educational settings. This review addresses this gap by synthesizing the existing literature to uncover key empirically supported predictors--such as performance expectancy, trust, habit, and AI literacy--that shape the adoption process. By leveraging a concept-centric approach [1], the study avoids simple narrative listings and instead builds its analysis around validated theoretical models and core constructs, offering a structured and theoretically grounded understanding. Importantly, the review does not limit itself to specific publication venuesor temporal scopes, ensuring broad and unbiased coverage of relevant empirical and theoretical contributions. Although several domains have begun to explore GenAI's application, including healthcare, mental health, and human activity recognition, education and training contexts remain among the most investigated fields. Studies such as systematic meta-review by [PERSON] et al. [2] and survey by [PERSON] [3] exemplify this interdisciplinary reach, examining conversational agents and large language models (LLMs) in contexts ranging from elderly care and mental health to behavioral modeling using wearable data. Across these fields, ethical concerns--including privacy, transparency, psychological impacts, and interpretability--frequently surface as barriers to adoption. Focusing on healthcare, reviews by [PERSON] et al. [4] and [PERSON] et al. [5] illustrate both the promise and limitations of LLMs in diagnostic imaging, clinical NLP, and decision support, noting the need for technological refinement and ethical safeguards. Similarly, review studies by [PERSON] et al. [6] and [PERSON] and [PERSON] [7] reveal that GenAI currently plays a supportive role, enhancing diagnostic decision-making and patient engagement but rarely replacing human judgment. These insights echo broader concerns raised by [PERSON] et al. [8] and [PERSON] [9] regarding the reliability and transparency of GenAI tools. In education, recent systematic reviews and scoping studies have begun to trace both the opportunities and risks associated with GenAI. [PERSON] et al. [10] and [PERSON] et al. [11] identify key adoption trends, ethical challenges, and themes such as academic integrity, assessment redesign, and institutional readiness. [PERSON] et al. [12] and [PERSON] et al. [13] highlight GenAI's potential in supporting personalized feedback, adaptive content generation, and conversational tutoring while also noting constraints such as the hallucination effect and the need for human oversight. At a pedagogical level, in their review study [PERSON] et al. [14] emphasize the gap between educational and industrial GenAI adoption, particularly in computing education, and call for alignment of curricular design with real-world AI practices. These concerns are further echoed in the scoping review of [PERSON] et al. [15] who offer a categorization of user, operational, technological, and ethical challenges in ChatGPT's educational use. Meanwhile, [PERSON]'s [16] systematic review contributes bibliometric insight, mapping global trends and flagging the technology's potential impact on learner creativity and academic dishonesty. The ethical dimension remains central to the discourse. Scoping reviews by [PERSON] [17] and [PERSON] et al. [18] explore the normative landscape of GenAI, with detailed taxonomies of risks related to fairness, privacy, hallucinations, and societal impacts.