Empowering the Future through Unleashing AI's Potential in Education to Transform Learning and Boost Digital Literacy

Recently, artificial intelligence (AI) has become a driving force in education, revolutionizing traditional learning models and opportunities to learn digital literacy. Studies that have examined the effect of AI on teaching and learning are scarce, as are those that investigate the relations between AI integration, teacher proficiency, student engagement, and digital literacy on educational platforms. In this study, AI integration in educational platforms is explored as it plays a part in education digital literacy development and the transformation of learning spaces. The research employs the Technology Acceptance Model (T-A-M) to investigate how AI-based platforms, teacher AI ability, and student engagement affect learning satisfaction. Methodology: data collected from 219 Russian students were analyzed using a structural equation modeling (SEM) approach. The study found that AI integration in educational platforms magnifies student engagement, which, in turn, mediates the relationship between AI adoption and student learning satisfaction. The path coefficient of 0.215 and T-value of 5.429 also indicated the effect of engagement, although of positive and significant value on satisfaction, when contributing to AI-supported learning processes.H3 was conducted to examine the direct effect of teacher AI proficiency on learning satisfaction. This relationship had a path coefficient of 0.144; the T value was 3.531, indicating a significant positive effect. Whereas teacher's AI proficiency positively affects students' engagement with AI tools. This relationship was strongly supported by a path coefficient of 0.268 and a T value of 7.312, implying that teacher expertise in AI significantly improved students' willingness to interact with AI-driven technologies. The results suggest that perceived usefulness and ease of use, as conceived in T-A-M, are likely facilitating factors of digital literacy development through student engagement. Within the context of education, building upon T-A-M, this study extends the framework to incorporate teacher proficiency and engagement to extend our understanding of the adoption of AI in education. The implications of the research suggest the need for professionals to develop AI platforms and design user-friendly AI platforms.