Published Paper Details:

The rapid evolution of Artificial Intelligence (AI) is reshaping digital education by enabling personalized learning, intelligent tutoring, and data-driven decision-making. However, existing educational systems often employ Generative Artificial Intelligence (GenAI), Retrieval-Augmented Generation (RAG), and Learning Analytics (LA) as independent technologies, limiting their collective potential. This study proposes a unified conceptual framework that integrates these technologies to support the development of future-proof intelligent education. In the proposed framework, GenAI facilitates intelligent content generation and learner support, RAG enhances the reliability of AI-generated responses through knowledge retrieval, and Learning Analytics provides personalized insights based on learner behaviour and performance. The framework illustrates how these technologies can work together to create adaptive, trustworthy, and learner-centered digital learning environments. To strengthen the theoretical foundation, a PLS-SEM structural validation model is proposed to establish the relationships among the constructs and provide a basis for future empirical investigation. The study further discusses key implementation challenges, including data privacy, algorithmic bias, ethical AI governance, interoperability, and digital literacy. The proposed framework contributes to the emerging field of AI-enabled education by offering a comprehensive roadmap for integrating trustworthy AI with evidence-based educational analytics. It also provides practical guidance for educators, researchers, and policymakers seeking to design intelligent and sustainable digital learning ecosystems. Future studies may empirically validate the proposed model across diverse educational contexts to further establish its applicability and generalizability.