Smart Decision-Making: The Role of Digital Twins, Retrieval-Augmented Generation-Enhanced AI, and Learning Analytics

Decision-making in higher education institutions faces complex challenges due to increasing data, regulatory demands, resource management, and changing educational needs. Effective decisions require accurate, timely insights into institutional processes. This study introduces an integrated decision-support approach consisting of Learning Analytics, Digital Twins, and Generative Artificial Intelligence (AI) with Retrieval-Augmented Generation (RAG). The objective is to enhance decision accuracy and operational efficiency in higher education. Learning Analytics provide insights into student performance, teaching effectiveness, and curriculum efficiency. Generative AI with RAG facilitates rapid retrieval and synthesis of institutional documentation and regulatory framework, enhancing decision accuracy. Digital Twins simulate institutional operations, enabling predictive resource planning and infrastructure modeling. Preliminary results indicate significant improvements in strategic planning and resource management by using an integrated decision support approach. Collectively, these technologies transform strategic decision-making and efficiency in higher education, enabling an agile and smart approach, offering a robust solution to contemporary challenges.