A Smart Decision Support Framework: Leveraging AI and Business Intelligence for Sustainable Higher Education

Abstract

The sustainability management in higher education has become a great challenge as the data is located in various systems and only limited decision-making and support capabilities are available. The data- based decision-making, predictive analytics, and the automation that are AI and BI-based (artificial intelligence and business intelligence) do bring in a very different approach to the problems. This paper argues that smarter decision support systems that are developed through the combination of the cognitive abilities of AI with the structured data processing of BI will amplify most of those management functions. The model uses machine learning algorithms for predictive modelling, natural language processing for automated data extraction, and interactive BI dashboards for real-time analytics. The universities, with the help of the AI-generated wisdom, could do better in their resources utilization, there can also be bettered policies, and last but not least, errors in sustainability reporting could be reduced. This way, it is also a means to solve the problem of compatibility through the more traditional university management systems by using standardized data formats and API-based solutions. Furthermore, the framework follows ethical AI principles such as fairness, transparency, and accountability to ensure the quality of sustainability decision-making. The use of the framework will cause the universities to change their management systems from reactive ones to proactive, data- driven, and thus will become more environmentally friendly and operationally efficient. However, the suggested framework may serve as a scalable and adaptable solution for sustainable higher education although it has some problems such as potential data privacy risks, limitations of the infrastructure, and resistance to technological change. It is proposed that future research should concentrate on perfecting AI models, improving interoperability with legacy systems, and researching on artificial intelligence for a comprehensive sustainability management.