Enhancing Educational Systems with Fuzzy Logic-Based Decision-Making Frameworks

The development of education demands a paradigm change toward learning systems that are tailored and flexible and that satisfy the several needs of students. Conventional methods typically lack the adaptability to handle different learning speeds, cognitive skills, and customized support. This limit also affects general performance and satisfaction as well. Fuzzy logic offers a new way to solve these problems by letting systems handle uncertain and imprecise data, so simulating human decision-making. This paper attempts to introduce a fuzzy logic based on framework for decision-making proposal to enhance educational systems. This framework creates adaptive recommendations by combining elements in a range that include student involvement, performance, learning preferences, and feedback. Examining real-time student data with fuzzy rules and membership functions, the model produces forecasts about outcomes and recommendations for tailored interventions. The method employs the evolution of fuzzy inference systems, the simulation of several learning environments, and the validation of the methodology using a dataset comprising of 1500 student records obtained by means of academic performance measures and surveys. The proposed fuzzy logic framework increases satisfaction by 30 % and improves prediction accuracy for student performance by 95 % when compared to the conventional approaches, preliminary results show. The system's adaptability lets one create personalized learning paths, so creating an inclusive environment where students could realize their own potential. Reducing the work teachers have to do, the framework also automates routine tasks including performance analysis and feedback generation.