The evaluation of student performance is a multifaceted process that traditionally relies on rigid grading systems, often failing to capture the complexity of student abilities and learning dynamics. This paper explores the application of fuzzy logic to enhance the accuracy and adaptability of student performance evaluation. By leveraging fuzzy logic principles, such as membership functions and linguistic variables, we model and assess the various factors influencing student performance—ranging from academic grades to engagement, participation, and personal learning styles. The approach allows for the representation of imprecise, uncertain, and subjective data, offering a more holistic view of a student’s progress. We propose a fuzzy inference system that combines multiple assessment criteria to generate more flexible and context-sensitive evaluations. The results demonstrate that this method provides more meaningful insights, offering educators a powerful tool for personalized learning, identification of at-risk students, and tailored interventions. The findings suggest that fuzzy logic-based models hold significant potential for transforming the traditional student evaluation process into a more dynamic, inclusive, and accurate system, adaptable to diverse educational contexts.

Keywords: Education, fuzzy logic.