IntelliFuzz: An Advanced Fuzzy Logic Framework for Dynamic Evaluation of Student Performance in Open-Ended Learning Tasks

This study presents IntelliFuzz, an advanced fuzzy logic-based assessment system designed for the dynamic evaluation of student performance in open-ended tasks. The proposed system leverages fuzzy logic to address the inherent subjectivity and ambiguity in evaluating tasks such as essays, project work, and case studies. IntelliFuzz incorporates multiple evaluation criteria, including task relevance, critical thinking, creativity, and presentation quality, to generate a comprehensive performance score. Experimental results on a dataset of 500 student submissions demonstrate the effectiveness of IntelliFuzz. The system achieved a 95% accuracy in aligning with expert assessments and reduced evaluation time by 30% compared to traditional manual grading methods. The fuzzy inference system was calibrated using 150 expert feedback samples, yielding an average correlation coefficient of 0.92 between system-generated scores and expert evaluations. Furthermore, IntelliFuzz was rated 85% satisfactory by instructors for its ability to provide consistent and fair evaluations.The study highlights the potential of fuzzy logic in educational assessment, offering a scalable and efficient solution for evaluating subjective student tasks. Future research will focus on integrating machine learning to further enhance the adaptability and precision of the system.