Large Language Model and Fuzzy Metric Integration in Assignment Grading for Introduction to Programming Type of Courses

The integration of large language models (LLMs) and fuzzy metrics offers new possibilities for improving automated grading in programming education. While LLMs enable efficient generation and semantic evaluation of programming assignments, traditional crisp grading schemes fail to adequately capture partial correctness and uncertainty. This paper proposes a grading framework in which LLMs assess student solutions according to predefined criteria and output fuzzy grades represented by trapezoidal membership functions. Defuzzification is performed using the centroid method, after which fuzzy distance measures and fuzzy C-means clustering are applied to correct grades based on cluster centroids corresponding to linguistic performance levels (poor, good, excellent). The approach is evaluated on several years of real course data from an introductory programming course with approximately 800 students per year called “Programski jezici i strukture podataka” in the first year of studies of multiple study programs at the Faculty of Technical Sciences, University of Novi Sad, Serbia. Experimental results show that direct fuzzy grading tends to be overly strict compared to human grading, while fuzzy metric correction significantly reduces grading deviation and improves alignment with human assessment, particularly for higher-performing students. Combining LLM-based semantic analysis with fuzzy metrics yields a more nuanced, interpretable, and adaptable grading process, with potential applicability across a wide range of educational assessment scenarios.