RubricAI: AI-driven Automated Assignment Evaluation for Plagiarism Detection and Grading

The manual process of grading student assignments presents significant challenges for educators, including substantial time consumption and the difficulty of detecting plagiarism at scale. This inefficiency detracts from valuable teaching time and can impact the fairness of evaluations. The traditional assignment evaluation process in academia is time-consuming, subjective, and prone to human error. With the growing volume of student submissions in higher education, scalable and intelligent solutions are needed. This paper presents RubricAI, an AI-powered platform designed for automated assignment evaluation, plagiarism detection, and intelligent grading. The system leverages Natural Language Processing (NLP), semantic similarity algorithms, and machine learning models to assess assignments against rubrics, identify originality issues, and provide fair, transparent, and consistent grading. Experimental analysis shows that RubricAI enhances academic integrity while reducing faculty workload, providing a transformative solution for modern educational ecosystems The "RubricAI" platform is designed as an intelligent solution to streamline and enhance the assignment evaluation process. By leveraging advanced AI and machine learning, RubricAI automates plagiarism detection and assignment grading, integrating seamlessly with existing classroom systems. With features including similarity checking, automated evaluation based on predefined criteria, and a comprehensive teacher dashboard, RubricAI aims to empower educators by providing an efficient, fast, and fair evaluation tool. By automating repetitive tasks, the platform allows teachers to focus more on instruction and less on administrative tracking, thereby improving the overall educational experience.