Automated Evaluation of Academic Assignments Using AI

Evaluating academic assignments manually requires considerable time and effort, particularly whenan instructor must assess many student submissions against common questions and evaluation criteria.Differences in interpretation may also lead to variation in the feedback provided to students. This paper presentsan AI-assisted academic assignment evaluation system that supports teachers in reviewing written studentresponses. The system accepts questions, reference answers, and evaluation rubrics from a teacher, extracts textfrom submitted assignment files and compares student responses with the corresponding reference material. Apretrained Sentence-BERT model is used to generate semantic sentence representations, and cosine similarity isapplied to estimate the semantic closeness between the student's response and the reference answer. Theresulting similarity information is considered within a rubric-oriented scoring workflow, while rule-based checkssupport the evaluation process. The application is implemented using a React/Vite frontend, a Python FastAPIbackend, and an SQLite database. Sentence-BERT is used in it pretrained form without task-specific fine-tuning.The completed testing reported for this project focuses on functional verification of the application workflow; abenchmark against human-assigned marks and quantitative grading-accuracy results have not been established.The system is therefore intended as a decision-support tool that can reduce repetitive evaluation effort andprovide structured feedback, with the teacher retaining responsibility for final academic judgement.