Mentor
Scot Anderson
Document Type
Thesis
Publication Date
Fall 12-12-2025
Abstract
We present a grading system that accelerates evaluation of open-ended student work across scanned and digital workflows. The system crops answer regions from PDFs, assigns submissions via OCR on identity regions only, and groups answers by visual semantics using a vision LLM. Instructors review and edit groups, apply rubric items once per group, and export grades from an on-screen table. The solution integrates Ghostscript rasterization, PdfPig page orchestration, SkiaSharp region extraction, Tesseract identity OCR, and GPT-4o Vision for grouping. We detail the architecture, token-budgeted batching strategy, and persistence design, then describe testing results for grouping quality, time-on-task, and usability. The approach avoids brittle handwriting OCR while preserving instructor control, fairness, and auditability.
Recommended Citation
Modiga, Andrei, "DEVELOPING AN AI-ASSISTED GRADING SYSTEM USING LARGE LANGUAGE MODELS" (2025). MS in Computer Science Project Reports. 15.
https://knowledge.e.southern.edu/mscs_reports/15
Included in
Artificial Intelligence and Robotics Commons, Graphics and Human Computer Interfaces Commons, Software Engineering Commons
Comments
Project available at https://www.oiclearning.com/