A RAG-Enhanced Human-in-the-Loop Framework for Automated Assessment of Engineering Laboratory Reports

Abstract

Recent developments in Large Language Models (LLMs) have created new opportunities to automate educational assessment and reduce workload for instructors. However, concerns regarding grading consistency, transparency, and pedagogical reliability continue to limit the adoption of fully automated assessment systems. In this study, we propose a Human-in-the-Loop framework for the automated evaluation of engineering laboratory reports based on Retrieval-Augmented Generation (RAG). The proposed framework integrates text extraction, structured content extraction, contextual retrieval from grading rubrics and laboratory resources, rubric-based evaluation, automatic feedback generation, and instructor validation into a unified grading workflow. The RAG module retrieves contextual information so that the language model can generate assessments that align with the course goals and are based on educational information about the specific assignments, thus ensuring consistent evaluation based on the rubric. The system was tested using a set of 56 laboratory reports collected from undergraduate courses in Electrical and Electronics Engineering. The experimental results indicate a strong agreement between the grades provided by the AI and the instructor, with a Pearson Correlation Coefficient of 0.988, a Mean Absolute Error (MAE) of 3.55, and a Root Mean Squared Error (RMSE) of 3.77. Besides, 89.29% of the reports were scored within ±5 points of the instructor scores. The grading time was reduced from 392 minutes to 84 minutes, a workload reduction of 78.57%. The results demonstrate that the integration of Retrieval-Augmented Generation, rubric-based evaluation, and Human-in-the-Loop validation constitutes an effective approach for AI-supported assessment of engineering laboratory reports, maintaining instructor oversight and educational integrity.