.png){kind=icon}
.png){kind=icon}
Return
AI-Based Automated Scoring Layer Using Large Language Models and Semantic Analysis
.png){kind=icon}
PRE
AI
DOI：10.3390/app16073537.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Featured Application This study presents an AI-based scoring layer for automated assessment of open-ended student responses. The proposed framework combines large language models, Retrieval-Augmented Generation (RAG), and analytical rubrics in order to support criterion-based, context-grounded evaluation in e-learning environments. It can be integrated into platforms such as Moodle to assist instructors in grading, improve consistency, reduce scoring time, and support faster and more structured feedback for learners.Abstract Automated scoring of open-ended questions is an important research direction in educational technology and artificial intelligence, as manual grading is time-consuming and often subject to inter-rater variation. This paper proposes an AI-based framework for automated scoring that combines large language models (LLMs), Retrieval-Augmented Generation (RAG), analytical rubrics, and structured machine-readable output within a Moodle-supported e-learning environment. The framework is designed to support context-grounded and criterion-based evaluation by combining the student response, retrieved instructional context, and rubric-defined scoring criteria within a controlled assessment workflow. The proposed approach aims to improve the consistency, traceability, and practical applicability of automated scoring for open-ended responses. To examine its performance, an experimental study was conducted in a real university setting involving a five-task open-ended examination. AI-generated scores were compared with independent human scores using agreement, reliability, correlation, and error metrics. The results indicate a strong level of agreement between automated and expert scoring within the tested setting, together with relatively low average deviation. These findings suggest that the proposed framework has practical potential for supporting automated assessment in digital learning environments, while also highlighting the importance of careful interpretation within the scope of the experimental design.
Keywords:
artificial intelligence
automated scoring
Bloom's taxonomy
large language models
natural language processing
open-ended questions
RAG
semantic analysis