EsyGrade: An AI-Based Essay Assessment System for Economics Education

Abstract

This study aims to design, develop, and evaluate EsyGrade, a web-based essay grading system integrated with ChatGPT to support the assessment of conceptual understanding in economics education. This study is motivated by the prevalence of multiple-choice questions in Indonesian high schools due to efficiency considerations, while essay assessments better suited to measuring conceptual reasoning are still rarely used because of high assessment load. The study employed a simplified Research and Development (R&D) approach consisting of seven stages. Data were collected through expert validation using Aiken’s V, a user response questionnaire, and pretest–post-test, and were later analysed using N-Gain and effect size (Cohen’s d). The research findings indicate that EsyGrade has a high level of validity based on the expert evaluation and an excellent acceptance rate in the initial pilot test. In the main field trial, the results indicated a moderate improvement in conceptual understanding based on N-Gain, with a “large” effect size. These findings suggest that the overall change in learning outcomes indicates a fairly strong impact, although the degree of improvement varied among students. In particular, the system’s key value lies in its ability to generate structured, reflective feedback, so that it serves not only as a summative assessment tool but also as a means for formative one. This study contributes to the development of e-learning by demonstrating that AI-based essay grading systems can be designed to improve efficiency while supporting deeper learning through the integration of pedagogical principles and technology.