Design and Application of an Automatic Scoring System for English Composition Based on Artificial Intelligence Technology

Abstract

The automatic grading of English compositions involves utilizing natural language processing, statistics, artificial intelligence (AI), and other techniques to evaluate and score compositions. This approach is objective, fair, and resource-efficient. The current widely used evaluation system for English compositions falls short in off-topic assessment, as subjective factors in manual marking lead to inconsistent scoring standards, which affects objectivity and fairness. Hence, researching and implementing an AI-based automatic scoring system for English compositions holds significant importance. This paper examines various composition evaluation factors, such as vocabulary usage, sentence structure, errors, development, word frequency, and examples. These factors are classified, quantified, and analysed using methods such as standardization, cluster analysis, and TF word frequency. Scores are assigned to each feature factor based on fuzzy clustering analysis and the information entropy principle of rough set theory. The system can flexibly identify composition themes in batches and rapidly score English compositions, offering more objective and impartial quality control. The goal of the proposed system is to address existing issues in teacher corrections and evaluations, as well as low self-efficacy in students' writing learning. The test results demonstrate that the system expands the learning material collections, enhances the identification of weak points, optimizes the marking engine performance with the text matching degree, reduces the marking time, and ensures efficient and high-quality assessments. Overall, this system shows great potential for widespread adoption.