AI-assisted linguistic analysis of grammatical errors in EFL Essay Writing: Implications for AI-enhanced language learning

Abstract

This study investigates the use of artificial intelligence–oriented linguistic analysis to identify and interpret grammatical errors in essays written by sophomore students enrolled in an English as a Foreign Language (EFL) program. The research adopts a qualitative descriptive design in which student essays are systematically analyzed using a structured taxonomy of grammatical errors. The analysis focuses on noun groups, verb–verb constructions, prepositional usage, and sentence structure patterns. The findings reveal recurring grammatical difficulties, particularly in subject–verb agreement, omission of plural markers, incorrect verb tense formation, misuse of auxiliary verbs, and inappropriate preposition selection. From an artificial intelligence perspective, these error patterns highlight opportunities for implementing automated grammar detection, intelligent feedback mechanisms, and adaptive learning systems that support students’ writing development. The study demonstrates how AI-driven linguistic analysis can assist instructors in diagnosing learner needs more efficiently and in designing targeted instructional interventions. Additionally, the results emphasize the role of AI-based writing evaluation tools in promoting personalized learning, improving grammatical accuracy, and enhancing students’ academic writing performance. By linking traditional error analysis with emerging AI applications in language education, this study contributes to the growing body of research on AI-enhanced teaching and learning. The findings provide practical implications for integrating intelligent tutoring systems, automated writing evaluation platforms, and data-driven feedback models in EFL classrooms.