Artificial Intelligence in English Language Teaching: A Systematic Analysis of Global Trends

Abstract

While existing applications of artificial intelligence (AI) in higher education are growing, so is the formal application of AI in the assessment of students. Background: Conventional assessment approaches fail to capture students’ performance in more than one dimension. Rationale: AI can be used to develop evaluations that are more comprehensive, data-driven and equitable. Objective: The purpose of this study was to design and analyze an AI-based comprehensive evaluation model that includes attendance, research activities, seminars and exams. Materials and Methods: The method used is descriptive analytical design, and the data obtained in this study are institutional data of the faculty of education in Tripoli. Student performance indicators were used to develop integrated assessment outputs using AI-based algorithms. Conclusions: The AI model proved to be more effective in detecting performance patterns, improving prediction accuracy, and providing more balanced evaluation results than traditional approaches. Discussion: The results show how AI can enhance academic assessment, facilitate data-driven decisions, and mitigate bias, thereby fostering a fairer and more efficient evaluation process.