AN ANALYSIS OF THE EFFECTIVENESS OF AUTOMATED ASSESSMENT OF STUDENTS' ENGLISH WRITTEN WORK

The rapid development of artificial intelligence (AI) and natural language processing (NLP) has significantly transformed educational assessment practices. Automated Writing Evaluation (AWE) systems are increasingly used to assess students’ English written work in various educational contexts. This study analyzes the effectiveness of automated assessment tools in evaluating students’ writing performance, with particular attention to accuracy, reliability, feedback quality, time efficiency, and pedagogical impact. Findings from recent research indicate that automated systems demonstrate high consistency in scoring and provide immediate, data-driven feedback that supports revision and self-regulated learning. However, limitations remain in evaluating higher-order skills such as creativity, critical thinking, and contextual appropriateness. The study concludes that automated assessment systems are most effective when implemented as complementary tools alongside human evaluation rather than as full replacements for teachers.