AI IN ASSESSMENT: AUTOMATED GRADING SYSTEMS, THEIR LIMITATIONS AND INTEGRATION INTO FORMATIVE AND SUMMATIVE ASSESSMENT

Abstract: The rapid advancement of artificial intelligence (AI) has significantly impacted various fields, including education. One of the most transformative applications of AI in this domain is the development of automated grading systems. These systems utilize machine learning (ML) and natural language processing (NLP) to assess student performance efficiently, accurately, and fairly. By automating the grading process, AI-driven systems alleviate the workload of educators, provide timely feedback to students, and enhance the overall assessment process. Additionally, they enable large-scale data analysis to identify learning patterns and offer personalized recommendations. However, despite their advantages, AI-based grading systems face several challenges, including biases in AI algorithms, lack of adaptability to new pedagogical methods, and ethical concerns related to data privacy and fairness. This paper explores the role of AI in automated grading, its benefits, limitations, and ethical considerations, and discusses strategies for integrating AI into both formative and summative assessments to improve educational outcomes.