TRANSFORMATIVE ASSESSMENT IN ACADEMIC WRITING: AI-BASED FEEDBACK SYSTEMS WITH ADAPTIVE RUBRICS

This study analyzed the effect of AI feedback on students' writing skills, providing reliable and impartial assessments. The study assessed the capability of artificial intelligence to provide feedback and conducted a comparative analysis with conventional methods employed for enhancing student writing skills. Sixty-three students in the Academic Writing course at the Modern College of Business Science were selected as the sample, of which 14 actively participated in focused discussions. A combination of pre-tests, post-tests, and rubrics aligned with course objectives served as the primary research tools, ensuring the feedback remained relevant. One group received feedback based on AI, while another received traditional feedback. Additionally, qualitative data was collected through focused discussions to gain deeper insights. The results suggest combining adaptive rubrics with AI-based feedback can enhance assessment methods and retention. These findings are expected to significantly contribute to the future use of AI in education, as there is substantial potential to transform feedback and assessment across various educational contexts.