Ai-Based Automated Grading and Feedback Systems: Technologies, Challenges and Future Directions in Higher Education

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in higher education assessment, particularly through automated grading and feedback systems. These AI-powered tools are reshaping higher education by addressing inefficiencies, subjectivity, and scalability limitations associated with traditional assessment methods. The rapid expansion of postgraduate programs, online learning environments, and large-scale digital classrooms has created an urgent need for assessment solutions that are scalable, consistent, and pedagogically effective.
AI-based automated grading and feedback systems use machine learning, natural language processing, and deep learning techniques to evaluate student work and provide personalized feedback. This paper presents a comprehensive journal-level review of AI-driven grading systems, examining their historical development, methodological foundations, cross-disciplinary applications, educational benefits, ethical and technical challenges, and emerging research trends. The review finds that, when implemented responsibly with human oversight and transparent evaluation frameworks, AI-based assessment tools can significantly improve efficiency and support formative learning outcomes.