AI-Based Automated Grading Systems for open book examination system: Implications for Assessment in Higher Education

In the rapidly evolving landscape of higher education, the integration of artificial intelligence (AI) into assessment methods presents a transformative potential for grading systems. This paper delves into the development and efficacy of an AI-based automated grading system tailored for open book examinations-a format increasingly adopted in higher education due to its reflection of real-world problem-solving skills. By leveraging advanced machine learning algorithms and natural language processing techniques, the proposed system automates the grading process, aiming to enhance objectivity, reliability, and scalability in assessments. We present a comprehensive architecture of the system, including algorithmic design, implementation methodologies, and mathematical underpinnings essential for processing and evaluating complex, open-ended student responses. Initial trials and evaluations demonstrate a promising alignment of automated grading results with human expert assessments, highlighting the system’s potential to significantly reduce grading time while maintaining high accuracy and fairness. Furthermore, this study explores the implications of integrating such AI-based systems into the broader educational assessment framework, considering factors like academic integrity, pedagogical effectiveness, and the shifting dynamics of student-teacher interactions. Through this exploration, the paper contributes valuable insights into the future of assessment in higher education, underscoring the pivotal role of AI in shaping equitable, efficient, and forward-thinking educational practices.