A Comprehensive Review on Automated Grading Systems in STEM Using AI Techniques

Le Ying Tan, Shiyu Hu, Darren J. Yeo and Kang Hao Cheong ()
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Le Ying Tan: College of Computing and Data Science (CCDS), Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
Shiyu Hu: School of Physical and Mathematical Sciences (SPMS), Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
Darren J. Yeo: Division of Psychology, School of Social Sciences, Nanyang Technological University, 48 Nanyang Avenue, Singapore 639818, Singapore
Kang Hao Cheong: College of Computing and Data Science (CCDS), Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore

Mathematics, 2025, vol. 13, issue 17, 1-23

Abstract: This paper presents a comprehensive analysis of artificial intelligence-powered automated grading systems (AI AGSs) in STEM education, systematically examining their algorithmic foundations, mathematical modeling approaches, and quantitative evaluation methodologies. AI AGSs enhance grading efficiency by providing large-scale, instant feedback and reducing educators’ workloads. Compared to traditional manual grading, these systems improve consistency and scalability, supporting a wide range of assessment types, from programming assignments to open-ended responses. This paper provides a structured taxonomy of AI techniques including logistic regression, decision trees, support vector machines, convolutional neural networks, transformers, and generative models, analyzing their mathematical formulations and performance characteristics. It further examines critical challenges, such as user trust issues, potential biases, and students’ over-reliance on automated feedback, alongside quantitative evaluation using precision, recall, F1-score, and Cohen’s Kappa metrics. The analysis includes feature engineering strategies for diverse educational data types and prompt engineering methodologies for large language models. Lastly, we highlight emerging trends, including explainable AI and multimodal assessment systems, offering educators and researchers a mathematical foundation for understanding and implementing AI AGSs into educational practices.

Keywords: automated grading systems; machine learning algorithms; mathematical modeling; AI-powered assessment; STEM education; educational technology (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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