AI-Based Automated Grading Systems: Opportunities, Challenges, and Future Directions

Junyao Wang; Yasmin Hussain; Xijia Zhang; Chencheng Mao; Zhao Wu

doi:10.5281/zenodo.15534471

Authors

Junyao Wang City Graduate School, City University Malaysia, Kuala Lumpur, Malaysia Author
Yasmin Hussain City Graduate School, City University Malaysia, Kuala Lumpur, Malaysia Author
Xijia Zhang City Graduate School, Universiti SEGi Malaysia, Kuala Lumpur, Malaysia Author
Chencheng Mao City Graduate School, City University Malaysia, Kuala Lumpur, Malaysia Author
Zhao Wu Shanghai Industrial and Commercial Polytechnic, Xinfeng North Road, Huaxin Town, Qingpu District, Shanghai, China Author

DOI:

https://doi.org/10.5281/zenodo.15534471

Keywords:

AI grading, automated assessment, artificial intelligence, machine learning, education technology

Abstract

Automated grading systems powered by artificial intelligence (AI) have significantly transformed education by enhancing efficiency, reducing grading biases, and providing instant feedback. This paper explores the opportunities, challenges, and future directions of AI-based automated grading systems. Opportunities include increased scalability, cost reduction, and enhanced student learning experiences. However, challenges such as bias in AI models, lack of human touch, ethical concerns, and data security issues remain prevalent. Future directions involve the integration of explainable AI, improved natural language processing (NLP) capabilities, and a focus on enhanced fairness and transparency. This paper concludes by emphasizing the need for interdisciplinary collaboration to optimize AI-driven grading solutions.

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