Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)

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Innovating Student Assessment with Artificial Intelligence

Authors
Sneha Singha1, *, Ganesh R. Pathak2
1Department of Computer Science Engineering, MIT Art, Design and Technology University, Pune, India
2Department of Computer Science Engineering, MIT Art, Design and Technology University, Pune, India
*Corresponding author. Email: singhasneha@gmail.com
Corresponding Author
Sneha Singha
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-978-0_44How to use a DOI?
Keywords
Artificial Intelligence; Automated Grading; Feedback Systems; Higher Education; Machine Learning; Natural Language Processing; Human-AI Collaboration
Abstract

With the advent of automated grading and feedback systems, application of artificial intelligence (AI) has revolutionized the evaluation practices in higher education. The educators’ efficiency can be enhanced by the AI feedbacks, the detailed feedback giving can be relieved from the educator’s shoulder, the time can be used effectively and student assignments can be handled in a more rapid way. Unlike the old methods which were totally dependent on the speed and some set of rules, the newly combined systems use Machine Learning (ML) and Natural Language Processing (NLP) to give the feedback meaning to the students and to provide a more deep-rooted insight to the feedback. At present, AI-based grading is being used in different fields, like STEM education, programming, and essay writing. But, there are still problems like making sure that the feedback will be personalized, handling the unbiasedness, and showing that the feedback will promote learning. This study, to which extent it is an effort to devise fair and efficient evaluation methods, surveys current developments, the issues that matter most, and the potential future directions, e.g., Human-AI collaboration and adaptive grading.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
Series
Advances in Engineering Research
Publication Date
31 December 2025
ISBN
978-94-6463-978-0
ISSN
2352-5401
DOI
10.2991/978-94-6463-978-0_44How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Sneha Singha
AU  - Ganesh R. Pathak
PY  - 2025
DA  - 2025/12/31
TI  - Innovating Student Assessment with Artificial Intelligence
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 513
EP  - 528
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_44
DO  - 10.2991/978-94-6463-978-0_44
ID  - Singha2025
ER  -