AutoGrader+: Automated Grading of Typed Answer Sheets Using Machine Learning With Human-Aligned Scoring

Gade Maria Reshvika Reddy; Kasala Sai Nikhitha; Painala Nikhil; Majeti Srinadh Swamy

doi:10.2991/978-94-6463-978-0_23

<Previous Article In Volume

Next Article In Volume>

AutoGrader+: Automated Grading of Typed Answer Sheets Using Machine Learning With Human-Aligned Scoring

Authors

Gade Maria Reshvika Reddy¹^{, *}, Kasala Sai Nikhitha¹, Painala Nikhil¹, Majeti Srinadh Swamy¹

¹School of Engineering, Anurag University, Hyderabad, Telangana, India

^*Corresponding author.

Corresponding Author

Gade Maria Reshvika Reddy

Available Online 31 December 2025.

DOI: 10.2991/978-94-6463-978-0_23 How to use a DOI?
Keywords: Automated Grading; Natural Language Processing; Sentence-BERT; Human-AI Score Alignment; Educational Technology
Abstract: AutoGrader+ is artificial intelligence technology that evaluates PDF answer sheets that contain written descriptive answers. AI techniques such as TF-IDF with Cosine Similarity and other NLP techniques such as Sentence-BERT are employed to process the text, extract the answers, and compare them against model answers. An optional machine learning regression model is used to allocate marks based on answer length, keyword detection, and the similarity score. Predictions are also made based on other features. Fairness measures such as MAE and Pearson Correlation are used to validate AI marks against marks given by teachers to ascertain the systems reliability. The system evaluates the answers in bulk and generates final reports, making the grading faster, unbiased, transparent, and scalable. The solution also integrates closely with human evaluation practices.
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.

Download article (PDF)

<Previous Article In Volume

Next Article In Volume>

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_23 How to use a DOI?
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

ris enw bib

TY  - CONF
AU  - Gade Maria Reshvika Reddy
AU  - Kasala Sai Nikhitha
AU  - Painala Nikhil
AU  - Majeti Srinadh Swamy
PY  - 2025
DA  - 2025/12/31
TI  - AutoGrader+: Automated Grading of Typed Answer Sheets Using Machine Learning With Human-Aligned Scoring
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 250
EP  - 256
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_23
DO  - 10.2991/978-94-6463-978-0_23
ID  - Reddy2025
ER  -

download .riscopy to clipboard