@inproceedings{yarmohammadtoosky-etal-2025-enhancing,
title = "Enhancing Security and Strengthening Defenses in Automated Short-Answer Grading Systems",
author = "Yarmohammadtoosky, Sahar and
Zhou, Yiyun and
Yaneva, Victoria and
Baldwin, Peter and
Rezayi, Saed and
Clauser, Brian and
Harik, Polina",
editor = {Kochmar, Ekaterina and
Alhafni, Bashar and
Bexte, Marie and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Tack, Ana{\"i}s and
Yaneva, Victoria and
Yuan, Zheng},
booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.bea-1.60/",
doi = "10.18653/v1/2025.bea-1.60",
pages = "830--840",
ISBN = "979-8-89176-270-1",
abstract = "This study examines vulnerabilities in transformer-based automated short-answer grading systems used in medical education, with a focus on how these systems can be manipulated through adversarial gaming strategies. Our research identifies three main types of gaming strategies that exploit the system{'}s weaknesses, potentially leading to false positives. To counteract these vulnerabilities, we implement several adversarial training methods designed to enhance the system{'}s robustness. Our results indicate that these methods significantly reduce the susceptibility of grading systems to such manipulations, especially when combined with ensemble techniques like majority voting and Ridge regression, which further improve the system{'}s defense against sophisticated adversarial inputs. Additionally, employing large language models suchasGPT-4with varied prompting techniques has shown promise in recognizing and scoring gaming strategies effectively. The findings underscore the importance of continuous improvements in AI-driven educational tools to ensure their reliability and fairness in high-stakes settings."
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<abstract>This study examines vulnerabilities in transformer-based automated short-answer grading systems used in medical education, with a focus on how these systems can be manipulated through adversarial gaming strategies. Our research identifies three main types of gaming strategies that exploit the system’s weaknesses, potentially leading to false positives. To counteract these vulnerabilities, we implement several adversarial training methods designed to enhance the system’s robustness. Our results indicate that these methods significantly reduce the susceptibility of grading systems to such manipulations, especially when combined with ensemble techniques like majority voting and Ridge regression, which further improve the system’s defense against sophisticated adversarial inputs. Additionally, employing large language models suchasGPT-4with varied prompting techniques has shown promise in recognizing and scoring gaming strategies effectively. The findings underscore the importance of continuous improvements in AI-driven educational tools to ensure their reliability and fairness in high-stakes settings.</abstract>
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%0 Conference Proceedings
%T Enhancing Security and Strengthening Defenses in Automated Short-Answer Grading Systems
%A Yarmohammadtoosky, Sahar
%A Zhou, Yiyun
%A Yaneva, Victoria
%A Baldwin, Peter
%A Rezayi, Saed
%A Clauser, Brian
%A Harik, Polina
%Y Kochmar, Ekaterina
%Y Alhafni, Bashar
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-270-1
%F yarmohammadtoosky-etal-2025-enhancing
%X This study examines vulnerabilities in transformer-based automated short-answer grading systems used in medical education, with a focus on how these systems can be manipulated through adversarial gaming strategies. Our research identifies three main types of gaming strategies that exploit the system’s weaknesses, potentially leading to false positives. To counteract these vulnerabilities, we implement several adversarial training methods designed to enhance the system’s robustness. Our results indicate that these methods significantly reduce the susceptibility of grading systems to such manipulations, especially when combined with ensemble techniques like majority voting and Ridge regression, which further improve the system’s defense against sophisticated adversarial inputs. Additionally, employing large language models suchasGPT-4with varied prompting techniques has shown promise in recognizing and scoring gaming strategies effectively. The findings underscore the importance of continuous improvements in AI-driven educational tools to ensure their reliability and fairness in high-stakes settings.
%R 10.18653/v1/2025.bea-1.60
%U https://aclanthology.org/2025.bea-1.60/
%U https://doi.org/10.18653/v1/2025.bea-1.60
%P 830-840
Markdown (Informal)
[Enhancing Security and Strengthening Defenses in Automated Short-Answer Grading Systems](https://aclanthology.org/2025.bea-1.60/) (Yarmohammadtoosky et al., BEA 2025)
ACL