@inproceedings{palomino-etal-2025-mitigating,
title = "Mitigating Bias in Item Retrieval for Enhancing Exam Assembly in Vocational Education Services",
author = {Palomino, Alonso and
Fischer, Andreas and
Buschh{\"u}ter, David and
Roller, Roland and
Pinkwart, Niels and
Paassen, Benjamin},
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.16/",
doi = "10.18653/v1/2025.naacl-industry.16",
pages = "183--193",
ISBN = "979-8-89176-194-0",
abstract = "In education, high-quality exams must cover broad specifications across diverse difficulty levels during the assembly and calibration of test items to effectively measure examinees' competence. However, balancing the trade-off of selecting relevant test items while fulfilling exam specifications without bias is challenging, particularly when manual item selection and exam assembly rely on a pre-validated item base. To address this limitation, we propose a new mixed-integer programming re-ranking approach to improve relevance, while mitigating bias on an industry-grade exam assembly platform. We evaluate our approach by comparing it against nine bias mitigation re-ranking methods in 225 experiments on a real-world benchmark data set from vocational education services. Experimental results demonstrate a 17{\%} relevance improvement with a 9{\%} bias reduction when integrating sequential optimization techniques with improved contextual relevance augmentation and scoring using a large language model. Our approach bridges information retrieval and exam assembly, enhancing the human-in-the-loop exam assembly process while promoting unbiased exam design"
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%0 Conference Proceedings
%T Mitigating Bias in Item Retrieval for Enhancing Exam Assembly in Vocational Education Services
%A Palomino, Alonso
%A Fischer, Andreas
%A Buschhüter, David
%A Roller, Roland
%A Pinkwart, Niels
%A Paassen, Benjamin
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F palomino-etal-2025-mitigating
%X In education, high-quality exams must cover broad specifications across diverse difficulty levels during the assembly and calibration of test items to effectively measure examinees’ competence. However, balancing the trade-off of selecting relevant test items while fulfilling exam specifications without bias is challenging, particularly when manual item selection and exam assembly rely on a pre-validated item base. To address this limitation, we propose a new mixed-integer programming re-ranking approach to improve relevance, while mitigating bias on an industry-grade exam assembly platform. We evaluate our approach by comparing it against nine bias mitigation re-ranking methods in 225 experiments on a real-world benchmark data set from vocational education services. Experimental results demonstrate a 17% relevance improvement with a 9% bias reduction when integrating sequential optimization techniques with improved contextual relevance augmentation and scoring using a large language model. Our approach bridges information retrieval and exam assembly, enhancing the human-in-the-loop exam assembly process while promoting unbiased exam design
%R 10.18653/v1/2025.naacl-industry.16
%U https://aclanthology.org/2025.naacl-industry.16/
%U https://doi.org/10.18653/v1/2025.naacl-industry.16
%P 183-193
Markdown (Informal)
[Mitigating Bias in Item Retrieval for Enhancing Exam Assembly in Vocational Education Services](https://aclanthology.org/2025.naacl-industry.16/) (Palomino et al., NAACL 2025)
ACL
- Alonso Palomino, Andreas Fischer, David Buschhüter, Roland Roller, Niels Pinkwart, and Benjamin Paassen. 2025. Mitigating Bias in Item Retrieval for Enhancing Exam Assembly in Vocational Education Services. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track), pages 183–193, Albuquerque, New Mexico. Association for Computational Linguistics.