@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://preview.aclanthology.org/fix-sig-urls/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"
}