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