article · Ingénierie des systèmes d information
In the context of competitive examinations, the number of items can be extremely high.In such situations, the item review process remains essential.It enables designers to consider the complexity and scope of the assessment by reviewing each item and distractor.Identifying redundancies becomes even more critical in this context, as the variety and quality of items are crucial to ensure a fair and equitable assessment of candidates' skills.This article aims to propose an artificial intelligence model specifically designed to efficiently detect and correct these redundancies in multiple-choice tests.By combining the human expertise in item review with the massive data processing capabilities of AI, we aim to improve the quality and reliability of competitive exams, while optimizing the time and resources required for their development.
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DOI: 10.18280/isi.290430
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