Automatic item generation: foundations and machine learning-based approaches for assessments

This mini review examines automatic item generation (AIG) methods for creating educational assessment questions, covering traditional rule-based approaches and machine learning/NLP techniques. The paper discusses item model development, quality evaluation frameworks, and challenges in scaling AIG for K-12 assessments across subject areas including math, science, and language arts.

This mini review summarizes the current state of knowledge about automatic item generation in the context of educational assessment and discusses key points in the item generation pipeline. Assessment is critical in all learning systems and digitalized assessments have shown significant growth over the last decade. This leads to an urgent need to generate more items in a fast and efficient manner. Continuous improvements in computational power and advancements in methodological approaches, speci

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