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This study reviewed automatic item generation (AIG) applications for educational assessments from 2010 to 2024. The analysis included 71 articles and focused on examining types of generated items and assessments, technical approaches, and evaluation models. The results showed that most generated items related to multiple choice questions, and the generated assessments were mainly about computer and medical sciences at college and vocational levels. The technical approaches were classified into four categories: feature engineering, architecture engineering, objective engineering, and prompt engineering. The models employed for evaluation were defined as manual annotation, man-machine collaborative evaluation, item analysis, Turing test, and value-added models. These findings provided knowledge and understanding to researchers and practitioners, showing the significance of expanding research focus, maintaining the theoretical foundation about educational assessments, and enhancing evaluation evidence for future AIG research.
Song et al. (Mon,) studied this question.