A Review of Automatic Item Generation Techniques Leveraging Large Language Models
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| Title: | A Review of Automatic Item Generation Techniques Leveraging Large Language Models |
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| Language: | English |
| Authors: | Bin Tan (ORCID |
| Source: | International Journal of Assessment Tools in Education. 2025 12(2):317-340. |
| Availability: | International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://dergipark.org.tr/en/pub/ijate |
| Peer Reviewed: | Y |
| Page Count: | 24 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Information Analyses |
| Descriptors: | Artificial Intelligence, Test Items, Automation, Test Format, Test Validity, Test Reliability |
| ISSN: | 2148-7456 |
| Abstract: | This study reviews existing research on the use of large language models (LLMs) for automatic item generation (AIG). We performed a comprehensive literature search across seven research databases, selected studies based on predefined criteria, and summarized 60 relevant studies that employed LLMs in the AIG process. We identified the most commonly used LLMs in current AIG literature, their specific applications in the AIG process, and the characteristics of the generated items. We found that LLMs are flexible and effective in generating various types of items across different languages and subject domains. However, many studies have overlooked the quality of the generated items, indicating a lack of a solid educational foundation. Therefore, we share two suggestions to enhance the educational foundation for leveraging LLMs in AIG, advocating for interdisciplinary collaborations to exploit the utility and potential of LLMs. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1476463 |
| Database: | ERIC |
| Abstract: | This study reviews existing research on the use of large language models (LLMs) for automatic item generation (AIG). We performed a comprehensive literature search across seven research databases, selected studies based on predefined criteria, and summarized 60 relevant studies that employed LLMs in the AIG process. We identified the most commonly used LLMs in current AIG literature, their specific applications in the AIG process, and the characteristics of the generated items. We found that LLMs are flexible and effective in generating various types of items across different languages and subject domains. However, many studies have overlooked the quality of the generated items, indicating a lack of a solid educational foundation. Therefore, we share two suggestions to enhance the educational foundation for leveraging LLMs in AIG, advocating for interdisciplinary collaborations to exploit the utility and potential of LLMs. |
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| ISSN: | 2148-7456 |
