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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
Language: English
Authors: Bin Tan (ORCID 0000-0001-6717-5620), Nour Armoush (ORCID 0009-0008-2310-5098), Elisabetta Mazzullo (ORCID 0009-0008-4847-9934), Okan Bulut (ORCID 0000-0001-5853-1267), Mark J. Gierl (ORCID 0000-0001-6717-5620)
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
FullText Text:
  Availability: 0
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IllustrationInfo
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  Data: A Review of Automatic Item Generation Techniques Leveraging Large Language Models
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  Data: <searchLink fieldCode="AR" term="%22Bin+Tan%22">Bin Tan</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6717-5620">0000-0001-6717-5620</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nour+Armoush%22">Nour Armoush</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0008-2310-5098">0009-0008-2310-5098</externalLink>)<br /><searchLink fieldCode="AR" term="%22Elisabetta+Mazzullo%22">Elisabetta Mazzullo</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0008-4847-9934">0009-0008-4847-9934</externalLink>)<br /><searchLink fieldCode="AR" term="%22Okan+Bulut%22">Okan Bulut</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5853-1267">0000-0001-5853-1267</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mark+J%2E+Gierl%22">Mark J. Gierl</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6717-5620">0000-0001-6717-5620</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Assessment+Tools+in+Education%22"><i>International Journal of Assessment Tools in Education</i></searchLink>. 2025 12(2):317-340.
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  Data: 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
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  Data: 24
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Items%22">Test Items</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Format%22">Test Format</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Validity%22">Test Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Reliability%22">Test Reliability</searchLink>
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  Data: 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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      – Text: English
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      Pagination:
        PageCount: 24
        StartPage: 317
    Subjects:
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        Type: general
      – SubjectFull: Test Items
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      – SubjectFull: Automation
        Type: general
      – SubjectFull: Test Format
        Type: general
      – SubjectFull: Test Validity
        Type: general
      – SubjectFull: Test Reliability
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      – TitleFull: A Review of Automatic Item Generation Techniques Leveraging Large Language Models
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            NameFull: Bin Tan
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            NameFull: Nour Armoush
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              Y: 2025
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