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Three Modeling Applications to Promote Automatic Item Generation for Examinations in Dentistry.

Hollis Lai1, Mark J Gierl2, B Ellen Byrne2

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PubMed
Summary

Automatic item generation (AIG) offers a cost-effective solution for creating dental examination questions. This technology enables a few experts to produce numerous items efficiently, meeting the growing demand for assessments.

Keywords:
assessmentdental educationitem generationitem writingtest development

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Area of Science:

  • Dental Education
  • Medical Informatics
  • Assessment Science

Background:

  • Traditional test item development for dentistry is resource-intensive, relying on individual content experts to create limited numbers of items.
  • The increasing demand for dental examinations necessitates more efficient and scalable item creation methods.

Purpose of the Study:

  • To describe and illustrate systematic approaches for generating large volumes of test items for dentistry examinations.
  • To showcase Automatic Item Generation (AIG) as a method to enhance item production efficiency.

Main Methods:

  • Utilized three modeling approaches for Automatic Item Generation (AIG): item cloning, cognitive modeling, and image-anchored modeling.
  • Integrated domain expertise of content specialists with computer technology to generate multiple-choice test items.
  • Combined expertise of two content specialists with AIG technology.

Main Results:

  • Generated a total of 5,467 new test items.
  • Demonstrated AIG's capability in item creation through content substitution, cognitive response modeling, and image-linked item generation.
  • Successfully produced a large volume of items efficiently.

Conclusions:

  • Automatic Item Generation (AIG) presents a viable and scalable solution for meeting the demand for dental test items.
  • The described AIG methods are adaptable for various item types and can be applied beyond dentistry.
  • Further research into AIG applications in dental education is warranted.