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Area of Science:

  • Psychometrics
  • Artificial Intelligence
  • Educational Assessment

Background:

  • Artificial Intelligence (AI) enhances assessment efficiency and scalability.
  • AI adoption in test construction is limited among researchers and practitioners.
  • This study reviews AI applications in test construction and proposes guidelines.

Purpose of the Study:

  • Critically review current AI-based applications in test construction.
  • Propose practical guidelines for AI in test development and calibration.
  • Address risks and maximize benefits of AI in assessment.

Main Methods:

  • Comprehensive literature review of AI in test construction.
  • Focus on item development and calibration processes.
  • Inclusion of real-world examples for practical implementation.

Main Results:

  • Evolving best practices for AI in test development necessitate human oversight.
  • Effective AI item generation requires quality data, alignment, and validation.
  • Calibration involves construct validity, prompt engineering, and model fit evaluation.

Conclusions:

  • A practical guide for generative AI in test development and calibration is proposed.
  • Guidelines address validity, reliability, and fairness challenges.
  • Promotes responsible and effective implementation of AI in assessment.