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Prompt Pattern Engineering for Test Question Mapping Using ChatGPT: A Cross-Sectional Study.

Jennifer L Babin¹, Hanna Raber¹, T Joseph Mattingly Ii¹

¹Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, UT, USA.

American Journal of Pharmaceutical Education

|August 17, 2024

Summary

This summary is machine-generated.

This study developed prompt engineering for test question mapping, finding that ChatGPT achieved 88.1% agreement with human faculty, demonstrating its effectiveness in educational assessments.

Keywords:

Artificial intelligence Curriculum mapping Prompt engineering

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Area of Science:

Educational Technology
Artificial Intelligence in Education
Curriculum Development

Background:

Effective test question mapping is crucial for aligning assessments with educational objectives and accreditation standards.
Current methods often rely on manual processes by faculty, which can be time-consuming.

Purpose of the Study:

To develop a prompt engineering procedure for test question mapping using ChatGPT.
To evaluate the effectiveness of ChatGPT for test question mapping compared to human faculty.

Main Methods:

A cross-sectional study involved 139 test questions from an Integrated Pharmacotherapeutics course.
Faculty mapped questions to module objectives and Accreditation Council for Pharmacy Education Standards 2016.
Prompt engineering procedures were developed for ChatGPT, and its mapping results were compared to human faculty consensus.

Main Results:

ChatGPT demonstrated 88.1% agreement with human faculty mapping of test questions.
Direct agreement with human consensus occurred in 68.0% of cases.
ChatGPT showed higher accuracy when human faculty fully agreed on the mapping decision.

Conclusions:

Prompt engineering offers a practical approach for utilizing ChatGPT in educational settings for efficient test question and outcomes mapping.
This methodology can assist college assessment and curriculum committees.