Exploring the Potential of Generative AI for Exam Question Development
Alternative Title
Exploring the Potential of Generative Artificial Intelligence (GenAI) for Exam Question Development
Contributor
University of Central Florida. Faculty Center for Teaching and Learning; University of Central Florida. Division of Digital Learning; Teaching and Learning with AI Conference (2024 : Orlando, Fla.)
Location
Seminole D
Start Date
23-7-2024 12:00 PM
End Date
23-7-2024 12:30 PM
Publisher
University of Central Florida Libraries
Keywords:
Exam question development; Generative AI; Professional certification; AI-assisted methods; Educational technology
Subjects
Artificial intelligence--Educational applications; Examinations--Computer-assisted instruction; Artificial intelligence--Research; Generative Exam System (Computer system); Artificial intelligence--Methodology
Description
This presentation explores the use of generative AI in developing exam questions for professional certification exams. It focuses on assessing the efficiency and potential of AI-assisted development, providing a comprehensive comparison with traditional, human-driven methods. Our objective is to highlight AI’s capacity to streamline the exam question development process, while maintaining or potentially enhancing the quality and fairness of exam questions.
Language
eng
Type
Presentation
Rights Statement
All Rights Reserved
Audience
Faculty, Administrators, Instructional designers
Recommended Citation
Ouazzani, Mami; Sivo, Stephen A.; and Narkiewicz, Nicole, "Exploring the Potential of Generative AI for Exam Question Development" (2024). Teaching and Learning with AI Conference Presentations. 47.
https://stars.library.ucf.edu/teachwithai/2024/tuesday/47
Exploring the Potential of Generative AI for Exam Question Development
Seminole D
This presentation explores the use of generative AI in developing exam questions for professional certification exams. It focuses on assessing the efficiency and potential of AI-assisted development, providing a comprehensive comparison with traditional, human-driven methods. Our objective is to highlight AI’s capacity to streamline the exam question development process, while maintaining or potentially enhancing the quality and fairness of exam questions.