Alternative Title
Leveraging Artificial Intelligence (AI) for Scalable and Effective Student Evaluation in Large-Scale Online Courses
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 (2025 : Orlando, Fla.)
Location
Sawgrass
Start Date
29-5-2025 11:30 AM
End Date
29-5-2025 11:55 AM
Publisher
University of Central Florida Libraries
Keywords:
Online courses; Student evaluation; Artificial intelligence; Personalized feedback; Scalable assessment
Subjects
Artificial intelligence--Computer-assisted instruction; Students--Evaluation; Grading and marking (Students)--Computer-assisted instruction; Artificial intelligence--Educational applications; Distance education--Evaluation
Description
We have developed two groundbreaking online courses, "Sex from Molecules to Elephants" and "Sensing and Movement from Waves to Whales", filmed in extraordinary locations such as the Serengeti, Yellowstone, Sumatra, and Iceland. These courses engage thousands of students annually but pose unique challenges for testing and evaluation due to their scale. Traditional methods fail to provide personalized, timely, and meaningful feedback.
Language
eng
Type
Presentation
Format
application/pdf
Rights Statement
All Rights Reserved
Audience
Faculty
Recommended Citation
Brandeis, Michael, "Leveraging AI for Scalable and Effective Student Evaluation in Large-Scale Online Courses" (2025). Teaching and Learning with AI Conference Presentations. 58.
https://stars.library.ucf.edu/teachwithai/2025/thursday/58
Leveraging AI for Scalable and Effective Student Evaluation in Large-Scale Online Courses
Sawgrass
We have developed two groundbreaking online courses, "Sex from Molecules to Elephants" and "Sensing and Movement from Waves to Whales", filmed in extraordinary locations such as the Serengeti, Yellowstone, Sumatra, and Iceland. These courses engage thousands of students annually but pose unique challenges for testing and evaluation due to their scale. Traditional methods fail to provide personalized, timely, and meaningful feedback.