Concurrent Session #1: Harnessing Large Language Models for Automatic Grading and Hint Generation
Location
Seminole A
Start Date
22-7-2024 1:00 PM
End Date
22-7-2024 1:30 PM
Description
Due to hallucinations and inconsistencies, using ChatGPT directly for grading assignments typically yields poor results. However, by employing sequences of specific prompts and detailed “guidance rubrics,” we can enhance the grading quality of AI. Instructors can also request compiled, detailed analytics that pinpoint common student errors and track improvements over time. These analytics help instructors improve guidance rubrics and steer the development of learning material.
Recommended Citation
Bohacek, Stephan, "Concurrent Session #1: Harnessing Large Language Models for Automatic Grading and Hint Generation" (2024). Teaching and Learning with AI Conference Presentations. 3.
https://stars.library.ucf.edu/teachwithai/2024/monday/3
Concurrent Session #1: Harnessing Large Language Models for Automatic Grading and Hint Generation
Seminole A
Due to hallucinations and inconsistencies, using ChatGPT directly for grading assignments typically yields poor results. However, by employing sequences of specific prompts and detailed “guidance rubrics,” we can enhance the grading quality of AI. Instructors can also request compiled, detailed analytics that pinpoint common student errors and track improvements over time. These analytics help instructors improve guidance rubrics and steer the development of learning material.