Concurrent Session #1: Harnessing Large Language Models for Automatic Grading and Hint Generation

Alternative Title

Harnessing Large Language Models for Automatic Grading and Hint Generation

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 A

Start Date

22-7-2024 1:00 PM

End Date

22-7-2024 1:30 PM

Publisher

University of Central Florida Libraries

Keywords:

Automatic grading; Hint generation; AI analytics; Guidance rubrics; Student performance

Subjects

Grading and marking (Students)--Computer-assisted instruction; Grading and marking (Students)--Computer programs; Artificial intelligence--Computer-assisted instruction; Grading and marking (Students)--Data processing; Grading and marking (Students)--Evaluation

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.

Language

eng

Type

Presentation

Rights Statement

All Rights Reserved

Audience

Faculty, Instructional designers, Educators

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Jul 22nd, 1:00 PM Jul 22nd, 1:30 PM

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.

Accessibility Statement

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