Concurrent Session #3: AI-Based Grading for Small Classes

Alternative Title

Artificial Intelligence (AI)-Based Grading for Small Classes

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 (2023 : Orlando, Fla.)

Location

Cape Florida C

Start Date

24-9-2023 2:00 PM

End Date

24-9-2023 2:30 PM

Publisher

University of Central Florida Libraries

Keywords:

AI grading; Small classes; Rubric-based assessment; Large Language Model; Educational technology

Subjects

Grading and marking (Students)--Computer-assisted instruction; Artificial intelligence--Educational applications; Grading and marking (Students)--Study and teaching; Grading and marking (Students)--Data processing; Artificial intelligence--Study and teaching

Description

This proposed discussion explores the idea of AI-based grading for small classes and the challenges it poses. While AI-based grading through supervised learning has shown success in large classes, its feasibility in small classes is limited due to insufficient training data. In such cases, utilizing a detailed rubric and leveraging a Large Language Model (LLM) becomes essential. This discussion aims to investigate the effectiveness and acceptability of this approach. By examining the advantages and limitations of rubric-based grading with LLM assistance, this discussion seeks to provide insights into alternative grading methods suitable for small classes.

Language

eng

Type

Presentation

Rights Statement

All Rights Reserved

Audience

Administrators, Faculty

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Sep 24th, 2:00 PM Sep 24th, 2:30 PM

Concurrent Session #3: AI-Based Grading for Small Classes

Cape Florida C

This proposed discussion explores the idea of AI-based grading for small classes and the challenges it poses. While AI-based grading through supervised learning has shown success in large classes, its feasibility in small classes is limited due to insufficient training data. In such cases, utilizing a detailed rubric and leveraging a Large Language Model (LLM) becomes essential. This discussion aims to investigate the effectiveness and acceptability of this approach. By examining the advantages and limitations of rubric-based grading with LLM assistance, this discussion seeks to provide insights into alternative grading methods suitable for small classes.