Using Artificial Intelligence to Extract Curriculum Insights from Student Evaluations of Teaching in Higher Education

Alternative Title

Using Artificial Intelligence (AI) to Extract Curriculum Insights from Student Evaluations of Teaching in Higher Education

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

Seminole B

Start Date

29-5-2025 9:00 AM

End Date

29-5-2025 9:25 AM

Publisher

University of Central Florida Libraries

Keywords:

Artificial intelligence; Curriculum insights; Student evaluations; Veterinary education; Sentiment analysis

Subjects

Student evaluation of curriculum; Curriculum evaluation; Competency-based education--Curricula--Evaluation; Artificial intelligence--Educational applications; Education, Higher--Curricula--Evaluation

Description

This project presents a structured, AI-enhanced framework for extracting actionable curriculum insights from student evaluations of teaching (EOT) within professional veterinary education. Set in a competency-based, integrated Doctor of Veterinary Medicine (DVM) program, the process begins by establishing the programmatic context, ensuring that teaching evaluation data is interpreted appropriately. The framework guides users through five key steps: categorizing qualitative feedback into core curriculum domains (Pace, Integration, Instructional Resources, Communication), selecting representative comments, assigning sentiment-based scores, identifying outliers, and generating structured administrator reports. AI-driven tools, including natural language processing and sentiment analysis, support the systematic classification and quantification of student feedback. This methodology promotes consistent, data-informed decisions for curriculum enhancement and instructional development. The process is replicable, scalable, and designed to support continuous quality improvement in veterinary and other health professions education settings.

Language

eng

Type

Presentation

Rights Statement

All Rights Reserved

Audience

Faculty; Students

This document is currently not available here.

Share

COinS
 
May 29th, 9:00 AM May 29th, 9:25 AM

Using Artificial Intelligence to Extract Curriculum Insights from Student Evaluations of Teaching in Higher Education

Seminole B

This project presents a structured, AI-enhanced framework for extracting actionable curriculum insights from student evaluations of teaching (EOT) within professional veterinary education. Set in a competency-based, integrated Doctor of Veterinary Medicine (DVM) program, the process begins by establishing the programmatic context, ensuring that teaching evaluation data is interpreted appropriately. The framework guides users through five key steps: categorizing qualitative feedback into core curriculum domains (Pace, Integration, Instructional Resources, Communication), selecting representative comments, assigning sentiment-based scores, identifying outliers, and generating structured administrator reports. AI-driven tools, including natural language processing and sentiment analysis, support the systematic classification and quantification of student feedback. This methodology promotes consistent, data-informed decisions for curriculum enhancement and instructional development. The process is replicable, scalable, and designed to support continuous quality improvement in veterinary and other health professions education settings.