AI in Education: Measuring the Impact of Chatbots on Student Outcomes

Alternative Title

Artificial Intelligence (AI) in Education: Measuring the Impact of Chatbots on Student Outcomes

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

Universal Center

Start Date

29-5-2025 4:00 PM

End Date

29-5-2025 5:00 PM

Publisher

University of Central Florida Libraries

Keywords:

Chatbot effectiveness; Student engagement; Educational outcomes; Quasi-experimental design; Information systems model

Subjects

Artificial intelligence--Educational applications; Artificial intelligence--Study and teaching; Chatbots; Educational technology--Research; Students--Attitudes--Research

Description

This poster complements a presentation on creating a chatbot for students as a class resource guide. It presents findings from a research project on student outcomes after deploying the chatbot. A quasi-experimental design measured student empowerment and engagement across multiple sections of the same class, with half having access to the bot and half not. Those with access also completed a survey based on the Information Systems Continuance Model, along with several control variables.

Language

eng

Type

Poster

Rights Statement

All Rights Reserved

Audience

Faculty; Students

This document is currently not available here.

Share

COinS
 
May 29th, 4:00 PM May 29th, 5:00 PM

AI in Education: Measuring the Impact of Chatbots on Student Outcomes

Universal Center

This poster complements a presentation on creating a chatbot for students as a class resource guide. It presents findings from a research project on student outcomes after deploying the chatbot. A quasi-experimental design measured student empowerment and engagement across multiple sections of the same class, with half having access to the bot and half not. Those with access also completed a survey based on the Information Systems Continuance Model, along with several control variables.