Asee peer logo

A Custom Generative AI Chatbot as a Course Resource

Download Paper |

Conference

2024 ASEE Annual Conference & Exposition

Location

Portland, Oregon

Publication Date

June 23, 2024

Start Date

June 23, 2024

End Date

July 12, 2024

Conference Session

The Best of Computers in Education Division (COED)

Tagged Division

Computers in Education Division (COED)

Permanent URL

https://peer.asee.org/46433

Request a correction

Paper Authors

author page

Yutong Ai University of Michigan

author page

Maya Baveja University of Michigan

author page

Akanksha Girdhar University of Michigan

author page

Melina O'Dell University of Michigan

biography

Andrew Deorio University of Michigan Orcid 16x16 orcid.org/0000-0001-5653-5109

visit author page

Andrew DeOrio is a teaching faculty and Associate Chair for Undergraduate Affairs at the University of Michigan and a consultant for web projects. His research interests are in engineering education and interdisciplinary computing. His teaching has been recognized with the Provost's Teaching Innovation Prize, and he has three times been named Professor of the Year by the students in his department. Andrew is trying to visit every U.S. National Park.

visit author page

Download Paper |

Abstract

Generative AI (GenAI) chatbots have taken the world by storm with their ability to summarize information and produce complex natural language responses. At the same time, the demand for computer science education has grown enormously.

We developed a custom GenAI chatbot and released it to over 700 students to assist with a course programming project. The tool was trained on the project specification, lecture content, lab materials, and past course forum posts relevant to the project. The course is a high-enrollment upper-level computer science elective at a large public research university. The project is a simple distributed system using processes, threads, and sockets. The goal of the tool was to provide an additional course resource that supplies valuable information to students comparable to advice they would receive from course instructors.

We measured the effectiveness of our tool with a student survey, evaluation by course instructors, and comparison with a state-of-the-art general purpose chatbot. The overall survey data indicated high rates of correctness and helpfulness in the Bot responses. We found that hallucination was not common, and most incorrect responses were identifiable by students. The Bot also performed better than general purpose bots for project-specific help.

Our experience can provide insights for faculty using GenAI to assist students in their courses. A customized chatbot can be helpful to students and augment traditional course resources.

Ai, Y., & Baveja, M., & Girdhar, A., & O'Dell, M., & Deorio, A. (2024, June), A Custom Generative AI Chatbot as a Course Resource Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/46433

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2024 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015