Student and Instructor Attitudes Towards Generative AI in Murray State CSIS
Academic Level at Time of Presentation
Senior
Major
Computer Science
List all Project Mentors & Advisor(s)
Dr. Jason T. Owen
Presentation Format
Oral Presentation
Abstract/Description
A programming assignment submitted by a student who used AI has the possibility of concealing how much that student understands about the concepts and skills being assessed, just as a grade or feedback generated by AI can conceal how much an instructor has reviewed the work. As generative AI enters both sides of academic assessment, permission to use it raises questions regarding disclosure, verifying its output, and resolving who is responsible for errors. Separate questionnaires examined how twelve students and seven instructors from Murray State University’s Department of Computer Science and Information Systems described their experiences and evaluated shared academic situations. Students commonly reported that their AI use mostly consisted of seeking explanations and debugging help, while instructors emphasized that their use was for coursework preparation. A central tension was with disclosure of AI use, specifically with students supporting expectations to identify AI contributions, check AI output, and revise their own work, yet reporting that guidance and uneven descriptions of assistance were provided by instructors. Human oversight was also a shared expectation, as most respondents in both groups preferred independent review of work from instructors rather than spot-checking results. The findings identify a practical challenge for CSIS and other departments with similar situations: turning support for responsible AI use into clear expectations for what students must understand and explain, and the expectations instructors should follow regarding their AI use and disclosure.
Fall Scholars Week 2026
Honors College Senior Thesis Presentations
Student and Instructor Attitudes Towards Generative AI in Murray State CSIS
A programming assignment submitted by a student who used AI has the possibility of concealing how much that student understands about the concepts and skills being assessed, just as a grade or feedback generated by AI can conceal how much an instructor has reviewed the work. As generative AI enters both sides of academic assessment, permission to use it raises questions regarding disclosure, verifying its output, and resolving who is responsible for errors. Separate questionnaires examined how twelve students and seven instructors from Murray State University’s Department of Computer Science and Information Systems described their experiences and evaluated shared academic situations. Students commonly reported that their AI use mostly consisted of seeking explanations and debugging help, while instructors emphasized that their use was for coursework preparation. A central tension was with disclosure of AI use, specifically with students supporting expectations to identify AI contributions, check AI output, and revise their own work, yet reporting that guidance and uneven descriptions of assistance were provided by instructors. Human oversight was also a shared expectation, as most respondents in both groups preferred independent review of work from instructors rather than spot-checking results. The findings identify a practical challenge for CSIS and other departments with similar situations: turning support for responsible AI use into clear expectations for what students must understand and explain, and the expectations instructors should follow regarding their AI use and disclosure.