In May 2023, the Writers Guild of America (WGA) went on strike against the Alliance of Motion Picture and Television Producers (AMPTP); two months later, the Screen Actors Guild-American Federation of Television and Radio Artists (SAG-AFTRA) followed suit. A key issue in both negotiations was generative artificial intelligence (GenAI)—specifically, the extent to which models like ChatGPT could replace human writers or replicate human voices and faces. The unions did win some protections, but the strikes have left lingering questions over what AI means for creative industries and the arts.
For those of us who teach in the arts, these questions loom large. How to prepare our students for creative careers in the age of AI? How to introduce them to the ethical complexities and deeper philosophical questions surrounding human vs. non-human artistic production? And how to balance developing students’ own creativity with building AI-related skills that (whether we like it or not) will be used in these industries?
Over the past two years, I have developed a final project for my course FTT 30706: Musical Theatre History that attempts to address these questions.1 The course primarily focuses on analyzing canonical musicals in relation to their historical context and the social, political, and cultural issues addressed by the shows.
For the final project, however, students use this knowledge as a foundation for creating a hypothetical new show in collaboration with GenAI. The primary goal of the project is to connect the history of the genre to its future, while exploring and critically evaluating how AI can be used in creative work.
This past semester, while participating in the Kaneb Center Course Design Academy (KCCDA), my personal goal was to increase the assignment’s level of transparency by ensuring the students understood how to do the project and how it connected to the course learning goals. In KCCDA, we devoted an early session to “Transparent Assignment Design” and the strong connection between communicating an assignment’s purpose, tasks, and criteria for success and students’ learning outcomes. While always helpful, transparency seems especially important in an experimental assignment that reaches beyond the immediate purview of course content.
For this assignment, students begin by pairing up with a classmate and brainstorming an idea for an entirely new musical, with shows and writers/composers we’ve studied serving as “inspirational touchstones.” The second part then asks them to work with text- and music-generating AI models to produce a scene and a song for their hypothetical musical. Along the way, I introduce basic prompt engineering and give plenty of time in class for working through issues that arise, and students are required to keep a log of AI interactions. The final product—which includes a team presentation and individual four–five-page papers—describes and evaluates their collaborations, reflects on what AI means for the future of musical theatre, and connects the project back to core course questions.
So far, the project has proved highly engaging and the AI-generated results decidedly mixed. Students report finding AI shockingly quick but frustrating to work with on creative pieces—it leans heavily on clichés and has trouble taking suggestions. Generating interesting, novel results can take hours of back-and-forth with the model. Students also commented on how asking it to create “in the style of” a famous composer or writer felt like plagiarism, since they could not stop the AI from directly lifting wording or musical phrases from material in its database.
Most came to the conclusion that they could not see AI fully replacing human creators in musical theatre anytime soon, nor do they want that to happen. They prefer to do it themselves.
These reflections contradict what seems to be the basis for much of the pedagogical frustration with AI—in other areas, students seem to prefer to have the AI do a task instead of doing it themselves. Creative, critically engaged projects might therefore help all faculty, regardless of discipline, to more effectively communicate the ethical quandaries of AI to students. AI’s artistic plagiarism feels like plagiarism in a way that unsettles students, while having it summarize readings or generate academic writing assignments often does not.
And in our current age of vibes, what could be a more persuasive argument?
Tarryn Chun is an associate professor in the Department of Film, Television, and Theatre and was a participant in the 2024–25 Kaneb Center Course Design Academy.
1 The course is a requirement for the FTT Musical Theatre Minor, and most students take it in their junior or senior years.
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Originally published by at learning.nd.edu on August 21, 2025.