BRIGHAM YOUNG UNIVERSITY
The integration of Generative Artificial Intelligence (GenAI) into the development of theatre/drama curricula offers a multitude of opportunities, and simultaneously presents complex challenges. This article explores the potential benefits and complications of utilizing AI in the development of theatre/drama curriculum, emphasizing the critical need for innovative research practices to maximize AI's effectiveness in pedagogy. I assert that ongoing exploration and research is imperative to identify best practices in the nascent relationship between AI and educators. I describe the efforts of BYU Theatre Education faculty to explore AI-driven content curation and recommendation algorithms as a means of strengthening curriculum design. I assert that this can only happen when Artificial Intelligence directives are paired with informed, embodied, decision-making processes that preserve the richness of drama pedagogies.
BRIGHAM YOUNG UNIVERSITY
In the fall of 2023 faculty from the Brigham Young University Theatre and Media Arts Department participated in a university faculty training about Generative Artificial Intelligence (GenAI). The presentation focused on responding to student’s academic writing in a climate of fear about the impact of Large Language Models and artificial content generators. The information shared was practical and informed by academic research. The presenter, Brian Jackson, a colleague from the English Department, carefully interrogated the challenges and opportunities that GenAI presents in the academic setting. My Theatre Education colleagues, and I left the meeting with new insights but also a lot of worry. We knew that our students were invested in using GenAI to develop curriculum and that we needed to be better prepared to help them effectively and ethically engage them in this technology (Seemiller & Grace, 2017; Chan et al, 2023). We wondered how we would know if our students were using GenAI to better understand pedagogies or develop their theatre/drama curriculum. We also questioned whether we would be able to determine if our students could develop and create an innovative and research-based curriculum for their future classrooms if artificial intelligence was doing the work for them. We were also aware that studies have shown that the more frequently educators like us used GenAI, the more positive their perspectives about integrating the technology became (Kaplan-Rakowski, et al, 2023).
We acknowledge that the integration of technologies, including Generative Artificial Intelligence (GenAI), into the development of theatre/drama curricula can offer young teachers’ opportunities (Piriyaphokanont & Sriswasdi, 2022). We are also aware that some scholars have found that the integration of digital technologies into arts education can lead to students' readiness for the future (Khujamberdiyeva & Isakov, 2021).
We hope that in our small study we will be able to present the potential benefits and complications of utilizing AI in the development of theatre/drama curriculum and its associated pedagogies. This study does not focus on the overarching structural program changes necessary to impact course outcomes or student learning outcomes. Instead, we see this nascent study as an initial exploration of Artificial Intelligence as a classroom tool. We believe that exploring the ways that Artificial Intelligence might be used as a classroom tool will aid us in determining how best to go about those eventual structural changes. As we looked at the existing literature about this subject, we also thought it could be timely to emphasize the critical need for innovative research practices to maximize AI's potential effectiveness in pedagogy. We agreed that the encroachment of AI into educational settings requires us to identify possible methods and practices through which theatre educators can conscientiously engage with AI to the benefit of their students.
Drawing on the work of our colleague Brian Jackson (2023) our theatre education faculty determined together that we would proactively study ways that our theatre education faculty could:
Critically engage with Generative Artificial Intelligence models to better understand their form, function, and place in our pre-service theatre teaching program and in the curricula of both theatre education professors and pre-service teachers.
Better understand ways that theatre/drama education professors and our students could effectively expand our understanding of various modes of communication by exploring Generative AI as a teaching tool.
Treat generative pre-trained transformer models as potential multipliers that might amplify our processes and products.
Make our values about the use of generative content explicit to the theatre education faculty and eventually to our pre-service students.
We also believed that some educational practices that were already integral to our program culture could influence the ways that we might explore the affordances and limitations of Generative AI content creation models amongst ourselves and in our classrooms. For example, we regularly teach our students that the exploration associated with theatre/drama process can be more valuable than a theatre/drama culminating product. We believed this tenet might also apply in the investigation of GenAI and its uses. Additionally, we teach our students that the context(s) in which we study and make theatre/drama work should inform and shape the content we create in our classrooms and performance spaces. This principle could also apply when considering the appropriate uses of Generative AI in educational settings.
We decided that we would work in two phases. In the first phase faculty members would critically engage with Generative AI models to actively develop our own questions and responses to the four parameters listed above. Then armed with new understandings we would conduct a second study where we would engage our students in an exploration of the ways that generative AI might be used in the development of their future classrooms. This article focuses on our work in the first phase of our project in which we examined how Generative Artificial Intelligence might impact the work of theatre/drama education professors and our own curricula.
Throughout the paper I will use several interrelated terms to describe our interactions with ChatGPT 3.5. For the purposes of the study, we defined the terms in the following ways.
Digital technologies: A general term describing the tools, systems, and devices that can generate, create, store or process data.
Machine learning: The use and development of computer systems that learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.
Artificial Intelligence: The theory and development of computer systems able to perform tasks that normally require human intelligence. Some of these tasks include visual perception, speech recognition, decision-making, and translation between languages.
In reviewing the literature on Generative Artificial Intelligence, we found support for our beliefs that the tools associated with GenAI have the potential to benefit the development of curriculum and instruction. Conversely, the academy is only beginning to understand and contextualize the inherent challenges that the encroachment of GenAI may have on schools and schooling. Below I provide some of the major themes that are surfacing around this topic.
In May 2023 the Chronicle of Higher Education produced a report of the diverse ways that Generative Artificial Intelligence might be applied in higher education settings. The intent of this report was to make known the benefits and challenges that machine learning presents within the academy. The fifty-seven-page document examines the uses of artificial intelligence from a variety of perspectives including those of faculty, administrators, and students. Summarizing the findings of the report the Editorial Board writes “[T]he release of ChatGPT suggests that we’re at the dawn of an era marked by rapid advances in artificial intelligence, with far-reaching consequences for nearly every facet of society, including higher education. From admissions to assessment, academic integrity to scholarly research, university operations to disappearing jobs…” (2023, p.6)
Some members of the academy and adjacent artist communities feel anxiety as they consider the impact and implications of artificial intelligence on learning and creation in educational environments. At times within the Chronicle’s account selected members of the academic community write about AI with a sense of despair. Owen Kichizo Terry, an undergraduate student at Columbia University, writes that his peers are so adept at using these technologies that even “AI catching technology” can’t find the marks of artificial intelligence in student writing (p. 25). Joseph Keegan, a graduate student at Tulane University calls on academic leaders to plan for a triage of its university systems to lay out rules and establish clear and reasonable guidelines that prevent rampant plagiarism (p. 27- 29).
Artists also express concerns about our lack of seriousness about artificial intelligence in creative communities. The general worries about AI’s impact stated with the Chronicle’s reporting match with the arts specific concerns of theatre professionals. For example, theatre artist and director Annie Dorsen is well known for creatively investigating the uses of digital technologies in her staged work. She has previously thought of digital technologies as additive tools that might benefit the creative process. However, as she has engaged with machine learning in recent productions fear has made her skeptical of machine learning technologies. In a recent essay in American Theatre, she strongly advocates for a critical, more reflective use of digital technologies, and especially artificial intelligence in theatre settings. She says:
AI Technologies were not designed to assist artists, they were designed to replace them. The datasets are hidden, dated, sloppy, and nobody knows exactly what’s in them. The outputs are full of plausible-sounding nonsense. Artists are in danger of becoming unwitting propagandists for Big Tech…. We make the technologies seem interesting, cool, full of potential, maybe even beautiful. (2023)
Dorsen invites artists to be more critical and conscientious about their own decision-making process related to technologies of this kind. She asks the collective community to determine the ways that we want to use our time and capacities. Dorsen summons theatre artists to make deliberative and responsible choices about technology use. She aggressively calls on artists to determine whether they will be in the service of big technology companies or push back while using the tools. She asks other theatre practitioners:
Do we want to put our skills and imaginations at the service of these tech companies, or not? And if not, what is the right way to push back? Should we reject these tools entirely, or try to use them to question [the tools], reveal how they operate, and pierce the illusions? There may be no perfect answer. But taking these questions seriously is the very least we can do. (2023)
Other members of the academy view the advent of machine learning as an opportunity to re-examine their own learning environments, specifically focusing on ways that teachers and students might be empowered within technology contexts. In contrast to Dorsen, many members of the higher education community embrace the potential learning opportunities that AI presents university faculty, staff, and students.
Hollis Robbins (2023), humanities scholar and Dean of Humanities at the University of Utah reminds us that, “Education is still a matter of teaching people how to access information and how to turn information into knowledge” (p.11). She strongly advocates for the university as a space where educators and researchers should be curious about the affordances and limitations of artificial intelligence. She says, “Scholars are best situated to know what is not yet known, to identify ‘blank spaces’ in the universe of knowledge.” She then adds, “Until culturally inflected AI is developed, models such as ChatGPT will stand apart from culture. Knowledge production within culture will not fully be absorbed by AI. Specific and local cultural knowledge will become more valuable” (pp.11-12).
Leon Botstein (2023), President of Bard College, determines that AI will force educators to “focus on those talents and skills that will remain uniquely human.” He says, “For those concerned about students’ abusing the power of ChatGPT, we just have to take the time and make sure we know our students and have worked with them closely enough to both inspire them to do their own work and take pride in work that is their own” (p.19).
Arts educators are leveraging digital technologies to enhance student experiences in classrooms. These educators have been utilizing digital technologies in classrooms for some time. Researchers have identified digital competencies that are useful in an artistic context (Webb & Layton). Others have suggested that because “theatrical performances have always been associated with culture and a people’s way of life” there is a cultural imperative to integrate ever present digital technologies into drama in education practices (Piriyaphokanont and Sriswasdi, 2022, p. 678). Hamadi and El Den (2023) describe ways that the integration of digital technologies into Higher Education settings has dramatically changed the way students learn and find these technologies valuable in learner centered classrooms. They also caution educators to find sustainable ways to integrate digital technologies (p.3).
In “Drama/Theatre Performance in Education through the Use of Digital Technologies for Enhancing Students’ Sustainability,” Zakopoulos et al. (2023) conducted a comprehensive review of the ways that theatre/drama performances in educational settings are effectively using digital technologies as a means of persistently engaging students. They call on educators to “Redesign … [drama] curricula to integrate the goals and dimensions of sustainable development into digital drama education to cultivate the ecological awareness of students.” They also propose that:
Drama teachers aim at digital upskilling for students and [themselves] to exploit digital opportunities in creating, sharing, and delivering drama content in educational settings.
Drama teachers should work collaboratively, discuss and exchange valuable ideas and techniques, attempting to find feasible solutions to overcome problems.
Drama teachers are urged to create an interdisciplinary interconnectedness of drama pedagogical philosophy, digital technologies, and sustainability awareness of their students.
[Drama researchers should] carry out …. small-scale or large-scale studies [to identify] sustainable development in education through the application of digital dramatic techniques. (2023)
These guiding principles offer a pattern that helps us imagine ways that we might sustain the integration of Generative Artificial Intelligence into our education settings. They were also helpful in determining how we might conduct our own investigation.
Below I describe our efforts to engage artificial intelligence using our own curriculum, and our associated curricular needs as a guide.
Our study began with a spurt of short conversations between the three members of the Brigham Young University Theatre Education faculty. In these initial conversations we shared anecdotal stories about students using GenAI in ineffective and sometimes insubordinate ways. We also admitted to ourselves that we had not really done enough purposeful research to make our values about the use of artificially generated content clear to our students or to ourselves. We established that we were not ready to counter any perceived problems with students’ approaches to using the available technologies.
We wondered if it was possible to heed the guidance we had received in the university training to begin to engage with AI. We also considered whether there was anything that we could do to critically engage with Generative Artificial Intelligence models to better understand their form, function, and place in our pre-service teaching program. We asked each other what we would need to know to treat generative pre-trained transformer models as potential multipliers. We wondered if GenAI could amplify our processes and products. We decided that we wanted to see what happened when GenAI directives were paired with our own informed, embodied, decision-making processes. In these small but successive conversations we determined together that we needed to see if our own expertise plus machine learning could improve learning outcomes and assignments while preserving the richness of drama pedagogies we used in our classrooms.
Following the conversation, I developed three questions that framed the investigation:
How might Theatre/Drama Education professors utilize Generative AI to enhance established drama activities and assignments?
How can Theatre/Drama Education professors and by extension their preservice teachers effectively respond to the encroachment of GenAI as they develop, teach, and revise curriculum?
What do Theatre/Drama Education professors need to know about Generative AI to consider the ways we might reshape a drama curriculum?
The goal of this study was to explore and report on the ideas we generated about professional practices for using Generative Artificial Intelligence in curriculum development for theatre education courses at Brigham Young University. Our study took place during the 2023-2024 academic year. The study involved three Theatre Education Faculty Members. I served as the key researcher and my colleagues Julia Ashworth and Kris Peterson served as collaborators and participants in the investigation of the ways that we might consider leveraging artificial intelligence in our curriculum.
During this year our study team selected learning outcomes and a capstone assignment from two current Theatre Education Program courses. Because our goal was to initially experiment with Artificial Intelligence our data set only included learning outcomes and capstone assignments from two course that are included in the Brigham Young University Theatre Education Program Core Curriculum. Specifically, we examined an Acting Pedagogy course, and an Applied Theatre course. Both are required courses that all students within the Theatre Education Major take to complete their degree and receive a teaching license.
Then I used Chat GPT 3.5 to process those learning outcomes and capstone assignments. This action let the technological processes review, revise, and offer alternate learning outcomes and assignments to the faculty. Then I sent the various iterations of the materials Chat GPT 3.5 created to my colleagues. This included modified learning outcomes, assignments and activities. The two faculty members initially read through the materials related to their specific courses. Kris Peterson reviewed the materials ChatGPT 3.5 produced for the Acting Pedagogy Course and Julia Ashworth reviewed the materials produced for the Applied Theatre course. After their initial review they re-read the materials with me. Each faculty member identified and highlighted useful components (items that improved the learning outcome or assignment) of AI generated materials in green. Then they highlighted components that were not useful (items that made the learning outcome or assignment more confusing or inappropriate) in red. The faculty members also used the comment feature to describe why they found components useful or not helpful. This activity in the comment section helped them to review the items they had coded and then flesh out their reasons for determining whether items they reviewed were useful or not. Later the information in the comment section helped us to identify themes and those themes’ relationships to each other. Finally, we gathered to collectively discuss the data and their implications for our practice. Following this discussion, I analyzed the faculty member’s recorded verbal responses along with their written response to the materials to develop the key themes that emerged as we examined the data together. Finally, I developed written responses to the questions we had about the practical use of Generative AI in our own curriculum development. Below we will describe themes that emerged from our investigation.
The analysis represents the beginning of our work. Next steps in our effort to better understanding Generative AI’s affordances and limitations include establishing investigation processes that includes more refining and iterating prompts to effectively develop sound learning outcomes and activities. We also plan to work directly with students as co-researchers to learn from their experiences with large language models.
In our survey of the GenAI produced learning outcomes, activities and assignments we established that GenAI has an emerging mastery of theatre and education language. Because of this the technology was able to inform and improve our original language choices for learning outcomes and assignments. For example, when Julia examined the AI generated learning outcomes created for her applied theatre class, she found that the Chat GPT “language provides clarity in thought and idea” and determined that the language produced would be “helpful to add to her syllabus.”
When considering a GenAI learning outcome Kris found that the technology could produce some learning outcomes that improved upon hers. When considering an Acting Pedagogy learning outcome she writes, “this [learning outcome] is probably better phrased than what I have written.” Sometimes the accuracy of the AI response unnerved us. On encountering a particularly strong learning outcome related to her class Julia writes: “This outcome stands out as being particularly on the nose [in relation to my own learning outcomes], it makes me wonder both how it knows [what it knows] and also makes me feel justified in some of my own choices.”
Kris noted that the content generator produces strong clear assignment instructions when we provide the digital technology with a carefully worded and strong prompt. This happened when I invited Chat GPT to revise an assignment for the Theatre Education Acting Pedagogy Course. I wrote: “Create an Acting Pedagogy Workshop assignment in which students describe the design, development, and facilitation of a workshop in which they focus on the pedagogical approach of one acting pedagogue including specific details created by that pedagogue. The audience for the workshop should be new acting students.” As Kris responded to the assignment it created, she noted, “This prompt helped the AI generate far more specific and navigable assignment instructions.”
One pleasant, but not unexpected, finding that I discovered when reviewing my colleagues’ responses to the GenAI produced assignments and activities was that as they engaged with the technologically produced materials, they began a critical dialogue with the technology guided by their own personal knowledge and experience. They completed the coding assignment which was to determine whether the materials were useful. And because of their expertise in developing curriculum materials, they saw possibility in the AI generated materials. For them the materials were not perfect, and they would need to be modified or changed but the faculty perceived value in simply seeing what the technology developed. They viewed their critical reading and evaluation of the work as a kind of dialogue with the digital technology. This seemed like an important finding because it demonstrated that human interaction with ChatGPT 3.5 could be a reciprocal relationship where the embodied teachers pushed the technological processes to improve. In our discussions we noted that building a reciprocal relationship with a large language model where the user maintains power must include an element of critical-creative agency. That agency only occurs when users work reflectively to utilize and engage more than one data set, including data sets from their lived experience. This becomes more difficult as we are siloed into finding a fast or easy solution to a curricular challenge. My colleagues modeled this possibility in their discussions about using the technology. They maintained power over the technology. They never assumed that the materials created by the technology should be either accepted or rejected, instead they interrogated the materials created by the digital technology so that they could develop sound pedagogical resources.
For example, when Kris examines the AI generated assignment entitled “Cultivating Artistic Pedagogy: Integrating Diversity, Spirituality, and Community in Acting Education” she notes that while the overall assignment is strong it “does not take into consideration the complexities of humans working together in the classroom.” In this way she brings her embodied knowledge of the classroom experience to her critique of the generated materials.
Later while examining this same assignment, she acknowledges that ChatGPT 3.5’s different approach to one aspect of the assignment might be considered. Here is the example and her response:
As a component of the assignment ChatGPT 3.5 instructs students to:
Develop Personal Acting Pedagogical Philosophy:
Create an outline and lesson plan for a performance workshop based on individual artistic beliefs and educational principles.
Receive feedback from peers and instructors to refine and strengthen the philosophy.
Kris responds with an emerging reconsideration of her own curriculum: “I like this idea … we cover this in a paper. Perhaps it's something to consider for future classes.”
Julia has a similar response to Kris’s when considering Chat GPT’s written workshop “Unveiling Oppression: A Theatre of the Oppressed Workshop” She responds effusively saying that: “This written assignment makes me wonder why I would ever write with [only] myself again. The reason I say that is because it's fully detailed and if there was some nuance I needed to change that would be so much faster than creating the assignment from scratch.”
Then when reviewing a second more refined version of the Chat GPT workshop Julia writes: “The detail in this [Theatre of the Oppressed or TO] workshop assignment is spot on. It's thorough and nuanced in expert ways. Again, I'm thinking of all the ways I could use it if I didn't have some [of the TO] materials I already have [access to], but I know that it's only gonna work because I understand where the holes might be. So how do I get my students to understand the same thing… That is the primary question I have right now. And my primary feeling is shock that this assignment is so good.”
Our survey of Generative AI produced learning outcomes, activities, and assignments also reaffirmed our suspicions that there are many ways that GenAI does not enhance curriculum development. In our examination of the GenAI produced materials we found that AI cannot replace the embodied classroom experiences of the practitioner.
When examining the ten AI produced learning outcomes for her Acting Pedagogy course Kris remarks, “[It] seems like this is a LOT of information to cover in a 2.0 credit class.” And when considering the assignment descriptions for the same course she recognizes that the same broad approach to learning that appeared in the learning outcomes is also a challenge in the assignments developed for those learning outcomes. She says, “to study various acting pedagogies in one workshop is a practical difficulty as it becomes a lot of material to cover. Multiple pedagogies in the workshop mean that we only cover breadth not depth. Students often report that they'd like more depth even in the short workshops we do in class.” In this case Kris has knowledge based on classroom experiences that Artificial Intelligence cannot replicate.
In one instance ChatGPT designs a thirty-minute Theatre of the Oppressed workshop entitled Identifying Oppression. In the plan students learn and utilize Forum theatre, Image Theatre, Rainbow of Desire, and Legislative Theatre with that brief timeframe. When encountering this section Julia writes: “This is ridiculous. There's no way one human could do all of this work in the time they've laid out. [ChatGPT has] no experience with Theater of the Oppressed. While the language is accurate and they identify key concepts in Theater of the Oppressed, there is not only a lack of time to do the amount of work they're listing, but also a lack of contextualization. If these were the instructions given to students being introduced to Theater of the Oppressed, they would be lost and confused and kind of baffled.”
Sometimes the statements are so comprehensive that our reviewers describe them as “vague and impossible” (Julia) or “a broader learning objective for the [department] rather than something specific to an Acting Pedagogy class” (Kris).
Two sample learning outcomes created by ChatGPT emphasize my colleagues’ concerns with the appropriateness of course level learning outcomes.
ChatGPT writes:
For the Applied Theatre Classroom: “Synthesize learning from the course to develop a comprehensive understanding of the role of applied theatre in community development, education, advocacy, and personal transformation.”
For the Acting Pedagogies Classroom: “Demonstrate a commitment to lifelong learning and growth as an acting educator, engaging in reflective practice and seeking out opportunities for further training, mentorship, and professional advancement.”
In both examples the language used is appropriate for the field but there is no understanding that these are introductory courses. Each ask students to complete tasks that are well beyond the skill level that would develop in a beginning exploration of the content shared in the class.
In their overall assessments of the materials that ChatGPT developed Kris and Julia expressed the most concern about the technology’s lack of humanity. Julia writes: “Oftentimes nuance is missing, sometimes the AI language feels impossible or almost irresponsible because of the lack of experience and nuance. There is a lack of understanding that comes from experience and time and space working in and facilitating these theater pedagogies. Something that really stood out to me were impossible expectations for facilitators or participants in a workshop. Not only impossible because of time constraints, but because of lack of details or lack of understanding how huge the ask was.”
Kris concurs with Julia and writes: “I rely heavily on what I’m seeing/hearing/feeling in class in addition to my spiritual intuition during the actual workshops. [It] seems next to impossible to have AI help me in this regard.” Both comments hearken back to Robbins’ (2023) assertion that specific and local cultural knowledge is more valuable than the “machine’s capacity for knowledge production” (pp. 11-12).
Through critical engagement with Generative Artificial Intelligence models like ChatGPT 3.5 the BYU faculty better understood the form, function, and place of machine learning. After analyzing ChatGPT’s creations and then participating in a critical evaluation of the materials Julia reported that she was ready to expand the ways that she invites her students to interact with AI. She says, “I've been scared to introduce AI into my classes, so my first thought was I just need to do it, which is what I always tell my students. They don't learn unless they have experience, which ironically a robot does not have. But I can clearly see how it's helpful, and I am fully aware that it's part of our society and our students are using it so, to pretend otherwise is silly and unproductive.”
She also began to formulate ideas about how AI might be used in the context of our pre-service teaching program. She noted that in using the tool she found that it might help us improve our course level learning outcomes in classes across the program. Julia thought that ChatGPT could be “especially helpful on our [theatre education program] assessment day.” In that yearly meeting faculty spend a lot of time looking at our course level learning outcomes and how they are impacting the flow of curriculum. In those conversations we also determined how those course level learning outcomes affect all the courses that we teach. Julia believed that AI might improve our efficiency in those meetings because of its capacity to generate refined iterations of our original course level learning outcomes.
Kris thought that we could use time in faculty meetings to set goals for our own experimentation and further exploration of AI. She writes, “... this is a technology that won’t be going away anytime soon, it will behoove us to understand the limitations of this technology so that we can help our students know how to navigate what quality curriculum will look like in the future.”
The Theatre Education faculty found limitations in ChatGPT’s products. Generative AI could not replicate the lived classroom experiences of faculty, it could not adequately articulate the embodiment of performance and performance practice, and faculty members found that the technology was limited in expressions of authenticity. Julia found that AI can often create a “central topic but not the [real world] application.” Kris writes: “[W]hat I find … missing is that because AI is unaware of some of the nuances of performance or performance practice, it speaks too much in generalities.”
Faculty members also found that Generative AI has some strengths. It could effectively gather information on general education subjects, develop strong learning outcomes, and when specifically prompted AI could also generate basic lessons and activities for theatre education courses.
More importantly, through their own exploration both colleagues began to imagine ways that generative prompts could improve on the ChatGPT 3.5 data sets and could potentially amplify both faculty and student’s processes and products. In their responses, each proposed nascent ideas about how using and modifying Generative AI datasets could be integrated into the courses in our program. They had a better understanding of specific ways that we could help our students to productively explore Generative AI as a teaching tool. In meetings following the study we have begun to systematize course assignments that would introduce Artificial Intelligence into curriculum design.
For example, Kris proposed that we could engage students in practicing and refining user developed prompts. She suggests an interactive and iterative process to teach them to develop progressively sound prompts: “... we will need to help them create or develop prompts that may generate clearer responses from the AI. I think it’s worth exploring with our students, especially the beginning theatre education students as they [initially create] their own curriculum.” She continues, “I would predict that in the future, one could create a lesson plan or workshop structure that could be input into AI and be used to generate quality curriculum. So, it may be worth it [for our students] to experiment with the sorts of inputs we can use to help us [develop quality artifacts].”
By determining some of the strengths and challenges of the learning outcomes and assignments produced by Generative AI both faculty members ultimately decided that it was our program’s responsibility to increase our collective (faculty and students) critical literacies around AI. We agreed to continue our own experimentations. We also agreed to develop course level assignments in which students were invited to shift from a potentially passive consumptive practice of interacting with GenAI to a more critically engaged effort. Julia expresses this saying “My gut response to this is full transparency with our students. And partnering with our students because the idea of doing it on our own feels counterintuitive to the world that they live in and [the world] I'm trying figure out.” Their responses hearken back Bolstien’s (2023) invitation to “know our students” and to “inspire them to do their own work and take pride in work that is their own.” (p.19). What Julia and Kris add to Bolstein’s invitation is their confidence that they and their students can successfully work together as co-researchers investigating the learning opportunities presented to educators with the advent of large language models.
There are certainly ethical considerations about the use of AI in the development of theatre/drama pedagogies. We continue to be worried about the originality of student work, and we certainly care about the widespread use of copyrighted material in the development of technology datasets. Our active exploration of GenAI-driven content curation and recommendation algorithms helped us to determine how this new technology could inform our curriculum design processes and curricular products. More importantly it aided us in determining the ways that we could manage the emotional impact of the potential disregard for critical and creative thinking that can arise when using Artificial Intelligence without reflection. We determined we would not teach in fear of the new technology. Instead, we are “trying to figure it out” in the ways that Julia described above. A value system for using Generative AI is emerging in our conversations. Our early framework includes being transparent with our students about our beginning understanding of the technology. The framework also embraces partnering with our students to better understand the ways that we can all work together to better leverage artificial intelligence in our future classrooms.
Since the writing of this article, we have begun to implement the second exploratory phase of our small research study. Grounded by the ideas that we generated during this study we are now partnering with pre-service teachers in our program to further consider the uses of artificial intelligence in their own curricula. Drawing on Leon Bolstein’s (2023) charge that university educators “focus on those talents and skills that will remain uniquely human” (p.19) we have invited student teams to find ways to use traditional drama tools to play with and interrogate their work with large language models. In this way we are trying to create a reciprocal relationship with the digital technology that still values embodied approaches to curriculum creation.
We hope that more theatre/drama-based research will follow. We agree with Zakopoulos that it is vital that arts researchers explore “digital dramatic techniques” that allow for a continued sustainable development of theatre/drama education within the ever changing technologically driven world (2023). Ideally these studies could further identify the affordances and limitations of artificial intelligence as a means of curriculum development. Hopefully those students can further address issues of creativity, ethics, and the impact large language models have on the primacy of embodied learning as a key aspect of curriculum creation.
Petersen Jensen, A. (2024). Educating ourselves and a large language model: A small study looking at the affordances and limitations of generative artificial intelligence for a theatre/drama curricula. ArtsPraxis, 11 (2), pp. 43-64. https://doi.org/10.33682/e847-hjb6
Bolstein, L. (2023). AI will make the university more human. In How will artificial intelligence change higher ed: Big bot on campus. The Chronicle of Higher Education.
Chronicle Review. (2023). Big bot on campus: The perils and potential of ChatGPT and other AI. The Chronical of Higher Education.
Chronicle Review (2023). Editorial board forward. In How will artificial intelligence change higher ed: Big bot on campus. The Chronicle of Higher Education.
Dorsen, A. (2023, August 30). The dangers of AI intoxication: Can theatre artists use the tools of big tech to dismantle its influence? In American Theatre. Retrieved April 23, 2024.
Chan, C.K.Y., & Lee, K.K.W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10. Retrieved April 20, 2024.
Hamadi, M., & El-Den, J. (2023). A conceptual research framework for sustainable digital learning in higher education. Research and Practice in Technology Enhanced Learning, 19 (1).
Jackson, B. (2023, August 25). GenAI and the future of writing. College of Fine Arts and Communications University Conference Lecture Series. Brigham Young University.
Kaplan-Rakowski, R., Grotewold, K., Hartwick, P. & Papin, K. (2023). Generative AI and teachers’ perspectives on its implementation in education. Journal of Interactive Learning Research 34 (2), pp. 313-338. Waynesville, NC: Association for the Advancement of Computing in Education (AACE). Retrieved September 25, 2024.
Khujamberdiyeva, S., & Isakov, A. (2021). Advanced experiences in the use of digital technologies in teaching fine arts (On the example of Finland and South Korea). Turkish Journal of Computer and Mathematics Education, 12, pp. 939–946.
Keegin, J. (2023). ChatGPT is a plagiarism machine. In How will artificial intelligence change higher ed: Big bot on campus. The Chronicle of Higher Education.
Liu, Y.T., Lin, S.C., Wu, W.Y., Chen, G.D., & Chen, W. (2017). The digital interactive learning theater in the classroom for drama-based learning. In Proceedings of the 25th International Conference on Computers in Education, Christchurch, New Zealand, 4–8 December; Asia-Pacific Society for Computers in Education: Taoyuan City, Taiwan. pp. 784–789.
Piriyaphokanont, P., & Sriswasdi, S. (2022). Using technology and drama in education to enhance the learning process: A conceptual overview. International Journal of Information and Education Technology, 12, pp. 678-684.
Robbins, H. (2023). 17 notes on academic AI. In How will artificial intelligence change higher ed: Big bot on campus. The Chronicle of Higher Education.
Seemiller, C., & Grace, M. (2017). Generation Z: Educating and engaging the next generation of students. About Campus, 22 (3), pp. 21–26.
Terry, O.K. (2023). I’m a student you have no idea how much we are using ChatGPT. In How will artificial intelligence change higher ed: Big bot on campus. The Chronicle of Higher Education.
Webb, A., & Layton, J. (2023). Digital skills for performance: A framework for assessing current and future digital skills needs in the performing arts sector. Arts and the Market, 13 (1), pp. 33-47.
Zakopoulos, V., Makri, A., Ntanos, S., & Tampakis, S. (2023). Drama/Theatre Performance in Education through the Use of Digital Technologies for Enhancing Students’ Sustainability Awareness: A Literature Review. Sustainability, 15 (18).
Amy Petersen Jensen is a professor in the Theatre Education Program at Brigham Young University where she teaches arts education courses in both theatre and media arts education. She is also an Associate Dean in the College of Fine Arts and Communications where she is primarily responsible for faculty development, research, and hiring.
Return to Volume 11, Issue 2
Return to Volume 11
Return to ArtsPraxis Home
Cover image from NYU Steinhardt / Program in Educational Theatre production of Two Noble Kinsmen, directed by Amy Cordileone in 2024.
© 2024 New York University