The rise of artificial intelligence is not a new phenomenon. Artificial intelligence has been part of our daily lives (smart phones, home appliances, editing software, chatbots and more) for some time without raising fears of an overthrow by machines. In recent years, this has changed due to the rise of Generative Artificial Intelligence (GenAI), a new wave of technology based on Large Language Models (e.g. ChatGPT, DALL-E2) and the imminent arrival of Artificial General Intelligence (AGI) which in theory could learn to accomplish any intellectual task that human beings can perform. Of the two technologies, GenAI has entered the mainstream, and it is timely to understand its operations and use across all aspects of society, including drama education. To achieve this understanding, a desk-top research methodology was used to examine the following research question: ‘How are Drama educators using generative AI in their teaching practice?’
GenAI uses machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analysing patterns and information from the training data (Ooi et al., 2023). Recent advances and increased access through lower cost applications means GenAI has seen an upsurge in its popularity. ChatGPT, just one of the GenAI tools available, claimed over 180 million users (October 2023) in its first year. Consequently, there is a hype around this technology that we cannot ignore. Claims of its capacities and warnings of its dangers are over-whelming in number. The rapid development of new GenAI technology, and GenAI extensions to existing technology, suggest this is a fast-track technology (Dedehayir & Steinert, 2016) which may be difficult for both users and policy makers to keep up with. For this reason, the United Nations (n.d.) has expressed concerns about the recent advances in GenAI, establishing an Artificial Intelligence (AI) advisory body to strengthen international governance of AI including scanning for risks and supporting international collaboration on data. At this point it may be useful to recall Collins’ (2010, p. 3) first law of technology which states: “We invariably overestimate the short-term impact of a truly transformational discovery, while underestimating its longer-term effects”. With this in mind, it is crucial that we track the short and long-term effects of GenAI, and more specifically its use and impact on specific industries and areas of specialisation such as drama education.
GenAI uses Large Language Models (e.g., ChatGPT, DALL-E2) to generate new content (e.g. text, images, music) by analysing patterns and information from the training data (Ooi et al., 2023). In short, GenAI uses written language as its primary mode. Drama education certainly incorporates written language in its practice, but this is not the primary concern for drama education which prioritises tacit knowledge and the aesthetic domain. The body is recognised as critical in drama learning as evidenced in drama education’s emphasis on embodied learning that asserts the centrality of the body and emotion in fostering aesthetic and artistic knowledge (Antilla, 2015; Bird & Sinclair, 2019; Eisner, 2008; Ewing, 2012). Embodied learning sits in direct opposition to computational, cognitivist models of learning that Large Language Models used by GenAI are based on. Cognitive theories conceptualise learning as purely mental representation (Ertmer & Newby, 2013; Michela, 2022). Cognitivist models of learning overlook the influence of the body and environment on learning. Emerging research increasingly demonstrates the critical role that the body plays in shaping learning experiences (Macedonia, 2019; Macrine & Fugate, 2022; Shapiro & Stolz, 2019; Sullivan, 2018). Drama education’s belief and commitment to embodied learning, points to the possibility of reduced uptake or novel use of disembodied GenAI technology by drama educators. The research presented in this article seeks to understand drama educators’ use of GenAI in the field of drama. Chomsky (2023) suggests that GenAI is based on a “fundamentally flawed conception of language and knowledge”. Could this flawed conception of language and knowledge impact drama educators’ perceptions and use of GenAI?
If we look at the broad field of education, online forums and emerging research tell us that GenAI is currently being used by teachers for lesson planning, assessment design and grading, administrative tasks, and resource creation amongst other uses. This literature also tells us that as teacher awareness of GenAI grows, they are likely to incorporate it into their practice more frequently (barring restrictions from their school) (Kaplan-Rakowski et al., 2023). Educating teachers and students alike about Generative AI’s capacities and limitations is crucial (Gill et al., 2024).
What is of interest here is whether drama-specific pedagogical practices and the primacy of tacit knowledge in drama education has or is likely to, produce unique uses of GenAI. The purpose of this article then is to understand current practices using GenAI for drama education, increase Drama educators’ awareness of the capacities of GenAI for drama education and perhaps spark innovative uses not yet imagined.
Literature Review
Drama education is known for its willingness to embrace and explore the affordances of available technologies. The emergence of GenAI is simply the next technology to be embraced, but the knowledge gained through drama educators’ use of predecessor technological advances offers insights into technology’s potential role in drama learning in the present.
Looking at recent decades, technology has increasingly been incorporated into drama education, transforming the learning experience for students. Examining the research literature focusing on drama and digital technology, there is a frequent assertion that technology doesn’t simply make things easier but fundamentally alters the way drama is taught and performed (Anderson et al., 2009; Davis, 2011; Fanouraki & Zakopoulos, 2023; Jensen, 2011; Nicholls & Philip, 2012). Digital technology is not a replacement for human interaction in drama learning. It is viewed as an addition, and the teacher’s role in guiding students’ creative exploration remains crucial (Carroll & Cameron, 2009). Cameron, Anderson and Wotzko (2017) stress the importance of the drama community’s expertise in understanding the interplay between the real and virtual worlds. They argue that drama educators are uniquely positioned to guide students in navigating these spaces and harnessing technology for meaningful dramatic exploration. It is yet to be seen if this is the case for the GenAI, but patterns of the past suggest that this will also be the case with GenAI. Understanding how technology is currently used in drama education is critical in order to capture the shifts that may occur with the introduction of GenAI technology to drama education.
The introduction of digital technologies such as online chat (Davis, 2011; Fanouraki & Zakopoulos, 2023), digital cameras, online watch parties, gamified content, 360 videos, and asynchronous online modules (Rixon et al., 2021) have been found to enhance the immediacy of drama learning experiences. Two projects conducted by Australian academic Sue Davis, ‘Cleo Missing’ and ‘The Immortals’ (Davis, 2012) both exploited digital tools to create immediacy for participants. ‘Cleo Missing’ is an example of an interactive process drama that employed digital cameras and a website where students could share and interact with narrative materials. ‘The Immortals’ project similarly involved immediate interaction via an online chat function which was used during both the creation process and performance of the play, allowing for real-time interaction and feedback. Online watch parties during the Covid-19 pandemic were effectively employed to engage students and facilitate immediate feedback and discussion of dramatic works (Rixon et al., 2021). Immediacy in online spaces provides learners with a sensation of presence (real or imagined) and a feeling of emotional connection (Drewery, 2022).
Employing digital technology to foster engagement in drama learning also features prominently in the reviewed research. Virtual worlds such as ‘Second Life’ have been effectively exploited as immersive learning environments to promote interactivity through online avatars (Nicholls & Philip, 2012). Digital pre-texts (Anderson et al., 2009), are a technological engagement tool echoing a common drama pedagogical tool used in analogue teaching. For many of the technologies mentioned, technology is a tool that replicates drama pedagogical methods or dramatic conventions common to drama education. However, absent from most of the listed uses of technology shared thus far is an ability to promote embodied drama learning experiences. Instead, the focus has been on language-based interactions or viewing visual materials.
Although embodiment is not consistently present in applications of technology in drama, there are examples where embodiment is facilitated through digital interaction, including projects such as ‘Creature’. This drama learning experience included an immersive 360-degree interactive cave environment designed to promote engagement through physical interactive digital projections (Clark-Fookes, 2023). Meanwhile, Zhang and Jiang (2024) employed virtual reality (VR) to engage students in drama education. They suggest that the use of VR promotes vocal, visual and somatic engagement that can enhance the outcomes of drama learning. These technologies sit in contrast with earlier technologies used in drama education, and GenAI’s lack of embodied application due to its reliance on language in the absence of the body.
Despite the advancements in technology, research sources acknowledge the challenges of integrating technology in drama education (Cameron et al., 2017; Davis, 2011). Schools often restrict access to online platforms due to safety concerns, and at times struggle to supply students with access to technology due to resourcing limitations. At this point in time, it should be noted that many schools are also restricting students access to GenAI, citing threats to academic integrity and learning development. A growing number of researchers suggest that drama practitioners should advocate for increased access to these tools, emphasizing the importance of digital literacy in drama education.
Methodology
Data collection for this desk-top methodology involved a review of published articles (both peer-reviewed and non peer-reviewed) and social media sites established for drama education communities. 45 social media sites created for drama education professionals were scanned for references to the following terms: ‘generative Artificial Intelligence’; ‘artificial intelligence’; ‘GenAI’; ‘AI’; and ‘Chat GPT’. ChatGPT was selected as a search term as it was most subscribed to GenAI at the time of the data collection. In addition, evidence suggested that Chat GPT had entered common vernacular and was, at the time of the research, being used as a noun and verb (e.g. “Chat GPT it”). Collins dictionary are currently monitoring the term Chat GPT for addition to their dictionary.
A Boolean search was also used to locate articles and blogs thematically connected to the research focus. The following Boolean string was employed to guide the systematic search for relevant literature: “(Drama OR Theatre) AND (Education OR Teaching OR Learning) AND (‘Artificial Intelligence’ OR AI OR ‘Generative AI’ OR ‘GenAI’ OR ‘Chat GPT’)”, ensuring the retrieval of studies intersecting the domains of drama or theatre, education-related practices, and advancements in artificial intelligence technologies. A final search parameter relating to date of publication was added. Only articles and posts published since 2020 were included in the search. This decision was made to ensure that the results pertained to publicly accessible GenAI and reflected the public launch of ChatGPT in October 2022. It should also be noted that articles were removed from the results if they did not reference GenAI technology. The search produced 10 items that met the search criteria (see Table 1).
Thematic coding was conducted manually through an iterative process to identify recurring themes in the data. This coding work generated three key codes: Planning, Pedagogy, and Assessment. The process began with data familiarisation, followed by open coding to assign preliminary labels to key concepts. Similar codes were grouped during axial coding, leading to the refinement of overarching themes.
The resultant planning code encompasses strategies for integrating AI into lesson design and creation of learning resources, while pedagogy focuses on the use of GenAI within drama learning activities. Assessment addresses the evaluation of student performance supported by GenAI for task and rubric design, and feedback. These codes form the foundation for analysing GenAI’s role in drama education within this review.
Findings
The three codes of Planning, Pedagogy, and Assessment, were used to facilitate systematic analysis and reporting. The findings are also reported using these codes.
Planning
The most frequently cited use of GenAI in the data relates to planning drama learning and the creation of resources to support learning and teaching. Drama educators used GenAI applications to generate lesson plans, but importantly it is noted that in many cases, the AI generated lesson plans were used as a starting point from which teachers would either differentiate learning experiences for their context, or further develop and enhance the AI generated lesson plan providing by drawing on their specialist knowledge and experiences. The most commonly cited use for GenAI in planning was the creation of learning and teaching resources. Drama educators pointed to the reduction in lesson preparation time through GenAI produced resources, particularly with respect to monologue and script production. The preparation of these materials is time and cognitively consuming for drama educators. The benefits cited for using GenAI for these tasks were connected to creating resources for their specific classroom context (e.g., number of roles required and differentiated literacy levels), personalisation of learning, and the demonstration of specific subject matter (e.g., stylistic aspects or conventions) in the generated scripts.
The following section provides an overview of drama educators’ use of GenAI for planning purposes. This list (and subsequent lists) is not in order of frequency, nor does it capture all of the possible uses, it merely captures the uses retrieved through the collected data.
Scriptwriting: GenAI is capable of producing scripts that adhere to specific parameters set by the drama educator. Drama educators are creating scripts that emulate a dramatic style, contain a set number of characters (monologue, duologue, whole class), explore a particular context or narrative, demonstrate a specific convention or dramatic structure, and can produce multiple versions of the same script at different reading levels.
Development of Stimulus and Pre-text Materials: Drama educators are reducing preparation time by using various GenAI applications to produce stimulus materials including pre-texts. Stimulus materials may take various forms such as an object, a poem, a photograph, music, soundscape, or video; anything that assists in activating the drama (Taylor & Warner, 2006). By contrast, a pretext is a specific type of stimulus that provides a guiding framework for the drama; ‘the means by which the dramaworld is set in motion’ (O’Neill, 1995).
The most commonly referred to stimuli created are character cards. The characters may be imaginatively generated or drawn from specific theatrical styles such stock characters from melodrama or Commedia dell’arte. This may involve the use of GenAI that produces text such as ChatGPT and/or image generators which can be found in Chat GPT4 and applications such as Midjourney.
Another commonly cited set of stimulus materials created using GenAI were improvisation starters containing starter lines, and ideas for setting, character and/or situation. Parameters such as group size, characters, theme or style of theatre can be provided through prompts to the GenAI, thus assisting in providing specificity in the subject matter and differentiation for the needs of the class.
Text Summaries: A strength of GenAI is its capacity to summarise text. GenAI can be prompted to provide a range of summaries, including plot, scene by scene, character and thematic summaries. Summaries can sometimes provide alternate readings and are very useful for streamlining big concepts and texts into easily digestible chunks. These are useful in the creation of learning material, but can also be used to plan units of work or playtext studies.
Differentiating Texts: GenAI can be used to rewrite texts for students with different reading levels or second-language learners. Text can be simplified, made more complex or colloquialisms removed by entering the target text and specific differentiation prompts for the application.
Tongue Twisters: An educator suggested the use of GenAI to produce tongue twisters. Tongue twisters can be generated to feature specific sounds and letters. The rapid generation of these allows for frequent renewal of teaching content, and varying challenges to be set for students.
Pedagogy
The completed review also suggests that Drama educators are exploring ways to employ GenAI to support their pedagogical practice and enhance students’ learning. Drama classrooms differ greatly to those within other subject areas. The practical and embodied nature of drama pedagogy means that drama educators, while using GenAI in some ways that match those in education generally, are often also adopting approaches that differ from those used in other subject areas.
The following section provides an overview of the use of GenAI for pedagogical purposes in the drama education context.
Improvisation contributor: One of the intriguing outcomes of GenAI and its ability to simulate human-like conversation is the tendency for users to anthropomorphise the application (Huxor, 2022). This is known as the Eliza effect (Hofstadter, 1995). Eliza is a direct reference to George Bernard Shaw’s play Pygmalion, where Eliza Doolittle is trained in language and societal norms to simulate a different persona. The parallels to machine learning through large language models has been drawn. This phenomenon is harnessed by drama educators for the purposes of improvisation.
Improvisational learning experiences can be delivered with or without other drama students. Students can effectively improvise a scene by engaging with GenAI. That is GenAI can be prompted to engage as a character in a dramatic exchange. Currently, this would only include language-based responses, but as new applications emerge, future opportunities may include video (including movement based) responses. The accessibility of GenAI makes improvisation tasks possible for individual students, at home, and in online learning contexts.
Ideation: Drama educators also report using GenAI to assist in brainstorming and ideation processes for students engaging in devising activities. Ideation may be provided by GenAI for initial dramatic starting points, plot, dramatic structure, and characters. Most drama educators referred to idea generation being useful to instigate devising tasks and ‘ignite’ students’ creativity. This may also be a useful tool for inclusion as students with executive function issues can benefit from the assistance in task instigation and organisation of ideas into a structure.
In terms of igniting creativity, GenAI may provide useful supports in the form of enabling constraints (Manning & Massumi, 2014). By way of explanation, one of the myths of creativity is that you must have openness for it to function. This has been proven a false assumption (Clark-Fookes, 2023; Haught-Tromp, 2017; Manning & Massumi, 2014). By providing enabling constraints such as a starting point, a plot overview or a dramatic structure, the range of available options reduces (Bix & Witt, 2020). This reduction in the scope of the task, assists students to engage with and ultimately complete the devising task. In short, they can find a way to access what a given task is asking of them. The use of GenAI here provides valuable parameters for students’ creative work, creating a set of conditions that support the development of creative work, ‘to the more-than of its potential’ (Manning, 2016, p. 58). Some may consider this cheating or lazy, but when used correctly, creativity and drama skills can be effectively given a boost. From a neuroscientific perspective, employing GenAI as a creative collaborator during the planning and ideation stage may reduce the cognitive load (Kolfschoten, 2011) for students and allow students to engage in the learning more efficiently, allowing them to focus on the embodied skills of drama earlier in the creative process.
Documenting practical work: A common approach to devising in drama is through improvisation. Students are engaged in active improvisation and this reaps more authentic action and dialogue, and its transitory nature means ideas can be worked and re-worked quickly. Capturing improvised action can be advantageous. In the past, students have made notes or transcribed dialogue from recordings. With the advent of GenAI, transcription software can transcribe dialogue in real-time and differentiated between speakers. A drama educator reports using software such as OtterAI to efficiently capture student devising in real-time. Further, this software can also summarise dialogue to provide synopses and overviews of major ideas explored.
Vocal Delivery: GenAI can be harnessed to assist with actor’s development of vocal skills. Specifically, GenAI can be used to analyse actor’s speech with capacity to provide analysis on volume, pitch, pace, pause, accent and articulation among other things. This use of GenAI provides valuable feedback to actors wanting to develop their performance skills. Elsa, Praat and Speech Prism are applications that may be useful for the purposes of speech analysis.
Script Analysis: A key skill in actor training is script analysis. Actors engage in script analysis to understand the text and their character’s motivations. A common activity in actor’s script analysis, is analysis of objectives. A key activity in this process is challenging the actor to assign ‘actioning verbs’ to capture the motivations and intention of scripted moments (Moseley, 2016). Actors are encouraged to explore the effect of different verbs on the scene in the rehearsal process. GenAI is useful here as it can analyse the text and suggest alternate actioning verbs for exploration in the rehearsal process. The value being that it opens up readings of text that may not be immediately apparent to an actor.
Design: A frequently cited use of GenAI in drama education is for design (Chrimson, 2023; Drama Peeps, n.d.; We Teach Drama, n.d.). Design tasks such as designing sets, props, backdrops, projected images, programs and costumes are accelerated by the use of GenAI. Image generating AI reduces the need to not only design these elements, but in the case of projected images and backdrops, it replaces the need to physically paint scenic elements.
Students developing directorial pitches or design concepts can employ GenAI to efficiently create prototypes of their production vision. This approach allows them to spend more time on other aspects of the design or directorial process or for developing technical skills to enhance their performances.
GenAI has the capacity to quickly generate alternate visions from the same prompt which can open up new ideas and lines of thinking for dramatists. GenAI such as VizComm can also be used to transform sketches to 3d models and fully rendered images which is handy for production meetings when ideas need to be generated and shared quickly for shared understanding across a production team.
Exploring Drama Knowledge: Drama is not generally the focus of GenAI produced to serve the broader education market, but there are some applications that have incorporated drama knowledge. For instance, School AI, a general education application, (at the time of writing this article), had two relevant learning experiences for drama education. The experiences focus on acting and theatre production, and offer an immersive educational conversation between the student and the AI. The GenAI instigates a conversation by posing a question to the learner. Importantly, the question will attempt to prompt a discussion about the students’ own experiences and understanding of the subject. Again, using a conversational approach, leverages the anthropomorphic illusion of GenAI.
Audio Composition: Music and sound effects are an effective way to underpin dramatic action. Using GenAI students can compose their own sound effects, soundscapes and musical compositions to underscore their drama work. The rapid pace of generation and ease of use means students can generate audio accompaniment to their classwork with ease. One example discussed the generation of audio to accompany song lyrics created in the style of Bertolt Brecht.
Video and Animation production: Similarly to audio composition, students can create video and animation from text-based prompts. These videos and animations can be projected and interacted with during student performance work.
Character development: One teacher within the review described the use of image generating AI to assist students in checking if their character descriptions accurately conveyed their intended vision (We Teach Drama, n.d.). By entering their descriptions as image prompts, they could see if their description accurately produced something akin to their description. If not, they were encouraged to refine and further develop their character descriptions.
Grouping students: Designing groups to maximise learning and productivity can be a difficult task when class attendance varies and we often seek to organise student groups with particular outcomes in mind (e.g., grouping students by literacy level, students playing a particular role, etc.). For teachers, completing this task prior to the start of the class is ideal, but not always achievable. GenAI can efficiently organise student groups while the teacher is freed up to focus on the lesson at hand.
Assessment
Not all drama education contexts incorporate formal assessment which provides students with an understanding of their achievement against a set of criteria. Having said this, assessment is a compulsory part of school and university practice. As drama educators working in contexts where formal assessment of students’ skills, knowledge and abilities occur, drama educators seek to support their students in attaining their best outcomes. Drama educators report using GenAI to support assessment processes (Waxman, 2024).
The following provides an overview of how drama educators have harnessed GenAI to support assessment literacy and student achievement on assessment tasks in the drama. It is interesting to note that teachers have only discussed GenAI’s use in supporting written assessment, despite the bulk of drama assessment occurring through practical modalities.
Generating mock exam questions: In education systems where students are required to sit written exams responding to an unseen question, or series of unseen questions about drama subject matter, a common use of GenAI is to attempt to pre-empt questions by using GenAI to suggest possible questions based on previous prior papers. These questions are then used as practice questions for students to respond to, thus practising their ability to respond to such questions under exam conditions. Further, by engaging in mock exams, students are developing assessment literacy with respect to reading the questions and knowing what is being asked of them. Prior to the exam the teacher can use these questions as formative assessment to check where students learning is at, and to unpack how to read and respond to the question. This inevitably raises students’ familiarity with the exam process with the intention of reducing exam anxiety.
Generating Mock Exam Responses: Another useful strategy outlined by teachers (Drama Queensland, n.d.) can be employed when preparing students for exams. Previous exam questions or questions simulating what might be on the exam (and desired genre of output) are entered into GenAI as prompts. The exam responses generated are then used for the purposes of familiarising students with marking criteria. Students use the marking criteria to grade the GenAI created work. This task requires students to actively engage with the criteria as they grade the work. It is anticipated that the critical engagement with the criteria will assist them arrive at an informed grasp on the criteria their work will be assessed on, thus assisting them to understand the task expectations.
Discussion
Planning & Pedagogy
It is evident from the range of GenAI uses shared by Drama educators that this tool is of benefit to Drama educators’ practice. The greatest discernible benefit is the use of GenAI to reduce the workload of teachers during the planning phase which occurs outside of class time. This frees teachers up for other work and non work-related tasks. Pedagogically, the articulated uses offer support to students by reducing some of the ‘busy tasks’ that do not directly relate to the core skills of drama that should be the focus of learning, such as creating backdrops or sampling sounds for soundscapes. Further, the ability for GenAI to support students with learning needs by assisting with initiating ideation or structuring ideas, frees teachers up to focus on a greater range of students and other learning opportunities. The benefit of which is felt by all students in the class. The efficiencies provided by students using GenAI in the drama classroom allows for more in-depth exploration of the core skills and subject matter of drama.
Assessment
The uses identified by drama educators currently are not expansive, but it is interesting to note that teachers’ discussion of its current use is limited to exam and more specifically written tasks. This may be in part attributed to the fact that the applications are largely language based, and also due to the ways we assess in drama. Drama assessment is largely embodied and praxical in nature with the exception of critique or tasks responding to drama in written modes. Perhaps this disconnect between the language based drivers of GenAI, and lack of embodied, emotionally mature outputs distances drama practice from some of GenAI’s capabilities. This may be key to differentiating and protecting drama practices from some of the perceived threats of GenAI to artists and education generally.
When considering when and how GenAI can be used to support assessment, teachers require a clear understanding of the purpose of the assessment task and what the key artistic practices and subject matter are. Once there is an understanding of what is valued and important to the task, then it is possible to consider GenAI as a tool to assist in assessment processes.
Although the data collected does not highlight GenAI use for the purposes of making assessment more inclusive, this is a potential strength yet to be tapped into by drama educators. GenAI has the capacity to assist teachers in designing more inclusive assessment tasks by using it to personalise and support individual learners. Research tells us that one of the greatest strengths of GenAI is its personalise learning and adapt content to the preferences or needs of learners (Abunaseer, 2023; Kadaruddin, 2023; N. Wang et al., 2024). This has been tapped into in the planning and pedagogical phase, but there is a gap in discussion regarding its use for assessment.
Drama educators should be encouraged to explore the range of GenAI tools available and consider how these might be leveraged to improve accessibility and inclusivity in assessment tasks and processes. To achieve this, a clear understanding of the purpose of the assessment task and what the key artistic practices and subject matter are is critical. From this knowledge, arts teachers can examine where assessment tasks may be modified or extra scaffolding provided to assist students overcome barriers that are not relevant to the task. For example, students with English as an Additional Language or Dialect (EALD), if the task does not assess students’ English proficiency, but focuses on acting skills, there is no reason that the task sheet could not be translated into their first language via GenAI (T. Wang et al., 2023). Similarly, task sheets can be re-written by GenAI to cater to specific reading levels, reducing literacy barriers that technical language might present to students with low-literacy. In the case of neurodivergent students and students exhibiting executive function delays, GenAI can provide step by step guides to completing a task or in cases where starting the task is a barrier to access, GenAI may provide an organizing structure (essay paragraph structure) or suggestions for starting the work e.g. opening lines of a scene. Image generating AI can be used to support students illustrating design concepts in directorial pitches.
Finally, another unidentified use for GenAI in drama assessment is the use of chatbots. Chatbots can be used to answer common student queries about assessment when the teacher is not available, particularly handy for out of school hours. This discussion features just a handful ways that teachers may harness GenAI to improve assessment. As the field of GenAI burgeons, increased opportunities for educators to harness the power of Gen will emerge.
Considerations for Use
Although this article has focused on the current use of GenAI in drama education contexts, while analysing the data there were two additional topics of note discussed by drama educators: ethical considerations and tips for getting the most out of GenAI. It would be remiss to ignore these topics, but this article is not the space for a detailed discussion.
Nonetheless, on the topic of ethics, many concerns were raised advising caution in relation to the use of GenAI. Discussion included concerns about GenAI’s impact on students’ capacity as thinkers and learners, erosion of academic integrity, threats to the future of work, issues of GenAI pillaging artists’ intellectual property and GenAI’s propensity for bias (Fang et al., 2024). Ethical concerns regarding GenAI are numerous and the data reported here captured only a small sample of these concerns. A further interesting observation from the data, was an absence of discussion regarding the impact of these technologies on amplifying existing inequalities and widening the digital divide due to disparities in resource allocation (Li, 2023). This article seeks to inform drama educators of GenAI’s capacities with a view to expanding practice, but it must be stated that drama educators need to consider ethical arguments relating to its use, and ultimately make their own informed decisions.
The second set of considerations emerging from the data relate to optimising GenAI use through the use of prompts and plugins to existing GenAI applications. Through the discussion, the importance of clear and specific prompts that provide context, and reduce the parameters of the task are beneficial in generating the desired outputs. This skill was considered critical to developing mastery in the use of GenAI.
In order to optimise GenAI as a drama-specific lesson planning tool, educators discussed the need to apply their own drama specific pedagogical filter to lesson suggestions produced by GenAI. A comment on a forum simply stated, “it doesn’t know how to act”, highlighting the inability for language-based GenAI applications to understand or plan for the tacit, the embodied or emotional understandings that are central to drama. Drama educators understand that “aesthetic knowing holds primacy in arts encounters and is present in arts learning across any domain; digital or analogue” (Clark-Fookes, 2023).
In the cases where GenAI did offer suggestions for practical learning, other educators highlighted GenAI’s inability to account for physical or emotional safety. Again, the disconnect between the valued pedagogies and knowledge of our field and the inadequacy of language-based models to account for this are highlighted.
Conclusion
This article provides a snapshot of drama educators’ use of GenAI at a point in time. The data indicates that the most common use of GenAI by drama educators is for resource creation, followed by lesson planning, but its use also extends to teachers’ pedagogical and assessment practice. It is likely that between the writing of this article and its publication, many more uses have been discovered, and the technology itself will have evolved not just through its own machine learning, but through the development of new plug ins and extensions to the technology. As discussed, there is tension between the language-based models underpinning GenAI’s function, and drama’s privileging of the aesthetic domain and tacit understanding through the body and emotion, not just words or images. The absence of embodiment in the identified applications of GenAI by drama educators underscores the inadequacy of current GenAI technologies in meeting the requirements of drama as an embodied practice. Despite this, drama educators have access to a tool that presents opportunities to enhance our practice as educators and artists. A particular strength of GenAI is its capacity to differentiate and personalise learning, and reduce teachers’ workload on tasks that are not core to drama subject matter or practice.
Of course, there are dangers and ethical concerns that we must be cognisant of, but for centuries artists have adapted and innovated, and GenAI presents a new opportunity for growth and development. This will only occur if drama educators understand what GenAI is, and learn to exploit its capacities to further our artform and pedagogical practice. It is timely to recall Cameron and Anderson’s assertion that "the teacher’s role in the drama classroom of today, infused (or not) with technology, is the same as it always has been: to provide students with the access to the tools of creation and support their growing control of those tools to create meaning’ (Anderson et al., 2009, p. 14).