As English language arts (ELA) teacher educators committed to growing students’ passion for reading, writing, and thinking critically, we acknowledge the impact that emerging digital technologies have on educational practices. Generative artificial intelligence (GenAI) is one such technology that has sparked both excitement and concern. GenAI, driven by deep learning algorithms, enables machines to produce language that resembles human-written text (Fitria, 2023). GenAI platforms, such as ChatGPT and Gemini, have led to intense debate on various topics, including education, health care, and ethics (Dwivedi et al., 2023). In education, GenAI can assist in or perform a wide range of tasks, including writing, language translation, and lesson planning. However, concerns have been raised regarding its potential to generate misinformation, exacerbate existing racial, gender, and linguistic biases, extend and reinforce dominant social and economic ideologies, and diminish the need for human interactions in communication (Inclezan & Prádanos, 2023; Tacheva & Ramasubramanian, 2023). Striking a balance between embracing the opportunities presented by GenAI and mitigating its potential pitfalls is crucial (Chan, 2023).
Educators must stay vigilant and curious as GenAI platforms evolve. Awareness of the affordances and constraints of the technology will allow educators to equip students with the skills necessary for recognizing biases and reliable information (Ali et al., 2021). Teacher educators, especially, must share in the challenge of disseminating informed knowledge about GenAI to future teachers, particularly given 91Ě˝»¨â€™s commitment to addressing new technologies and antiracism in tandem (91Ě˝»¨, 2021). Specifically, teacher educators should emphasize that it is vital to maintain a human-centered approach to education, stressing the significance of human connections, creativity, and critical thinking skills that cannot be replaced by GenAI (Shneiderman, 2021; Young et al., 2024).
This statement is the product of two years of ongoing conversation, writing, presentation, and productive, collaborative disagreement between the 14 members of the 91Ě˝»¨ Commission on Digital Literacies and Teacher Education (D-LITE) AI working group and with teachers working in schools. Members of the D-LITE commission previously authored 91Ě˝»¨â€™s Beliefs for Integrating Technology into the English Language Arts Classroom (2018), a position statement that deeply informs our approach to critically exploring the use of new technologies in ELA teacher education classrooms. We have all worked to critically and thoughtfully explore and incorporate generative AI into our work with preservice and inservice teachers and have published and presented on this subject in 91Ě˝»¨ journals and conferences (e.g., Moran, 2024; Nash et al., 2023; Turner et al., 2024). As GenAI is a new technology, particularly in English education, we do not position ourselves as AI experts but, rather, as scholarly explorers investigating this evolving technology and drawing on our own and others’ ongoing research into digital literacies in ELA classrooms and teacher education. We offer this position statement with ten recommendations for incorporating, addressing, and exploring GenAI in ELA teacher education.
These recommendations are meant as starting points, not endpoints. We encourage teacher educators to mess around (Ito et al., 2010) with GenAI and seek students’ input on what works. We also encourage teacher educators to learn about AI, to explore the way the physical technologies needed for it to operate harm the planet, the economic imperatives that drive its development and govern its corporate platform-holders, and the myriad voices used without permission or left out of AI data corpuses (Crawford, 2021; Tacheva & Ramasubramanian, 2023). We encourage teacher educators to explore AI in its totality, working and learning with and about it, and inviting their students along for these journeys. We provide these statements as informed, evolving beliefs with suggestions for practice, knowing they likely will change as GenAI itself evolves. We offer these ideas as an invitation to other teachers and teacher educators to think along with us.
- GenAI platforms are literacy technologies. They function in tandem with their users’ literate abilities, meaning that users must engage in reading, speaking, and writing in order to use GenAI platforms. They are used to construct products (e.g., written words, images, audio, videos) that have long been the purview of literacy and English education scholars and teachers. Moreover, GenAI has the potential to usher in drastic changes to teaching in general and ELA, specifically. Learning the nuances of how AI operates can help English teacher educators see beyond techno-utopian hype to examine what is useful and/or problematic about GenAI. Exploring GenAI critically with preservice and inservice teachers must be the purview of ELA education.
- ELA teacher educators cannot ignore AI technologies. While educators may have varying levels of comfort or enthusiasm for incorporating GenAI into their teaching practices, it is essential to address and engage with these emerging technologies. Ignoring GenAI’s existence and potential impacts would be a disservice to students, as they are likely to encounter GenAI platforms and tools in various contexts throughout their lives. Rather than avoiding the topic altogether, educators should aim to foster critical discussions and explorations of GenAI’s possible roles, affordances, limitations, and implications within the context of literacy and ELA education.
- GenAI literacy includes understanding how GenAI platforms work. ELA teacher educators, preservice, and practicing teachers need GenAI literacies, which refer to the ability to use GenAI platforms and knowledge about how these platforms work, including the statistical models they are based on, the human labor used to train them, the way they predict text, and the kinds of outputs they construct. Finally, GenAI literacies encompass critical awareness regarding the varied cultural, emotional, and economic factors surrounding GenAI (Beach et al., 2023; Leander & Burriss, 2020).
- GenAI includes varied multimodal platforms. GenAI tools based on large language models have shown the ability to imitate art, music, and other compositions previously created by human artists and authors, “thereby encroaching on humans’ monopoly on the creative process” (Kirkpatrick, 2023, p. 21). GenAI-mediated forms of multimodal composition are challenging existing expectations of the multimodal creative process, aesthetics, and creative consciousness. As teachers and educators, we must consider what it means to be a writer and creator and what it means to support our student writers in an age of AI. While collaborating with AI platforms opens new avenues for multimodal content creators, teachers, and teacher educators will need to develop pedagogical and technological competencies to promote and sharpen, rather than hinder, young writers’ creativity and human voices.
- GenAI platforms can reproduce existing biases and prejudices that must be critically examined. Despite the perception of neutrality, GenAI platforms can perpetuate and amplify existing racial, gender, linguistic, national, and other biases and prejudices present in their training data or underlying algorithms (Noble, 2018; Robinson, 2023). In addition, it is crucial for ELA educators to critically analyze the outputs generated by GenAI, scrutinizing them for potential biases, and engaging students in discussions about the ethical considerations and implications of these technologies. By examining the ways in which AI can reflect and reinforce societal biases, educators can promote critical thinking, media literacy, and a deeper understanding of the complex interplay between technology and societal values.
- ELA teacher educators should facilitate their students’ situated practice with GenAI platforms. As GenAI continues to unfold and proliferate, it is imperative for teachers—as learners themselves—to practice low-stakes explorations of what it is, how it works, and what it can mean in their teaching. To facilitate robust pedagogical development, we encourage space for teachers to learn beyond rigid institutional demands in order to benefit from playful tinkering (Kim et al., 2023; McBride et al., 2023) with GenAI technologies. This freedom to experiment and collaborate allows teachers and students to embrace failure as a productive component to learning.
- ELA teacher educators should not eliminate the productive struggle of writing. Fears of GenAI being a tool of cheating have led to policies that prohibit any use. This view takes agency away from writers. Of course, GenAI can also supplant thinking; teacher educators must help teachers critically explore how these platforms can be used in ways that encourage the productive struggle of learning. Teacher educators can model practices that engage GenAI as a tool throughout a writer’s process to help shape, refine, or advance writing. GenAI can serve in many ways as a cowriter that supports process-oriented intellectual work.
- Learning to read GenAI texts critically is crucial. Although writing with GenAI has received more attention in English education, teachers and students must also learn to read AI-generated texts critically. As discussed above, despite being machine-generated, GenAI texts are not neutral, but reflect existing cultural biases. The biases, inaccuracies, hallucinations, and ideology present within AI-generated text can be subtle. AI can also be used to spread deepfakes, non-consensual intimate imagery, and false information. These factors create the need for AI-specific critical reading practices. Earlier critical approaches to analyzing content, source, modality, and media of digital texts (Cohn, 2021; Turner & Hicks, 2015) can be adapted and practiced as ELA teachers learn to critically analyze GenAI texts with a mind to their unique factors.
- ELA teacher educators must discuss and model ethical practices when using GenAI. GenAI has sparked a number of controversies related to the ethics of its development and use. AI is trained through ethically dubious labor practices (Tacheva & Ramasubramanian, 2023) and the hardware necessary to keep such programs running requires high energy demands and water usage, causing additional damage to the earth’s climate (Behizadeh et al., 2024). GenAI also relies on human intellectual property being used as training data without creators’ permission and can be used to replace creators and artists in productive processes (Bozard, 2023); artists have struggled with preventing AI platforms from scraping their designs from the internet and imitating them in ways that impact their livelihood. As teacher educators, it is important for us to facilitate conversations that explore our entanglement in these problems and help students make informed choices about the use of GenAI.
- Teaching with GenAI is still a human-centered process. Teaching is messy, learning involves feelings, and we are increasingly understanding the value of so-called soft skills and the social-emotional aspects of belonging to a classroom community. ELA educators can help teachers explore the possibility of using GenAI in teaching activities like lesson planning, activity design, or written feedback (Hashem et al, 2023). Teachers may use AI to tweak existing unit plans or develop new ones, to create summaries of class notes or reports of department meetings. Thinking about which assignments AI could give useful feedback on, and where harm may be caused through the hasty application of AI assistance, is an essential practice for teachers considering whether and how to use GenAI in their teaching. ELA remains a humanistic discipline, even as GenAI and other technologies increasingly mediate human relationships and communications. Teaching with AI should provoke us to think about what is valuable and necessary about the human relations that underpin our teaching and our technologies of communication.
Conclusion
As with the many technologies that have preceded GenAI—from pencils, pens, and paper to typewriters, word processors, and voice-to-text dictation—our response to, implementation of, and instructional choices surrounding our use of GenAI will be informed by empirical research and reflective practice. We return to 91Ě˝»¨â€™s Definition of Literacy in a Digital Age (2019), which reminds us that “a literate person possess[es] and intentionally appl[ies] a wide range of skills, competencies, and dispositions” and that “[t]​​hese literacies are interconnected, dynamic, and malleable.” Just as teachers and students have adapted and extended technologies to support ELA teaching and learning in the past, based on principled judgment and the changing nature of the tools and of ELA itself, so too will we learn to work with, extend, critique, and—when necessary—resist GenAI in our various classroom contexts. Given the rapidly changing nature of GenAI platforms, it is essential to recognize that this work is ongoing and involves a necessary and continual evolution of our approaches to curriculum, assessment, and teaching practice. In doing so, we, along with our students, will learn how to engage with GenAI in increasingly critical, ethical, and agentive ways—an endeavor, at heart, that we see as truly grounded in humanity and creativity.
References
Beach, C.L., Alvermann, D.E., Loomis, S., Wright, W., & Hutcherson Price, L. (2023). Digital remixing online: Entangled feelings. Digital Culture & Education, 14(4), 92–108. (Open Access) https://www.digitalcultureandeducation.com/volume-14-4
Behizadeh, N., Johnson, L.L., Garcia, M. (2023). Promise and perils of GenAI in English education: Reflections from the national technology leadership summit. English Education, 56(1), 8–19.
Bozard, Z. (2023). What does it mean to create art? Intellectual Property rights for Artificial Intelligence generated artworks. South Carolina Journal of International Law & Business, 20(1), 83–101.
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 1–25.
Cohn, J. (2021). Skim, dive, surface: Teaching digital reading. West Virginia University Press.
Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press
Fitria, T. N. (2023, March). Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay. ELT Forum: Journal of English Language Teaching, 12(1), 44–58.
Hashem, R., Ali, N., El Zein, F., Fidalgo, P, & Abu Khurma, O. (2023). AI to the rescue: Exploring the potential of ChatGPT as a teacher ally for workload relief and burnout prevention. Research and Practice in Technology Enhanced Learning, 19(23): 1–26. https://doi.org/10.58459/rptel.2024.19023
Inclezan, D., & Prádanos, L. I. (2023). AI for sustainability. In D. La Torre, F. P. Appio, H. Masri, F. Lazzeri, & F. Schiavone (Eds.), Impact of artificial intelligence in business and society (1st ed., pp. 192–211). Routledge. https://doi.org/10.4324/9781003304616-13
Ito, M., Baumer, S., Bittanti, M., Boyd, D., Cody, R., Herr-Stephenson, B., Horst, H. A., Lange, P. G., Mahendran, D., MartĂnez, K. Z., & Pascoe, C. J., (2010). Hanging out, messing around, and geeking out. MIT Press.
Kim, G. M., Johnson, L. L., & Nash, B. L. (2023). Games to promote empathy as a literacy practice: A new teacher’s playful practice. English Education, 56(1), 20–43. https://doi.org/10.58680/ee202356120
Kirkpatrick, K. (2023). Can AI demonstrate creativity? Communications of the ACM, 66(2), 21–23. https://doi.org/10.1145/3575665
Leander, K.M., Burriss, S.F. (2020). Critical literacy for a posthuman world: When people read, and become, with machines. British Journal of Educational Technology, 51(4), 1262–1275. doi:10.1111/bjet.12924
McBride, C., Smith, A., & Kalir, J. H. (2023). Tinkering toward teacher learning: a case for critical playful literacies in teacher education. English Teaching: Practice & Critique, 22(2), 221–233.
Moran, C. M. (2024), Ed. Revolutionizing English education: The power of AI in the classroom. Bloomsbury/Rowman & Littlefield/Lexington Books.
Nash, B. L., Hicks, T., Garcia, M., Fassbender, W., Alvermann, D., Boutelier, S., McBride, C., McGrail, E., Moran, C., O’Byrne, I., Piotrowski, A., Rice, M., & Young, C. A. (2023). Artificial intelligence in English education: Challenges and opportunities for teachers and teacher educators. English Education, 55(3), 201–206. https://doi.org/10.58680/ee202332555
91Ě˝»¨ Council of Teachers of English (2018, October 25). Beliefs for integrating technology into the English language arts classroom. /statement/beliefs-technology-preparation-english-teachers/
91Ě˝»¨ Council of Teachers of English. (2019, November 7). Definition of literacy in a digital age. /statement/nctes-definition-literacy-digital-age
91Ě˝»¨ Council of Teachers of English (2021). 91Ě˝»¨ standards for the initial preparation of teachers of English Language Arts 7–12 (Initial Licensure). /wp-content/uploads/2021/11/2021_91Ě˝»¨_Standards.pdf#:~:text=Standard%201:%20Candidates%20apply%20and
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
Robinson, B. (2023). Speculative propositions for digital writing under the new autonomous model of literacy. Postdigital Science and Education, 5(1), 117–135. https://doi.org/10.1007/s42438-022-00358-5
Shneiderman, B. (2021). Human-centered AI. Issues in Science and Technology, 37(2), 56–61.
Tacheva, J., & Ramasubramanian, S. (2023). AI Empire: Unraveling the interlocking systems of oppression in generative AI’s global order. Big Data & Society, 10(2), 20539517231219241. https://doi.org/10.1177/20539517231219241
Turner, K. H. & Hicks, T. (2015). Connected reading is the heart of research. English Journal, 105(2), 41–48.
Turner, K., Hicks, T., Schoenborn, A., Murchie, S., & Cornwell, A. (2024, March). Now the hard work begins: Inviting writers to use AI tools. In Society for Information Technology & Teacher Education International Conference, 2024(1), 887–891. Association for the Advancement of Computing in Education (AACE).
Young, C. A., Jacobs, L., Celaya, A., Lathrop, B., Hackett-Hill, K., Scialdone, M., Moehrke, H., Gaddis, B., Settie, K., Kuriny, D., and James, N. (2024). In conversation about GenAI in ELA education: Initial insights and experiences from ELA teachers and ELA teacher educators. In C. M. Moran (Ed.), Revolutionizing English education: The power of AI in the classroom. Bloomsbury/Rowman & Littlefield/Lexington Books.
Statement Authors
Brady L. Nash, The University of Florida
Merideth Garcia, University of Wisconsin-La Crosse
Carl A. Young, North Carolina State University
Kristen Hawley Turner, Drew University
Mary Rice, The University of New Mexico
Amy Piotrowski, Utah State University
W. Ian O’Byrne, College of Charleston
Cherise McBride, Stanford University
Ewa McGrail, Georgia State University
Clarice Moran, Appalachian State University
Troy Hicks, Central Michigan University
William Fassbender, Montana State University
Stefani Boutelier, Aquinas College
Donna Alvermann, The University of Georgia
This position statement may be printed, copied, and disseminated without permission from 91Ě˝»¨.