Preparing Educators to Teach and Create With Generative Artificial Intelligence
DOI:
https://doi.org/10.21432/cjlt28606Keywords:
AI literacy, teacher education, instructional design, AI education, generative AIAbstract
Teachers skilled in using generative artificial intelligence (GAI) have advantages in terms of increased productivity and augmented instructional capabilities. Alongside the rapid advancement of GAI, teachers require authentic learning opportunities to build the confidence and expertise necessary for engaging with these technologies creatively and responsibly. This article provides an illustrative case of preparing preservice and in-service teachers with the knowledge, skills, and mindsets to teach and create with GAI. Using a self-study method to investigate professional practices, we analyzed the curriculum, instruction, and assessment in an upper-level undergraduate course in multimedia design and production. Thirty-five teachers engaged in experiential activities focussed on developing artificial intelligence (AI) literacy, alongside a collaborative assignment to co-author an open-access textbook, Teaching and Creating With Generative Artificial Intelligence. To support equitable and inclusive access to the educational benefits offered by AI, the Student Artificial Intelligence Literacy (SAIL) framework was developed. SAIL facilitates student AI literacy through curriculum engagement and three distinct types of interactions: cognitive, socio-emotional, and instructor-guided. Building on lessons learned from the COVID-19 pandemic regarding the issues with technology training for teachers in Canada, five recommendations are offered to facilitate the meaningful integration of AI literacy in teacher education programs.
References
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics 2, 431–440. https://doi.org/10.1007/s43681-021-00096-7
Amazon Web Services. (n.d.). What is artificial general intelligence? https://aws.amazon.com/what-is/artificial-general-intelligence/
Bauschard, S. (2023, May 30). AI literacy: The immediate need and what it includes. Education Disrupted: Teaching and Learning in an AI World. https://stefanbauschard.substack.com/p/ai-literacy-the-immediate-need-and
Bauschard, S. (2024, May 20). Microsoft Copilot will hear, see, and speak. Education Disrupted: Teaching and Learning in an AI World. https://stefanbauschard.substack.com/p/microsoft-copilot-will-hear-see-and?triedRedirect=true
Bullock, S. M., & Butler, B. M. (2022). Reframing collaboration in self-study. In B. M. Butler & S. M. Bullock (Eds.), Learning through collaboration in self-study. Critical friendship, collaborative self-study, and self-study communities of practice (pp. 313–323). Springer. https://doi.org/10.1007/978-981-16-2681-4_22
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: A systematic literature review. International Journal of STEM Education, 10(1), Article 29. https://doi.org/10.1186/s40594-023-00418-7
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y
Ciampa, K., Wolfe, Z. M., & Bronstein, B. (2023). ChatGPT in education: Transforming digital literacy practices. Journal of Adolescent & Adult Literacy, 67 (3), 186–195. https://doi.org/10.1002/jaal.1310
Cope, M., Kalantzis, M., & Searsmith, D. (2020). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(12), 1229–1245. https://doi.org/10.1080/00131857.2020.1728732
Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43–71. https://doi.org/10.1002/piq.21143
ETAD 402. (2023). Teaching and creating with generative AI. University of Saskatchewan. https://openpress.usask.ca/etad402teachingandcreatingwithgenai/
Francom, G. M., Lee, S. J., & Pinkney, H. (2021). Technologies, challenges and needs of K-12 teachers in the transition to distance learning during the COVID-19 pandemic. TechTrends, 65(4), 589–601. https://doi.org/10.1007/s11528-021-00625-5
Hagerman, M., Beach, P., Cotnam-Kappel, M., & Hébert, C. (2020). Multiple perspectives on digital literacies research methods in Canada. International Journal of E-Learning & Distance Education Revue Internationale Du E-Learning Et La Formation à Distance, 35(1). https://www.ijede.ca/index.php/jde/article/view/1159
Hervieux, S., & Wheatley, A. (2020, March 11). The ROBOT test [Evaluation tool]. The LibrAIry. https://thelibrairy.wordpress.com/2020/03/11/the-robot-test/
Hoechsmann, M., & Poyntz, S. (2017). Learning and teaching media literacy in Canada: Embracing and transcending eclecticism. Taboo: The Journal of Culture and Education, 12(1). https://doi.org/10.31390/taboo.12.1.04
Hollister, B., Nair, P., Hill-Lindsay, S., & Chukoskie, L. (2022). Engagement in online learning: Student attitudes and behavior during COVID-19. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.851019
Johnson, G. P. (2023). Don’t act like you forgot: Approaching another literacy “crisis” by (re)considering what we know about teaching writing with and through technologies. Composition Studies, 51(1), 169–175. https://compstudiesjournal.com/wp-content/uploads/2023/06/johnson.pdf
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), 313–338. https://www.learntechlib.org/primary/p/222363/
Kim, J., Lee, H., & Cho, Y. H. (2022). Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies, 27, 6069–6104. https://doi.org/10.1007/s10639-021-10831-6/
Lock, J., Gill, D., Kennedy, T., Piper, S., & Powell, A. (2020). Fostering learning through making: Perspectives from the International Maker Education Network. International Journal of E-Learning & Distance Education Revue Internationale Du E-Learning Et La Formation à Distance, 35(1). https://www.ijede.ca/index.php/jde/article/view/1160/
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In R. Bernhaupt, F. F. Mueller, D. Verweij, & J. Andres (Chairs), Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
Loughran, J. (2005). Researching teaching about teaching: Self-study of teacher education practices. Studying Teacher Education, 1(1), 5–16. https://doi.org/10.1080/17425960500039777
MacDowell, P., & Korchinski, K. (2023). A collaborative future: New roles of students and teachers learning and creating with generative AI. In S. Bauschard, A. Rao, P. Shah, & C. Shryock (Eds.), Chat(GPT): Navigating the impact of generative AI technologies on educational theory and practice (pp. 490–507). Pedagogy Ventures.
MediaSmarts. (2023). Young Canadians in a wireless world, Phase IV: Digital media literacy and digital citizenship. https://mediasmarts.ca/research-reports
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13232
Park, J. (2023, May 29). A case study on enhancing the expertise of artificial intelligence education for pre-service teachers. Preprints, Article 2023052006. https://doi.org/10.20944/preprints202305.2006.v1
Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000366994
Prachagool, V., Nuangchalerm, P., & Yawongsa, P. (2022). Digital literacy of pre-service teachers in the period time of COVID-19 pandemic. Journal of Educational Issues, 8(2), 347–358. https://doi.org/10.5296/jei.v8i2.20135
Robinson, L. E., Valido, A., Drescher, A., Woolweaver, A. B., Espelage, D. L., LoMurray, S., Long, A. C. J., Wright, A. A., & Dailey, M. M. (2022). Teachers, stress, and the COVID-19 pandemic: A qualitative analysis. School Mental Health, 15, 78–89. https://doi.org/10.1007/s12310-022-09533-2
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 31–40. https://doi.org/10.37074/jalt.2023.6.1.17
Vanassche, E., & Kelchtermans, G. (2015). The state of the art in self-study of teacher education practices: A systematic literature review. Journal of Curriculum Studies, 47(4), 508–528. https://doi.org/10.1080/00220272.2014.995712
Vaughan, N., & Lee Wah, J. (2020). The Community of Inquiry Framework: Future practical directions—Shared metacognition. International Journal of E-Learning & Distance Education Revue Internationale Du E-Learning Et La Formation à Distance, 35(1). https://www.ijede.ca/index.php/jde/article/view/1154
Zhang, K., & Aslan, A. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, Article 100025. https://doi.org/10.1016/j.caeai.2021.100025
Zhao, L., Wu, X., & Luo, H. (2022). Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modeling analysis. Sustainability, 14(21), Article 14549. https://doi.org/10.3390/su142114549
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Copyright (c) 2024 Paula MacDowell, Kristin Moskalyk, Katrina Korchinski, Dirk Morrison
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