Developing AI Literacy: Preservice Teachers’ Use of AI in Educational Planning

Framework

This study is grounded in social constructivism, social cognitive theory, and Shulman’s concept of practical wisdom. Social constructivism emphasizes the role of social interactions in the development of knowledge, which is relevant as preservice teachers collaborated to critically analyze and refine AI-generated content. Social cognitive theory, particularly Bandura’s concept of self-efficacy, is central to this research, as it examines how using AI tools impacts preservice teachers' confidence in their instructional planning abilities. Additionally, Shulman’s concept of practical wisdom, which involves the ability to apply theoretical knowledge in practical situations, is a key framework. The study investigates how preservice teachers develop this practical wisdom through reflective practice and the iterative use of AI in their lesson planning.

Methods

The research employed a qualitative approach to explore the experiences of 30 undergraduate preservice teachers enrolled in a learning theories course at a university in South Central Texas. Participants were divided into lower-level (freshmen and sophomores) and upper-level (juniors and seniors) groups. They were tasked with creating lesson plans using two AI tools, Google Bard and ChatGPT, followed by a group comparison and refinement of these plans. Data sources included AI-generated lesson plans, modified group lesson plans, questionnaire responses, and reflective essays. The data were analyzed using constant comparison techniques, with the analysis focusing on clustering responses, refining codes, examining group dynamics, and identifying emerging themes related to practical wisdom and self-efficacy.

Results

The analysis revealed that upper-level preservice teachers, who had more experience in educational courses, were more adept at critically evaluating AI-generated content, integrating it into lesson plans that aligned with educational objectives. In contrast, lower-level participants were more focused on incorporating engagement activities, often without fully considering the educational value of the AI-generated content. The study found that the development of practical wisdom and self-efficacy is closely linked to experience and reflective practice. As preservice teachers engaged more deeply with AI tools, their ability to apply theoretical knowledge in practical instructional planning scenarios improved. These findings underscore the importance of experience and guided reflection in developing the skills necessary for effective and ethical AI integration in education.

Importance

This study is highly relevant to the current educational landscape, where AI tools are becoming increasingly prevalent in teaching and learning. By examining how preservice teachers interact with AI in instructional planning, the research provides critical insights into how teacher education programs can better prepare future educators to use AI effectively and ethically. The findings contribute to the broader discourse on AI in education, particularly in understanding how to develop educators’ competencies in integrating AI into their teaching practices. This study is valuable to higher education faculty interested in the intersection of AI and perservice teacher education, offering evidence-based recommendations for enhancing teacher preparation programs.

References

References

Ayanwale, M. A., Frimpong, E. K., Opesemowo, O. A. G., & Opoku, D. O. (2024). Exploring factors that support pre-service teachers’ engagement in learning artificial intelligence. Journal for STEM Education Research. https://doi.org/10.1007/s41979-024-00121-4
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., Romero-Hall, E., Koutropoulos, A., Toquero, C. M., Singh, L., Tlili, A., Lee, K., Nichols, M., Ossiannilsson, E., Brown, M., Irvine, V., Raffaghelli, J. E., Santos-Hermosa, G., Farrell, O., Adam, T., Thong, Y. L., Sani-Bozkurt, S., Sharma, R. C., Hrastinski, S., ... Jandrić, P. (2023). Narratives on ChatGPT and AI (negative): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 92-107. https://doi.org/10.5281/zenodo.7559479
Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., Romero-Hall, E., Koutropoulos, A., Toquero, C. M., Singh, L., Tlili, A., Lee, K., Nichols, M., Ossiannilsson, E., Brown, M., Irvine, V., Raffaghelli, J. E., Santos-Hermosa, G., Farrell, O., Adam, T., Thong, Y. L., Sani-Bozkurt, S., Sharma, R. C., Hrastinski, S., ... Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53-91. https://doi.org/10.5281/zenodo.7559479
Chung, C. J. (2024). Preservice teachers’ perceptions of AI in education. AI-EDU Arxiv. Retrieved from https://journals.calstate.edu/ai-edu/article/view/4155​ (Cal State Open Journals).
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8
Crompton, H., Jones, M., & Burke, D. (2022). Affordances and challenges of artificial intelligence in K-12 education: A systematic review. Journal of Research on Technology in Education. https://doi.org/10.1080/15391523.2022.2121344
Fish, D., & Coles, C. (1998). Developing professional judgment in health care: Learning through the critical appreciation of practice. Butterworth-Heinemann.
Goodfellow, R. (2001). Online literacies and learning: Operational, cultural and critical dimensions. Language and Education, 15(3), 179-195.
Google Bard. (2023). Google.
Harris, J. B., & Hofer, M. J. (2011). Technological pedagogical content knowledge (TPACK) in action: A descriptive study of secondary teachers’ curriculum-based, technology-related instructional planning. Journal of Research on Technology in Education, 43(3), 211–229. https://doi.org/10.1080/15391523.2011.10782570
Henriksen, D., & Mishra, P. (2024). Teaching, teacher education, and practical wisdom in the age of generative AI. In J. Cohen & G. Solano (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 779-787). Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/primary/p/224040/
Higgs, J., & Titchen, A. (2001). Practice knowledge and expertise in the health professions. Butterworth-Heinemann.
Kartal, T., & Dilek, I. (2021). Preservice science teachers’ TPACK development in a technology-enhanced science teaching method course. Journal of Education in Science, Environment and Health (JESEH), 7(4), 339-353. https://doi.org/10.21891/jeseh.994458
Klassen, R. M., & Tze, V. M. C. (2014). Teachers’ self-efficacy, personality, and teaching effectiveness: A meta-analysis. Educational Research Review, 12, 59–76. https://doi.org/10.1016/j.edurev.2014.06.001
Kuhn, C., Alonzo, A. C., & Zlatkin-Troitschanskaia, O. (2016). Evaluating the pedagogical content knowledge of pre- and in-service teachers of business and economics to ensure quality of classroom practice in vocational education and training. Empirical Research in Vocational Education and Training, 8, 5. https://doi.org/10.1186/s40461-016-0031-2
Mishra, P., Warr, M., & Islam, R. (2023). TPACK in the age of ChatGPT and generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235–251. https://doi.org/10.1080/21532974.2023.2247480
OpenAI. (2022). ChatGPT.
Pressley, M., & Afflerbach, P. (1995). Verbal protocols of reading: The nature of constructively responsive reading. Routledge.
Shulman, L. S. (2004). The wisdom of practice: Essays on teaching, learning, and learning to teach. Jossey-Bass.
Sternberg, R. J. (1990). Wisdom: Its nature, origins, and development. Cambridge University Press.
Stenberg, K., & Maaranen, K. (2022). Promoting practical wisdom in teacher education: A qualitative descriptive study. European Journal of Teacher Education, 45(5), 617–633. https://doi.org/10.1080/02619768.2020.1860012
van den Berg, G., & du Plessis, E. (2023). ChatGPT and generative AI: Possibilities for its contribution to lesson planning, critical thinking and openness in teacher education. Education Sciences, 13(998). https://doi.org/10.3390/educsci13100998
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Zhang, C., Schießl, J., Plößl, L., & Zlatkin-Troitschanskaia, O. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20, 49. https://doi.org/10.1186/s41239-023-00420-7