Streaming Session
Recorded SessionOur session will incorporate the following key activities:
• First, we will use Mentimeter to have attendees respond to various questions about their previous experience with and knowledge about digital teaching simulations to support teacher learning. We will describe the grounding and rationale for using digital teaching simulations to support teacher learning. Anticipated time: 8 minutes.
• Second, we will provide participants will an opportunity to prepare for and engage in one GenAI teaching simulation where they will have an opportunity to practice eliciting a GenAI elementary student’s thinking about a math topic within an online simulation platform. In their preparation, participants will engage in peer-to-peer interaction as they brainstorm some questions and prompts that they could use to elicit the GenAI student’s thinking. Then they will use these questions and prompts to complete the GenAI simulation – either individually or in pairs (each participant will choose their desired format). Anticipated time: 15 minutes.
• Third, we will provide participants with an opportunity to reflect on their GenAI teaching simulation experience with each other. They will have a chance to compare the teaching moves that they used to elicit the GenAI student’s thinking and how the GenAI student responded. In addition, they will discuss what they learned about the GenAI student’s thinking from their elicitation. Anticipated time: 7 minutes.
• Fourth, participants will watch a video of another GenAI teaching simulation where one teacher practiced facilitating a science discussion with three GenAI elementary students. Participants will have access to a transcript of the discussion and will work in small groups to analyze the teaching moves used in the discussion to support student learning and engagement. Anticipated time: 10 minutes.
• Fifth, we will provide participants with a behind-the-scenes look at how we developed and refined these GenAI teaching simulations. We will show them the student knowledge profiles we developed, our AI prompts, and few-shot examples and explain how these components worked to power the GenAI student responses during the simulations. Anticipated time: 10 minutes.
• Finally, participants will engage in a collaborative discussion about the opportunities and challenges for integrating GenAI teaching simulations within their own teacher professional learning settings. We also will field questions from participants during this time. Anticipated time: 10 minutes.
After this session, participants will be able to: (1) understand and value the use of GenAI teaching simulations to support teachers’ learning of key instructional skills; (2) learn about the design features of GenAI teaching simulations and how they can be used to develop teachers’ ability to engage in key teaching practices; (3) become familiar with and expand their ideas about how GenAI teaching simulations can be integrated productively into varied teacher learning settings (e.g., coaching sessions; professional learning communities; PD workshop; etc.) to support teacher learning.
• Mikeska, J.N., & Bhatia, A. (2025). Using digital teaching simulations powered by generative artificial intelligence to propel teacher learning. Journal of the Chartered College of Teaching. https://my.chartered.college/impact_article/using-digital-teaching-simulations-powered-by-generative-artificial-intelligence-to-propel-teacher-learning/
• Howell, H., Bhatia, A., O’Dwyer, E. P., Kevelson, M., Mikeska, J. N., & Cisterna, D. (2025). Designing performance-based professional development: Stakeholder views on essential competencies and approaches. Education Sciences, 15(2), 204. https://doi.org/10.3390/educsci15020204
• Mikeska, J.N., Cross Francis, D. I., Lottero-Perdue, P. S., Park Rogers, M. A., Shekell, C., Bharaj, P. K., Howell, H., Maltese, A., Thompson, M. & Reich, J. (2025). Promoting preservice teachers’ facilitation of argumentation in mathematics and science through digital simulations. Teaching and Teacher Education, 154. https://doi.org/10.1016/j.tate.2024.104858
• Shekell, C., Mikeska, J.N., & Bharaj, P.K. (2024). Examining what and how secondary science preservice teachers learn from using a suite of online simulations. Journal of Science Teacher Education. Early online view
• Mikeska, J.N., Lottero-Perdue, P.S., & Kinsey, D. (2024). Using videos as a tool for self-reflection: The nature of in-service elementary teachers’ reflections on their ability to facilitate argumentation-focused discussions in a simulated classroom. Journal of Science Education and Technology. Online view. https://doi.org/10.1007/s10956-023-10085-6
• Mikeska, J.N., Howell, H., & Kinsey, D. (2023). Inside the black box: How elementary teacher educators support preservice teachers in preparing for and learning from online simulated teaching experiences. Teaching and Teacher Education, 122. Advance online publication. https://doi.org/10.1016/j.tate.2022.103979
• Mikeska, J. N., Howell, H., & Kinsey, D. (2023). Do simulated teaching experiences impact elementary preservice teachers’ ability to facilitate argumentation-focused discussions in mathematics and science? Journal of Teacher Education, 74(5), 422-436. https://doi.org/10.1177/00224871221142842
• Mikeska, J.N., Shekell, C., Dix, J., & Lottero-Perdue, P.S. (2022). “Unnatural how natural it was”: Using a performance task and simulated classroom for preservice secondary teachers to practice engaging student avatars in scientific argumentation. Journal of Technology and Teacher Education, 30(3), 341-376. https://doi.org/10.70725/567745yfcaed
• Mikeska, J.N., Beigman Klebanov, B., Bhatia, A., Halder, S., & Suhan, M. (2025, July 22-26). Evaluating the use of generative artificial intelligence to support learning opportunities for teachers to practice engaging in key instructional skills. In A.I. Cristea, E. Walker, Y. Lu, O.C. Santos, & S. Isotani (Eds.) Artificial Intelligence in Education. Artificial Intelligence in Education, Proceedings, Part II (pp. 378-391). AIED 2025. Lecture Notes in Computer Science, vol 15878. Springer. https://doi.org/10.1007/978-3-031-98417-4_27
• Mikeska, J.N., Beigman Klebanov, B., Marigo, A., Tierney, J., Maxwell, T., & Nazaretsky, T. (2024). Exploring the potential of automated and personalized feedback to support science teachers in learning how to attend to student ideas equitably and responsively. In Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (Eds.) Artificial Intelligence in Education. AIED 2024. Lecture Notes in Computer Science, vol 14830. Springer, Cham. https://doi.org/10.1007/978-3-031-64299-9_19
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