Designing AI-Integrated Learning Experiences for Pre-Service Teachers: A Real-Classroom Example
Authors: Teddy Kim, Yerin Seung, and Dr. James Basham; info@ciddl.org
Artificial Intelligence (AI) is no longer a futuristic concept but a present reality that is reshaping how we teach and learn. Dr. James Basham, Yerin Seung, and I recognized that for pre-service teachers, developing a proactive attitude and the ability to leverage AI are fundamental competencies they must possess before entering the field. We designed our course to go beyond mere tool use, aiming to foster deep-seated AI literacy that balances technological benefits with pedagogical integrity. This post explores how we integrated AI into our curriculum to prepare the next generation of educators for a tech-driven classroom.
A Proactive AI Policy: Encouraging Transparency
Our journey began with brief AI literacy training, followed by establishing a clear AI policy that reflected our progressive stance on technology. Rather than restricting students’ use of AI, we explicitly guide them toward responsible use by requiring transparency: students document their process and explain the role AI played in their final outputs. We believe accountability is the cornerstone of responsible AI use in professional settings. To push this concept further, Dr. Basham even introduced a bonus-point system for students who could use AI to identify loopholes in assignments and demonstrate how to "cheat or bypass” them. This exercise was not about encouraging dishonesty; it was a strategic way to help students understand the limitations of current assessment models and the capabilities of the tools on their own. This also helped us, as course designers, identify issues in our design. If students can simply use AI to cheat, we likely need to redesign that component of the course.
Designing with Purpose: Backward Design and Core Frameworks
Once the policy was set, we utilized the Backward Design framework to structure the entire course. We started by identifying the ultimate learning outcomes—the specific skills we wanted students to master in this course—and then developed flexible assessments to measure those competencies. With the learning goals and assessments in place, we mapped out the lessons and skills needed to support students in achieving the outcomes. Specifically, the assignment was designed to assess students’ ability to apply Universal Design for Learning (UDL), High-Leverage Practices (HLP), and Evidence-Based Practices (EBP) to support students with disabilities in the classroom. By grounding the curriculum in these frameworks, we sought to ensure that the technology served the pedagogy, rather than the other way around.
The assessment design was also informed by the Inversion, Substitution, Augmentation, and Redefinition (ISAR) model (Bauer et al., 2025). The ISAR model describes four ways AI integration can shape cognitive engagement in AI-supported instruction. We focused specifically on redefinition, which refers to AI enabling forms of deep learning that would not otherwise be possible, particularly through simulation and collaboration. This concept inspired us to design an AI-based student simulator. Pre-service teachers could interview the AI as if they were interacting with a student, allowing them to explore learner variability in a more interactive and situated way. Through these simulated interactions, students could develop the understanding needed to design instruction grounded in UDL, HLPs, and EBPs.
The AI Simulator: Safe Practice through Custom GPTs
To operationalize this idea, we developed custom GPTs based on specific student case studies. Using "My GPTs," we trained a simulator on specific case studies, instructing the AI to mimic the diverse behaviors and needs of students described in our research. Pre-service teachers could then interact with this simulator in a safe environment to test their lesson plans in real-time and see how different "students" might respond. This allowed them to iterate on their designs and verify if their strategies truly supported the needs of every learner in the case study. It provided a low-stakes environment for high-stakes learning, giving them the confidence to refine their approach based on simulated feedback.
Generative AI and Human Agency: Crafting Instructional Materials
In the final phase of the project, students were tasked with creating five comprehensive lesson plans and accompanying instructional materials using Generative AI tools (e.g., Gemini, ChatGPT, Claude). Without prescribing any tool, we challenged the students to produce materials ready for immediate use in a real classroom, emphasizing that every output must adhere to UDL, HLP, and EBP standards. Students engaged in a rigorous process of prompt engineering and multiple revisions to ensure the AI-generated content met their specific educational goals. Rather than evaluating only the final products, we asked students to submit their prompts, document the revisions they made, and explain why they accepted, rejected, or modified AI-generated suggestions for their transparency. This process encouraged them to critically evaluate AI outputs, align those outputs with UDL, HLP, and EBP principles, and make intentional decisions. In this way, students developed AI literacy, strengthened their professional judgment, and exercised human agency by critically evaluating AI-generated results, deciding what to keep or change, and refining the outputs.
All of the students in the course demonstrated thoughtful integration of AI within their five-day unit. Throughout the course, we asked students to reflect on their understanding of AI and their feelings about this technology. This reflection took place both during assignments and during class sessions. We found reflection important for understanding students' baseline knowledge and perceptions of AI. This practice also opened conversations about the need for teacher AI literacy, humans-in-the-loop, and the ethics of using this powerful technology in practice.
Conclusion
This collaborative teaching experience demonstrated that when AI is integrated thoughtfully, it can significantly enhance pre-service teachers' AI literacy. While the requirement for a five-session lesson plan was ambitious for undergraduate students, AI support enabled them to reach a level of detail and quality that would have been difficult otherwise. They didn't just learn about AI; they lived the experience of balancing technology with human insight. We are committed to continuing this work, ensuring that future teachers are not just consumers of technology but empowered leaders who can navigate the complexities of an AI-enhanced educational world.
We encourage you to support the purposeful integration of AI in your classes. Requiring the purposeful use of AI, encouraging preservice teachers to explore its uses, and opening up student dialogue and voice around this technology are important for shaping the future of the educator workforce. Once you try something, we'd love to hear from you. Join our community and share your experiences.
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