AI Literacy in Teacher Preparation
Dr. Victor Lee
About Dr. Victor Lee
Dr. Victor Lee is an Associate Professor at Stanford University’s Graduate School of Education and a Faculty Lead for the Stanford Accelerator for Learning’s AI and Education initiative. His research focuses on data literacy, K–12 data science education, and AI literacy for both students and teachers. Dr. Lee is a nationally recognized expert actively shaping the AI literacy landscape through his involvement in collaborative projects, such as Stanford’s CRAFT initiative. His work not only bridges research and practice but also offers actionable models for equitable, context-driven AI integration across educational settings.
The problem highlighted in this brief
As Artificial Intelligence (AI) becomes increasingly integrated into daily life and educational settings, teacher preparation programs face a critical challenge: ensuring that pre-service educators are equipped to navigate, integrate, and critically assess AI in their practice. AI literacy refers to the ability to understand, utilize, and critically evaluate AI technologies, including their functionality, societal implications, and how to engage with them responsibly (Long & Magerko, 2020; Ng et al., 2021). However, many in the education field still approach AI with uncertainty, often viewing it as a potential threat to academic integrity. As Dr. Victor Lee noted, misconceptions about AI's capabilities, such as assuming it is highly intelligent or inherently subversive, can limit meaningful integration. Without shared understandings, practical frameworks (e.g., AILit Framework, CODE.org), and intentional dialogue, there is a risk of reducing AI to a reactive concern rather than embracing it as a powerful tool for teaching, learning, and innovation.
Why does this topic matter to teacher preparation?
To prepare future-ready educators, it is essential to shift from reactive approaches to proactive and intentional training on AI Literacy (Sperling et al., 2024). Understanding how AI systems work, their impact on learners, and how to use them ethically is central to fostering critical and informed teaching practices. AI literacy should be integrated across pedagogical tasks, subject content, and classroom interactions, rather than being isolated in standalone courses (Laupichler et al., 2022). This matters even more in special education contexts, where ethical concerns such as privacy, representation, and student support are particularly heightened.
About This Brief
This brief offers an in-depth examination of how AI literacy is being conceptualized and implemented in teacher education, drawing on the research and leadership of Dr. Victor Lee. Readers will gain a comprehensive understanding of the nuanced challenges that teacher preparation programs face in integrating artificial intelligence, encompassing concerns about academic integrity and instructional design. Dr. Lee’s perspective highlights the ongoing complexity in defining AI literacy and underscores the need for educators to move beyond functional skills toward more ethical, inclusive, and contextually grounded applications.
In addition, readers will discover how AI literacy can be implemented through strategies such as co-designing curriculum with teachers, embedding lessons in existing subjects, and modeling responsible use in classroom settings. Dr. Lee highlights the importance of shifting AI’s role from a perceived intelligent agent to a practical instructional tool. The brief also identifies emerging priorities in AI literacy research, particularly the development of assessment tools and the need to align AI integration with values such as transparency and student well-being. Educators, policymakers, and faculty alike will find this brief a valuable resource for reimagining the future of teacher preparation in a world surrounded by AI.
Research and Practice Context
AI Literacy in Teacher Preparation
The conversation delves into the intricate landscape of AI in education, where concerns about cheating and misuse frequently dominate public discourse. Dr. Lee advocates for reframing AI as a tool rather than an intelligent actor, urging educators to emphasize transparency, student well-being, and alignment with pedagogical goals. His work also highlights the importance of co-design with teachers and the development of scalable, discipline-integrated learning modules.
Conversation with Dr. Victor Lee
Q1: What key issues are you addressing through your work on AI literacy in teacher coaching and preparation?
Dr. Lee highlights that effective AI literacy must be understood through three key perspectives: the user, the developer, and the critic. He points out that while interest in AI literacy is growing, its meaning and practical definition remain unclear, with no shared consensus or structured framework on what to teach or assess. By naming these distinct roles, Dr. Lee emphasizes the need for a multidimensional and intentional approach, one that prepares educators and students not only to use AI tools but also to understand how they are built and critically engage with their broader social and ethical implications.
Dr. Lee: “One of the things that I speak a lot about with AI literacy is that in our debates about what is AI literacy, there's three perspectives that tend to be prominent. One is that of the user of AI. One is that of the developer of AI, and one is that of the critic of AI.”
Q2: What is needed for AI literacy in teacher preparation, and how might it impact both teachers and students?
Dr. Lee emphasizes that a key goal of AI literacy is not simply teaching how to use AI, but rather redefining what AI truly is, what it is capable of, and what critical issues must be considered in its use. This recalibration calls on educators to move beyond surface-level engagement with AI tools and instead foster a deeper, more reflective understanding. It involves addressing common misconceptions, such as assuming AI outputs are always reliable, and recognizing concerns, including algorithmic bias, copyright issues, and privacy regulations, particularly in sensitive areas such as special education. For Dr. Lee, this reflective stance is essential for preparing pre-service teachers to integrate AI meaningfully into all aspects of teaching, curriculum, and student interaction, while staying grounded in ethical and pedagogical responsibility.
Dr. Lee: "So in this push for AI literacy is to recalibrate what it is that AI is and what it can do, and the sorts of concerns and considerations that we should foreground."
Q3: How do you see AI literacy being integrated into teacher preparation programs, and how can we assess it?
Dr. Lee emphasizes that AI should not be treated as a standalone subject isolated from the rest of teacher preparation. Instead, he advocates for a holistic integration of AI into every aspect of pedagogy, from lesson planning and subject-area instruction to student support and classroom relationships. This approach ensures that AI is not just an add-on or technical elective, but a meaningful thread woven into the fabric of teaching practice. He also warns against the isolated use of AI and encourages educators to think carefully about both its potential and its limitations in real classrooms. By embedding AI literacy across pedagogical contexts, teacher education can more effectively prepare future educators to navigate AI's role in schools with flexibility, ethical awareness, and instructional purpose.
Dr. Lee: “What we don’t want to do is create a new AI course for pre-service teachers that becomes a requirement and kind of lives sequestered from all of the other things. Rather, it's how do we think about AI when we think about the whole range of pedagogical tasks? How do we think about AI within the subject matter areas? How do we think about AI with respect to student well-being or student interaction?”
Q4: What advice do you have for programs just beginning to explore AI literacy? What are common misconceptions?
Dr. Lee identifies two essential entry points for integrating AI into teacher preparation: first, reframing AI not as an all-knowing intelligence but as a practical tool; and second, ensuring instructional activities remain transparent, aligned with learning goals, and thoughtfully structured. He encourages educators to explore AI’s documented capabilities, identify competencies that directly support intended learning outcomes, and begin embedding these insights into their own instructional practices. Ultimately, he emphasizes the importance of open, collaborative dialogue across teacher education programs to define and refine the responsible use of AI in preparing future educators.
Dr. Lee: "What I would encourage is to start getting acquainted with what are some of the capabilities that you see... and then begin to formalize integration of those into your own teaching and promote an open dialog within a larger program.”
Q5: Where is AI literacy research headed, and what areas should we explore more?
Dr. Lee emphasizes that while numerous frameworks are emerging to define what AI literacy should look like today, ranging from managing and creating with AI to exploring it in classroom practice, the real challenge lies in developing robust assessment systems to track learning progress. He foresees a growing need for design-based research and accessible resources that support teacher engagement with AI. However, he argues that the most pressing issue is societal: we must clarify the ultimate purpose of AI literacy. Whether it's preparing students for future careers, fostering critical thinking, or combating inequities and misinformation, AI education must be aligned with broader social values and goals.
Dr. Lee: “There is kind of a larger looming question that I think as a society we need to reckon with, which is, why do we care about AI, is it for the jobs of the future, or is it for more informed decision making? Is it to address issues with inequities or misinformation?”
Q6: What is Stanford’s CRAFT initiative, and how does it support AI literacy?
Dr. Lee introduces the CRAFT initiative as a practical model for integrating AI literacy into existing school structures without requiring a separate, unrealistic course. Instead of isolating AI instruction, CRAFT emphasizes modular, subject-connected learning experiences that reflect current topics, such as AI’s role in medicine or environmental science, while respecting the time constraints and pedagogical practices of educators. He underscores the importance of co-design with teachers to ensure relevance and adaptability in diverse classrooms. Ultimately, CRAFT offers a research-informed, openly available approach that evolves in response to teacher input, helping to bridge immediate needs with long-term, systemic goals.
Resources and References
If you're interested in learning more about Dr. Victor Lee and his team's work on AI and AI Literacy, we highly recommend reading the following publications:
- Chen, R., Lee, V.R. & G Lee, M. A cross-sectional look at teacher reactions, worries, and professional development needs related to generative AI in an urban school district. Educ Inf Technol (2025). https://doi.org/10.1007/s10639-025-13350-w
- Lee, V. R., Pope, D., Miles, S., & Zárate, R. C. (2024). Cheating in the age of generative AI: A high school survey study of cheating behaviors before and after the release of ChatGPT. Computers and Education: Artificial Intelligence, 7, 100253.
- Xie, B., Sarin, P., Wolf, J., Garcia, R. C., Delaney, V., Sieh, I., ... & Lee, V. R. (2024, March). Co-designing AI education curriculum with cross-disciplinary high school teachers. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 21, pp. 23146-23154).
Suggested Citation
Kim, T., Lee, V. R., & the CIDDL Team. (2025). AI Literacy in teacher preparation. The Center for Innovation, Design, and Digital Learning.
This work is licensed under a Creative Commons Attribution 4.0 International License.