Using AI to shape schools

Christine, why is it important for teachers to keep up to date with developments in language-based artificial intelligence?

Artificial intelligence is changing rapidly, and it's infiltrating all the technologies we already use. With previous technologies, teachers still had the choice to incorporate them into their teaching. With AI, it's already here; it's part of students’ everyday lives. For them to participate in society and use this tool for good, they need to become conscientious consumers and ethical developers. Our research team at Stanford, led by Stanford Accelerator for Learning Faculty Affiliate and Education Professor Victor Lee, is very focused on the concept of AI literacy: What do students need to know and understand to be AI literate? What are teachers’ AI literacy needs, and what do they need to know about what students need to know so that they can teach them? What about teacher trainers and school leaders?
 

In September, the Stanford Accelerator for Learning in collaboration with the Robert Bosch Stiftung, launched the project “M.E.T.A.,” a one-year professional development program for teacher trainers in Germany. Why focus on teacher trainers?

In our discussions with the Robert Bosch Stiftung, it became clear that they were most interested in system-wide change. And teacher trainers are the ones we believe can have the greatest impact because they work with so many teachers themselves. Teacher trainers also have a systemic view and will to be able to see ways to change parts of the system—hence the name “M.E.T.A.”

This course introduces teacher trainers to how AI works, including exploring specific tools. Participants examine the ethical implications of AI, such as data privacy and biases, and learn how to critically evaluate the sources of data used in AI models. They also explore practical considerations: deciding when it’s appropriate to use AI for a task and when human judgment should take precedence. We try to foster a creative mindset—thinking broadly about the possibilities of AI, but also carefully deciding when and how to apply it in teaching and learning contexts. We also help participants understand what transformative professional learning looks like in general.

How is the program structured and what pedagogical approaches do you use?

The program launched in September with an in-person session in Stuttgart, Germany, where participants engaged in collaborative, hands-on activities. We believe in putting participants in the role of learners so they can experience the methods we hope they will use with their teachers. The first session focused on community building, as research shows that adults learn best when they feel part of a connected, supportive group.

Throughout the year, participants meet remotely and work in peer groups organized by region or by the type of teachers they train. These peer groups serve as ongoing support systems where participants share experiences, exchange feedback, and troubleshoot challenges. Our pedagogy emphasizes observing a new practice, rehearsing it in a safe space, applying it in real-world settings, and then reflecting on and refining it. For example, we asked participants to interview the teachers they work with, asking them about their interests and what they hope to learn about AI. Many participants found this practice enlightening, as it helped them tailor their training to better meet teachers’ needs.

How do you ensure that the program builds a sustainable network for participants?

The Robert Bosch Stiftung played a key role in recruiting teacher trainers from across Germany and ensured the cohort was diverse and well-connected. From the beginning, we have focused on creating a network among participants through shared experiences and opportunities to collaborate. Another way we ensure sustainability of the program is by involving alumni in the training of future cohorts. For the next cycle, four teacher trainers from this inaugural cohort will step into roles as facilitators and coaches. By embedding this mentorship structure, we create a ripple effect in which each cohort not only learns but also contributes to the program’s ongoing growth. This approach ensures that even if Stanford eventually steps back, the program can continue independently, with each generation of participants supporting the next.

As part of your work, you and a delegation of Stanford colleagues traveled to Germany last summer to compare approaches to integrating AI into education. What did you learn during your visit?

One thing that stood out was the depth of teacher preparation in Germany. Teachers undergo extensive training before they enter the classroom. In the U.S., teacher training is often less extensive, and there is more emphasis on professional development after teachers enter the workforce – which Germany doesn’t seem to emphasize as much.

At the same time, I was struck by the universality of many of the challenges in education. Whether in Germany or the U.S., educators share the same goals: a deep commitment to students’ success and a desire to prepare them for the future. Despite differences in language and culture, it was inspiring to see how educators in both countries speak a common language when it comes to their aspirations for their students.