Schools change in the AI powered society, and the teaching experience changes with them.
A 2025 study conducted by the educational resource organization Twinkl reveals a majority of teachers already use AI tools, and find the AI tools to remove stress, yet a vast majority of US based teachers report receiving minimal to no formal AI training from their schools.
This page seeks to explore the evolving role of the teacher in the AI powered society, the need for professional development and the myriad teachers’ points of view with respect to these profound changes.
Expert Opinions:
Megan Cuzzolino
Megan Cuzzolino is a researcher and educator with expertise in cognition and development, instructional design, and public engagement with science. She is the Project Director of the Next Level Lab at the Harvard Graduate School of Education and continues to collaborate with the Causal Learning (CLiC) lab. Her primary research focus is on the emotion of awe and its relationship to learning and motivation in classroom and workplace contexts. Previously, Megan was an elementary and middle school science teacher, a Science Education Analyst at the National Science Foundation, and a research assistant for a number of projects in the lab focused on complex causal learning.
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Megan’s key takeaways:
- AI in elementary education should move students and educators toward the positive version of awe, rooted in curiosity, purpose, and connection. When used well, AI can help both students and teachers feel more empowered by linking their tasks to a larger sense of meaning. AI is not a tool for replacing human to human connection. Rather, AI should be augmenting human interaction in the classroom.
- AI can ease the burden of data and logistics (“reckoning”), freeing up teachers time to focus on the parts of their role that require real human judgment and empathy. Since AI can’t truly understand a student’s emotional state or full complexity, face-to-face interaction will remain essential. Kids are multidimensional beings, and effective learning depends on knowing each student’s goals, strengths, and starting point.
What boundaries should we set?
And how can those rules protect learning?
- To use AI meaningfully in school, students need to be taught to use tools like AI as starting points, not shortcuts. We need to create clear and fair guidelines around appropriate use, with kids actively involved in the conversation. This is a constantly evolving puzzle; rather than saying “do this, not that,” we need to explain the reasoning behind tech policies and encourage reflection. The most powerful learning often comes from difficulty and uncertainty, so we have to preserve that in order to foster growth. When used effectively, AI can remove meaningless busywork, but what’s “meaningless” varies by person. Therefore, discussion around AI needs to be rooted in empathy, critical thinking, and a shared commitment to human-centered learning.
Lior Zalmanson
Lior Zalmanson is an associate professor at the Technology and Information Management Program, Coller School of Management, Tel Aviv University and a visiting fellow at Cornell Tech. His research interests include social media, user engagement, internet business models, human-AI interaction, and algorithmic management. Lior is also the founder of the Print Screen Festival, Israel’s digital culture festival, which connects internet researchers, activists, and artists. Furthermore, he is a grant and award-winning digital artist, playwright, and screenwriter. His most recent VR work (about the bystander syndrome) debuted at the 2021 Tribeca Film Festival.
Lior’s key takeaways:
- AI is no longer just a tool, it’s becoming the invisible infrastructure beneath how students live, communicate, and learn. As AI weaves into daily life, schools have to rethink not just what we teach, but why and how.
- Inside the classroom, the challenges consist of redesigning the curriculum to emphasize social and emotional skills and human connectivity, and to encourage creativity and out of the box thinking, along with critical thinking and ability to assess and criticize AI outputs.
What happens when the assignments themselves change?
And how do we assess knowledge?
- Out of the classroom, while AI is a promising path to reduce administrative load, the path for assessing or enhancing education itself becomes questionable. For example, it is not clear what can be assigned as homework in the AI powered social environment. Who actually did the homework and what are the new metrics of knowledge and of voice for students?