Newswise — As one of Penn GSE’s newest faculty members, Shiyan Jiang is helping expand what AI education can look like—and who it’s for. She doesn’t believe AI should be confined to computer science classes or taught only by tech experts. Instead, she’s designing curriculum and professional learning that help educators across all subjects bring AI into their classrooms in ways that are accessible, meaningful, and rooted in identity. In this Q&A, Jiang shares her vision for inclusive AI education, what drew her to Penn GSE, and why she believes every teacher can be an AI educator.

Q: What first sparked your interest in education and technology?

A: What sparked my interest wasn’t just the excitement of building software or exploring new technologies—it was the realization that technology can transform how we experience learning. 
As an undergraduate, I was deeply engaged in developing augmented reality tools that made textbooks come alive—books that didn’t just sit on a desk, but responded to the reader, invited interaction, and sparked curiosity. That experience taught me something powerful: when students can see, touch, and engage with ideas in dynamic ways, learning becomes more meaningful. It showed me that technology, when thoughtfully designed, can be a bridge between what students know and what they could imagine.

Q: Can you share a project or area of research you’re especially excited about right now?

A: I’m especially excited about our work on integrating AI across the curriculum in ways that are accessible, meaningful, and sustainable. We started with a simple but powerful idea: AI should be for all students—not just those in computer science classes. So, we created an AI curriculum for high school English Language Arts. But we soon realized that no single subject can capture the full range of what it means to understand AI. That led us to design short, discipline-specific modules—AI for ELA, AI for math, and AI for history—where each teacher contributes just 3–5 hours from their own subject lens, while students gain exposure to AI from multiple perspectives.

What excites me most now is shifting the narrative: AI doesn’t need to be a separate, specialized topic. It can show up in everyday teaching—through a 5-minute classroom discussion or a full 15-hour unit. 
Every teacher has the potential to be an AI educator. So now, my work is focused on supporting teachers in building their AI educator identity, while also studying how students develop their AI learner identities. Because if we want AI education to be truly inclusive, we need to empower every teacher—not just a few—to bring AI into their classrooms.

Q: What drew you to Penn GSE?

A: What drew me to Penn GSE is its bold vision for what education can be. The school’s commitment to interdisciplinary research and leadership in educational innovation aligns perfectly with how I approach my own work—blending AI, data science, and learning sciences to reimagine what’s possible in classrooms.

I’ve long admired the translational work happening at Penn GSE. It’s a place where big ideas are not only welcomed but also supported and scaled. I see tremendous potential to partner with faculty, students, and practitioners at Penn GSE to keep pushing the boundaries of how we think about AI and the future of learning.

Q: How do you hope your work will make a difference for educators or students?

A: I want every student—not just those in computer science classes—to have the opportunity to understand, question, and shape the role of AI in their lives. My work is about making AI education more inclusive and relevant, so that students from all backgrounds can see themselves in AI careers and feel empowered to use data and technology to tackle the issues they care about.

For educators, I hope to remove the barriers that make AI feel intimidating or “not for them.” I design tools, curricula, and professional learning experiences that support teachers in confidently bringing AI into their classrooms—regardless of their subject area or technical background. Because ultimately, if we want AI education to reach all students, we need to equip all teachers to be part of that journey.

Q: What’s something about your research that you wish more people understood?

A: I wish more people understood that education—whether we’re teaching AI, data science, or any other subject—is not just about delivering knowledge. At its core, my research is about creating spaces where students can reflect on who they want to become. When we design learning experiences or build educational tools, the goal isn’t just to teach technical skills—it’s to help students see themselves as capable, curious, and powerful contributors to their communities and the world. Whether they end up working in AI or not, I want students to leave the classroom with a stronger sense of identity, agency, and purpose.

Q: Outside of work, what’s something you’re passionate about or enjoy doing?

A: Outside of work, I love creating creative data visualizations. For me, it’s not just about charts and graphs, but about finding beauty in patterns and helping people see information in new ways. 
I’m also a passionate traveler—especially a food traveler. Wherever I go, the first place I want to visit is the local market. I love experiencing a culture through its food: the ingredients, the smells, the way people gather and cook. It’s a joyful and delicious way to connect with people and places.

Q: When you think about the growing role of AI in education, are you more hopeful or concerned—and why?

A: Honestly, my feelings have evolved over time—just like many others navigating this space. There have been moments when I’ve felt incredibly hopeful, seeing all the new possibilities AI opens up for teaching and learning. And there have been moments of concern, especially when I see issues of bias and over-reliance surface. 

But I’ve come to realize that this kind of fluctuation is part of the exploration process—it’s normal when we’re working with something so new and transformative. Overall, I consider myself an AI optimist. I believe in engaging with these technologies thoughtfully, trying things out, and learning through doing. For me, it’s not about waiting for perfect solutions—it’s about building better ones through reflection, iteration, and inclusive design.