This piece is part of a series by participants in the Winter 2026 Open Knowledge Fellowship, coordinated by the Mina Rees Library. Fellows will share insight into the process of converting a syllabus to openly-licensed and/or zero-cost resources, as well as their experiences teaching undergraduate courses at CUNY.
Qiwen (Thomas) Tong is a CUNY Graduate Center PhD student and Experimental Psychology lab instructor at Brooklyn College, focusing on using open educational resources and student-centered, collaborative learning in the classroom.
I grew up in a family of public-school teachers, so I absorbed pretty early that education is something you share, not something you gate behind a paywall. That background has shaped how I think about my role at a public university and why providing open access to educational resources feels less like an unrealistic stance and more like a basic condition of learning. I’ve been teaching Experimental Psychology for a year now, and I was already piecing together a “low-cost” course: a free journal article here, an online textbook there. The more I watched students weigh whether to purchase a $150 textbook they would only use for one semester, the less comfortable my “low cost” approach felt. I wanted to move from that kind of patchwork to a deliberately Zero-Textbook-Cost course, where every reading, video, and dataset was chosen with intention rather than desperation.
Going into the Open Knowledge Fellowship, my understanding of ‘openness’ was quite shallow. The program made it clear that it could mean a multitude of things. The sessions walked us through licensing, copyright, and the difference between Open Access and remixable and revisable OER in a way that was practical for teachers: how to find materials, how to read licenses, how to build a course that does not depend on hoping no one notices the PDF you uploaded. Just as importantly, I learned where to look for OER – subject repositories, library-curated collections, tools I’d simply never used. Choosing materials started to feel more like designing an experiment and less like rummaging around the internet. The cohort itself was a big part of the learning. Sitting with instructors from biology, history, and other branches of psychology, I realized we are all having the same conversations with our students about authority, access, and what “good evidence” looks like. That sense of shared struggle– and the fellowship’s honesty about the labor involved and the limits of what one instructor can change alone– made the whole experience feel grounded.
The session I was most skeptical about, and that ended up influencing me the most, was on AI and OER. As a methods instructor, my first reaction to AI in the classroom tends to be worry, and the discussion pushed me toward something more nuanced, and not just about how instructors might use AI, but about how students already do. Because here’s what I’ve noticed: students aren’t a monolith when it comes to AI, and pretending otherwise doesn’t serve anyone. Some use it to shortcut the very processes that build understanding– submitting AI-generated lab reports, asking it to interpret data they haven’t actually looked at, treating it as a way to produce outputs without doing the thinking that makes those outputs meaningful. That’s a real concern, and anyone who teaches research methods feels it acutely. But other students are doing something genuinely interesting: using AI to find open resources they didn’t know existed, to get a plain-language explanation of a concept before tackling the primary source, or to orient themselves in an unfamiliar literature before they start reading in earnest. That’s not bypassing learning– that’s scaffolding it, which is something we as instructors try to do deliberately all the time.
What the session helped me see is that this same complexity applies on the resource-design side too. AI isn’t just a threat to the integrity of OER, it’s also becoming a tool for creating and remixing them. I left thinking the real question isn’t “AI or no AI?” It’s “Who is doing the critical evaluation, and do they have the skills to catch what’s wrong?” That happens to be a question Experimental Psychology is unusually well-positioned to take on. We already spend a lot of time asking students to slow down, scrutinize sources, and distinguish between what the data shows and what we wish it showed.
Coming out of the Fellowship, I now think of open education less as “finding free stuff for my class” and more as contributing to a shared, evolving resource pool. My current project is building on my openly accessible course site on CUNY Academic Commons. The hope is that it will be useful to students I never meet and adaptable by instructors beyond my course. That feels appropriately “experimental” to me: try something, document it, share it, and let others build on it.
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