The Professor Leading SUNY’s Charge Against AI Policing

Most universities are still stuck in a defensive crouch, trying to figure out how to catch students using ChatGPT to ghostwrite essays. But Namsook Kim is flipping the script. As one of the newly minted SUNY AI for the Public Good Fellows, Kim isn’t looking for better plagiarism detectors; she’s building a blueprint for how higher education survives—and thrives—in an automated world.

While the rest of academia debates bans, Kim is integrating AI into the “information literacy” core competency at the University at Buffalo. It’s a shift from seeing AI as a threat to seeing it as a mandatory workplace skill. This aligns with broader national efforts, such as advancing AI literacy for future leaders, which aims to prepare students for a tech-driven landscape rather than simply policing it.

The Why: Moving Past the “Punitive” Phase

Higher education faces a crisis of relevance. If a student can prompt their way to an A-, the traditional grading model is broken. Kim’s appointment as a SUNY Fellow signals a system-wide acknowledgment that “AI policing” is a dead end.

The problem isn’t just that students use AI; it’s that they often use it poorly, reinforcing biases or hallucinating facts. These risks highlight the dangers of AI illiteracy, which can lead to declining critical thinking and a wider digital divide. Kim’s work addresses the equity gap. If only the tech-savvy students know how to leverage these tools, the digital divide widens. By bringing AI into the “gateway” writing courses and involving librarians and writing consultants, Kim is ensuring that AI competency becomes a baseline right, not a luxury.

Step-by-Step: How to Build an AI-Empowered Classroom

Kim’s approach isn’t about adding a single “AI day” to the syllabus. It’s about a holistic redesign. Purdue University has also launched similar initiatives to bridge the literacy gap and shift the focus from cheating to innovation. Here is how to implement a similar framework:

Audit Your Literacy Goals. Stop testing for rote memorization. Identify where AI can handle the “grunt work” (outlining, brainstorming) so students can focus on high-level critique and synthesis.
Form a Cross-Disciplinary Strike Team. Kim didn’t work in a silo. She brought together librarians, instructional designers, and residential educators. You need the people who manage information (librarians) to talk to the people who teach writing.
Draft “Student-Ready” AI Guidelines. Move away from “Do not use.” Instead, define “Responsible Use.” When is a prompt an assistant, and when is it a crutch?
Deploy AI-Integrated Open Educational Resources (OER). Use free, open-source materials to ensure every student has the same access to high-quality prompts and AI training, regardless of their ability to pay for a “Pro” subscription.
Measure for Inclusion. Analyze whether AI tools are helping non-native English speakers or first-generation students close the gap, or if they are creating new barriers.

💡 Pro-Tip: Don’t just teach students how to prompt; teach them how to reverse-engineer. Have students generate an AI essay on a topic, then spend the class period fact-checking and critiquing the logic. This builds “AI Skepticism,” which is more valuable than “AI Fluency.”

The “Buyer’s Perspective”: The SUNY Strategy vs. The Rest

The SUNY “AI for the Public Good” initiative stands out because it treats AI as a public utility rather than a private shortcut. This contrasts sharply with recent international trends, such as the Norway AI ban in primary schools, where the government has chosen to return to analog learning to protect cognitive development.

While Ivy League institutions often focus on high-level research and ethics, the SUNY model—led by Fellows like Kim—is remarkably practical. They are focusing on “gateway” courses. These are the classes where students either find their footing or drop out. By targeting these specific friction points, SUNY is betting that AI can actually increase student persistence.

Compared to corporate AI training, which focuses purely on productivity (doing more in less time), Kim’s framework focuses on Communication Literacy. It’s not about writing faster; it’s about thinking clearer with a machine as a sounding board.

FAQ

Is AI going to make student writing worse?
Only if we keep giving the same assignments. If an assignment can be finished in 30 seconds by a bot, it wasn’t a good assessment of critical thinking to begin with. Kim’s work focuses on shifting the goalposts to “AI-empowered” outcomes.

How do we handle the ethics of data privacy in the classroom?
This is a core pillar of the SUNY fellowship. The focus is on using tools responsibly, which includes teaching students where their data goes when they hit “enter” and opting for Open Educational Resources whenever possible. Other institutions, like the University of Arizona through Wildcat Chat, have established high standards for ensuring academic integrity and data privacy.

Does this mean we stop teaching grammar and structure?
No. It means we teach them as foundational skills so students can recognize when an AI is hallucinating or producing “hallmark-card” fluff. You can’t edit what you don’t understand.

Ethical Note/Limitation: Current AI models lack lived experience and cultural nuance, meaning they often fail to capture the “human” element required in high-stakes communication and inclusive pedagogy.