From Policing to Pedagogy: Reframing Artificial Intelligence in Higher Education
By Keith Hoell, Assistant Professor and Chair, Management and Information Technology Department, St. Francis College, Brooklyn, NY
Artificial Intelligence (AI) has rapidly become one of the most disruptive forces in higher education. Instructors across disciplines are grappling with a fundamental question: Is AI a threat to academic integrity, or an opportunity to enhance learning?
For many institutions, the initial response has leaned toward caution — if not outright resistance. Some faculty have banned student use of AI tools entirely, and most rely heavily on AI detection platforms to identify potential use or misuse. While these approaches are feasible, they risk missing a larger opportunity. AI is not simply a challenge to manage, but it is a capability to teach.
At St. Francis College, where I am an Assistant Professor and Chair of the Management and Information Technology Department, we have taken a different approach: rather than positioning AI as something to police, we integrate it into the learning process itself.
The Role — and Limits — of AI Detection
AI detection systems are not definitive proof of misconduct. They operate probabilistically and can produce false positives or fail to detect AI-assisted content altogether. When used improperly, they can shift the classroom dynamic from one of trust and learning to one of surveillance and suspicion.
In our courses, detection tools are used as a starting point for conversation, not as an endpoint for judgment. More importantly, they are not the centerpiece of our AI strategy. Instead, we emphasize transparency, expectations, and education.
Teaching AI as a Skill, Not a Shortcut
We clearly teach students how and when to use AI, rather than discourage them from doing so. In our introductory computer course, students complete an AI module that outlines ethical use, limitations, and appropriate applications. This AI module has been standardized across course sections to ensure all students understand these aspects.
We frame AI as a learning partner, not a replacement for effort. Students are expected to engage with the material, think critically, and use AI to support their understanding.
Students engage with AI more responsibly and effectively when they understand that it is part of the learning environment rather than something to hide.
Artificial Intelligence is not a passing trend, as it is rapidly becoming part of both the infrastructure of education and the workforce. The question is no longer whether students will use AI, but how they will use it.
Applying AI to Real-World Data: The NYC Pothole Project
One of the most impactful ways we integrate AI is through applied data analysis using real-world datasets. We encourage faculty who teach the introductory computer course to choose a public dataset for their students to work with. For example, in my introductory computer course, students work with publicly available data from the NYC Open Data platform, focusing on 311 pothole complaints. (In New York City, a “311” request refers to a non-emergency service call to the city, such as reporting a pothole, noise complaint, or sanitation issue).
The “311 dataset” includes variables such as when a pothole was reported, when it was repaired, and where the request originated. Students download the data as a simple spreadsheet; however, when paired with AI tools, it becomes a powerful medium for inquiry. We encourage students to use various AI tools to analyze the data, like Open AI’s ChatGPT, Microsoft Copilot, or Google Gemini.
Students use AI to:
- Identify patterns in response times
- Compare service levels across boroughs
- Detect seasonal trends (e.g., winter-related spikes)
- Explore potential disparities in municipal/borough response
Through guided prompts, AI can provide insights such as:
- Longer repair delays in high-volume areas
- Correlations between weather patterns and complaint frequency
- Variations in response time that may suggest inequities across neighborhoods
These variations may not often be determined through the “naked eye.”
The learning outcome is not simply about generating answers. It is about evaluating those answers. Through written analyses, students are encouraged to question AI-generated insights, identify possible biases, and consider what additional data might be needed to draw valid conclusions.
This helps students not only develop critical thinking, writing, and technical skills, but also data literacy and ethical awareness.
Culturally Relevant Computing Meets AI
Another powerful application of AI in our courses emerges through culturally relevant computing (CRC). In this project, students, most of whom are not technology majors, create simple HTML webpages that showcase elements of their culture, such as food, traditions, clothing, or personal heritage.
This assignment provides an accessible and meaningful entry point, especially for students who initially struggle with basic computing concepts. It also helps connect technical skills to personal identity.
Students submit their code to an AI of their choice and ask targeted questions such as:
- “How can I improve the accessibility of this webpage?”
- “What changes would make this more visually engaging?”
- “How can I organize this code more effectively?”
The results are often transformative. Students who once felt intimidated by technology begin to see themselves as creators. They iterate on their work, experiment with improvements, and take ownership of their learning in ways not always achieved through traditional instruction alone.
Most importantly, they begin to recognize AI not as a crutch or tool to deceive, but as a collaborator in the creative process.
Balancing Opportunities and Risks
The integration of AI into the classroom is not without challenges. There are legitimate concerns about over-reliance, uneven access (e.g., the “digital divide”), and the potential erosion of critical thinking and technology skills. Faculty may also face a steep learning curve in adapting their teaching methods to incorporate AI effectively.
However, I believe the benefits exceed the concerns. I’ve noticed an immediate enhancement of student engagement, critical thinking, technical skills, and creative expression. Additionally, faculty come to appreciate their own professional development by adapting their teaching methods to accommodate these effective uses of AI.
Looking Ahead
Artificial intelligence is not a passing trend, as it is rapidly becoming part of both the infrastructure of education and the workforce. The question is no longer whether students will use AI, but how they will use it. Our responsibility, as educators, is to guide that process.