A CUNY AI Lab workshop on demystifying AI and crafting thoughtful classroom policies.
This workshop provides a brief introduction to generative artificial intelligence and helps instructors craft thoughtful and responsive policies for their classes. Together, we will work to demystify AI basics through interactive visualizations and hands-on tinkering, followed by discussion of sample policy statements and their implications across disciplines.
Workshop Date: Tuesday, October 1, 2025 Duration: 90 minutes Format: Interactive workshop with visualizations and policy drafting Facilitator: Zach Muhlbauer
This workshop was originally conducted as part of the Teaching and Learning Center at the CUNY Graduate Center.
The CUNY AI Lab is a faculty and staff-led incubator for experimentation with AI by CUNY researchers across disciplines, strengthening CUNY's position at the leading edge of technological innovation. Located at the CUNY Graduate Center and led by experienced scholars and technologists who have built widely used open publishing platforms, the Lab fosters momentum for AI experimentation while foregrounding ethical practices and values that reinforce CUNY's mission of accessible, equitable, and transformative education.
This workshop is part of the CUNY AI Lab's commitment to developing critical AI literacy among faculty, staff, and students across the university system.
This workshop addresses two critical needs: understanding how AI systems actually work, and creating clear classroom policies that support student learning while acknowledging AI's presence. Through interactive visualizations and collaborative policy drafting, participants will develop both technical literacy and practical frameworks for their teaching.
- Understand AI capabilities and limitations through interactive exploration
- Analyze the human cost of detection-based policies using student testimonials
- Create clear, supportive AI policies with attribution guidelines
Activities:
- Completion game and neural network visualization
- AI technology overview
- How Large Language Models (LLMs) work
Key Concepts:
- Understanding what LLMs can and cannot do
- Neural network fundamentals through visualization
- Common misconceptions about AI detection
Activities:
- Student testimonials and detection problems
- Policy components and values
- Draft your syllabus statement
Focus Areas:
- Attribution guidelines for AI use
- Use cases and boundaries
- Values-based policy language
- Defining critical AI literacy in your discipline
All materials are licensed under a Creative Commons Attribution-ShareAlike (CC-BY-SA) 4.0 International Public License.
This folder contains:
- Workshop outline and plans
- Presentation slides
- Sample policy statements
- Educational Technology
- Course Planning
- Policy Development
For questions or facilitation inquiries, contact the Teaching and Learning Center at the CUNY Graduate Center.