Navigating the ethical landscape of generative AI is the foundational step in responsible integration. This theme addresses the critical “guardrails” required to protect student data, maintain academic integrity, and align with university standards. The primary considerations here involve understanding data privacy, ensuring that sensitive intellectual property and student records are not used to train external models, and transparency, which involves setting clear, documented expectations for how and when AI may be used in your course. By establishing these boundaries early, you create a classroom environment where innovation is balanced with digital citizenship and institutional safety.
A central guide to the generative AI services officially supported by the university. Use this to identify which tools have been vetted for security and are recommended for campus use.
Essential reading for understanding how different types of university data (from public to restricted) can be handled. This resource helps you determine what information is safe to input into AI systems.
Still not sure how to craft your AI Policy? Explore this collection of diverse policy statements from across various universities.Â
