Faculty members across our network are identifying an exciting and timely opportunity: the chance to establish a shared language and clear guidance for addressing approved instructional uses of AI within course syllabi. As individual instructors continue to experiment and innovate with AI in their teaching, HAIL is committed to shining a light on the resources, frameworks, and emerging practices that can empower faculty to lead with confidence — giving students, departments, and institutions a strong, coordinated foundation as AI becomes an increasingly powerful part of the learning experience.
Why Syllabus Language Matters
The course syllabus is more than an administrative document. It is a contract between instructor and student, a signal of institutional values, and increasingly, a site where higher education’s relationship with artificial intelligence is being negotiated in real time. When AI policies are absent, inconsistent, or vague in syllabi, the consequences ripple outward:
- Students receive mixed messages about expectations and academic integrity.
- Faculty feel isolated in making high-stakes policy decisions alone.
- Institutions face reputational and legal exposure from inconsistent application of AI-use standards.
- Opportunities to thoughtfully integrate AI as a legitimate learning tool are missed.
Clear, approved syllabus language gives faculty a foundation to build on as a shared starting point that respects disciplinary variation while upholding institutional coherence.
What Pitt’s Center for Teaching and Learning Has Already Built
The good news for Pitt faculty is that this work is not starting from zero. The University Center for Teaching and Learning (CTL) has developed a substantial and growing library of guidance on generative AI and instruction. Below is a map of the most relevant resources currently available.
Syllabus Language & Policy
- Teaching with Generative AI — CTL’s primary resource for AI syllabus statements. Includes three ready-to-use policy templates ranging from full prohibition to encouraged use, with citation guidance and semester-long transparency strategies.
- Syllabus Checklist — A comprehensive semester-prep checklist that includes a dedicated AI section and links to sample syllabus language faculty can adapt.
- Five Key Generative AI Strategies for Better Teaching — Practical guidance on syllabus policy drafting, student transparency, and assessment redesign to support academic integrity in an AI-enabled classroom.
- AI Academic Integrity Policy Suggestions (Writing Institute) — A focused PDF from Pitt’s Writing Institute offering policy language examples for faculty across disciplines.
Academic Integrity & Enforcement
- Encouraging Academic Integrity (CTL) — Addresses AI detection tools (and their unreliability), how to handle suspected AI misuse, and how to refer cases under Pitt’s existing Academic Integrity Guidelines.
- Academic Integrity Guidelines (Office of the Provost) — The university-wide policy framework that governs all academic integrity cases, including those involving unauthorized AI use.
- Academic Integrity Guide (Library) — A student- and faculty-facing guide to plagiarism, citation, and academic integrity—useful context for writing AI use policies.
Governance & Institutional Strategy
- Ad Hoc Committee on Generative AI in Research and Education — A joint initiative of the Offices of the Provost and Senior Vice Chancellor for Research. The committee—which includes faculty, staff, and students—is developing formal recommendations on AI policy, faculty guidance, and long-term institutional strategy. John Radzilowicz (CTL) and Nora Mattern (HAIL) are both members.
- Considerations for Responsible Use: Generative AI in Research and Education — The committee’s draft guidance document, developed in partnership with the Provost’s office.
- Pitt Guidance on AI: Practicality and Governance (Law Library) — A research guide aggregating Pitt’s institutional AI governance resources, including the Office of Research and the University Senate Committee on Computing and Information Technology.
Teaching Practice & Pedagogy
- Teaching and Learning in the Age of Generative AI — CTL’s foundational pedagogical framework for AI integration, including equity, bias, and accessibility considerations alongside practical classroom strategies.
- Generative AI Events and Resources — A curated and regularly updated bibliography of workshops, readings, assignment examples, and external tools—including the Sentient Syllabus Project and work by Ethan Mollick.
- The Prompt: Readings and Articles (CTL Blog) — CTL’s ongoing blog series helping the Pitt community stay current on ethical and instructional uses of generative AI.
- Using Generative AI to Create More Accessible Learning Experiences (University Times) — A practical piece from CTL’s Lindsay Onufer on using AI to support disabled students and design more inclusive courses—with syllabus policy implications.
Note: A living collection of Sample Assignments That Integrate Generative AI (created by Pitt faculty) is also available through Canvas for those logged into the Pitt system. These and more resources will be available through the Rol-AI-Dex.
Get Involved
If you are a faculty member with insights on what’s working—or not working—at your institution, or if you’d like to be part of the conversations ahead, reach out to HAIL Managing Director Kendra Oliver. This work moves faster and lands better with broad faculty input from across the network.
Key Contacts: Kendra Oliver, HAIL Managing Director | John Radzilowicz, CTL — jgradz@pitt.edu | Nora Mattern, HAIL Associate Director for Responsible Data and AI Practices | CTL General: teaching@pitt.edu
Pitt CTL Generative AI Hub: teaching.pitt.edu/generative-ai-resources-for-faculty