Recap
From Policy to Practice: A Framework for AI in Academic Tasks
As AI use accelerates across higher education, institutions face a critical challenge: how to guide responsible and effective use of AI in academic work. In our recent webinar, From Policy to Practice: A Framework for AI in Academic Tasks, panelists from the Medical University of South Carolina (MUSC) shared how they developed and rolled out a campus-wide framework to align AI use with their academic and professional mission.
Hosted by Brett Christie, Ph.D., the session invited educators and administrators to explore how policy can move beyond a written document to become an actionable guide for faculty and students. The MUSC team described their collaborative process and shared insights for other institutions navigating similar transitions.
Key Takeaways
1. Define “academic tasks” broadly.
The MUSC framework deliberately uses the term academic tasks rather than just “assignments” or “assessments.” This inclusive language captures the full range of academic activities where AI might play a role—from lab reports and presentations to clinical simulations and research projects.
2. Build the framework collaboratively.
The policy emerged from partnership among CATL leadership, executive sponsors, and engaged faculty and instructional designers. This cross-role collaboration ensured that the framework reflects multiple perspectives and gains buy-in across the campus.
3. Provide clear, faculty-facing guidance.
Rather than a static policy document, the framework serves as a practical resource for instructors. Faculty can adapt sample language and scenarios to their own courses, making it easier to communicate expectations and integrate AI thoughtfully.
4. Address challenges and lessons learned.
The MUSC team acknowledged that early implementation brought questions about equity, consistency, and how to keep pace with AI’s rapid evolution. Their approach includes continuous feedback and iteration to refine guidance over time.
5. Engage the campus community.
Participants in the webinar highlighted the importance of interactive opportunities—Padlet discussions, polls, and live Q&A—that mirror MUSC’s own commitment to campus-wide dialogue and shared ownership of AI policy.
Why It Matters
Moving from policy to practice is essential for institutions that want to harness AI’s potential while safeguarding academic integrity. MUSC’s experience shows that successful frameworks are living documents built collaboratively, applied flexibly, and updated as technologies and teaching practices evolve.
What’s Next
For colleges and universities exploring their own AI policies, MUSC’s model offers both inspiration and a roadmap. Their framework can be adapted to different institutional contexts and provides a starting point for meaningful conversations about AI’s role in learning and assessment.
If you missed the live discussion, you can watch the full recording on our YouTube channel to gain deeper insights and actionable strategies.
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