The Medical University of South Carolina is taking a deliberate approach to the use of artificial intelligence, rooted in two strategic priorities that leaders say reflect their commitment to transformative healthcare education.
AI Strategic Goal No. 1 calls for MUSC to pioneer changes in healthcare delivery, research and education through AI.
AI Strategic Goal No. 2 aims to create an AI-competent workforce prepared to thrive in a digital world.
Leaders say that together, these goals position MUSC not just as an adopter of emerging technology but as a force in shaping how healthcare professionals learn to use AI ethically and responsibly.
“The goal of education is to prepare students for authentic problem-solving in the real world,” said Julaine Fowlin, executive director of the Center for the Advancement of Teaching and Learning at MUSC. “We should be asking: What core competencies must our learners achieve? What does evidence of that learning look like? And how does AI fit into that picture?”
Those questions have guided MUSC’s development of the AI Acceptable Use Framework for Academic Tasks. Fowlin said the five-category model moves the conversation beyond cheating and toward AI literacy, transparency and trust.
E’lise Nissen serves as director of AI in Education and Scholarship at MUSC. “Students want clearer guidance about AI use,” she said.
“The framework gives faculty a consistent way to communicate expectations while helping students understand not only what is allowed, but why. When those decisions are tied to learning goals and communicated transparently, students can have greater confidence that AI is being used to support learning rather than replace it.”
Fowlin agreed. “Our role as an institution is to have a centralized way of communicating a mindset and a philosophy rather than telling faculty what to do,” she said.
That mindset led to five categories for acceptable AI use in academic tasks at MUSC.
1. No AI. Examples of where this applies include exams and skills evaluations.
2. AI planning. Students can use AI to plan before beginning a task.
3. AI limited. Students can use AI only for specific aspects of a task. This might help with activities such as designing poster presentations and getting feedback to refine their work.
4. AI extensive. This could apply in situations such as complex data and evidence analysis and helping students to practice making clinical decisions.
5. AI exploration. Tasks in this category require AI to do things like explore its use in healthcare practice.
Every category except the first could potentially require documentation of how AI has been used in a task. For example, the student might submit a log of AI interactions or an explanation of how AI was used.
The framework has been added to MUSC’s Student Guidelines for Plagiarism and Artificial Intelligence.
A collaborative, strategic evolution
This work began under the leadership of former provost Lisa Saladin, PT, Ph.D., and former associate provost Gigi Smith, R.N., Ph.D. It has continued to evolve under the guidance of current provost John Marymont, M.D.; chief artificial intelligence officer Marylyn Ritchie; and associate provost Sharlene Wedin, Psy.D. The AI Acceptable Use Framework emerged from a collaborative effort that started with the revision of MUSC’s plagiarism policy, led by Smith, along with Fowlin and Academic Affairs professor Tom Smith, Ph.D.
The framework was initially informed by the Artificial Intelligence Assessment Scale, published in the Journal of University Teaching and Learning Practice in 2024. It was further adapted using the updated AI Assessment Scale, under the guidance of Marymont, Ritchie and other leaders.
Fowlin said MUSC honed the AI scales to meet the needs of health sciences education and expanded them into a shared language for educators and students. The result is a framework that she said reflects the high stakes of healthcare education, where critical thinking, clinical judgment and patient safety are essential.
Nissen said the framework reflects MUSC’s belief that AI should extend and deepen learning, not replace it. “My goal is for our students to trust us. Right now, many students do not trust higher education to guide them in when, how and why to use AI. I believe the Acceptable Use Framework is one of the most important tools we have for providing that guidance. The more transparently and consistently we use it, the more trust we can build.”
Part of a broader AI education ecosystem
The framework is not a standalone policy. Fowlin said it is one component of a comprehensive AI ecosystem at MUSC.
All new students take a course called AI at MUSC, and employees have access to training that builds AI literacy across the institution. Fowlin said this shared foundation ensures that transparency and responsible use are understood in context, not just mandated from above.
But she knows that not everybody embraces AI, underscoring Nissen’s point. “We have students who are scared of using it because they don’t want to rely on it. In some cases, what we’ve done is ask, ‘If you went into practice, and this is a tool that you had to use, would you not work with that practice?’” she said.
“But we always want them to know that we respect their views.” In cases where AI use doesn't directly affect clinical practice or other professional settings, faculty members are encouraged to offer alternatives that still meet learning objectives.
The framework has already gained national attention, featured in webinars and discussions that are helping other institutions think about acceptable AI use in healthcare education.
Fowlin said MUSC’s approach reflects what leading institutions are doing: defining explicit learning objectives before deploying AI, piloting tools before scaling and establishing faculty-led governance structures that prioritize learning outcomes over adoption metrics.
Looking ahead: Governance, access and leadership
MUSC is developing a governance structure to ensure that AI adoption continues to align with its mission and values while maintaining the ability to adapt as the field advances.
“We believe in transformation, and we know that education is dynamic,” said Fowlin. “We really want to move the needle. We’re passionate about doing transformation in a compassionate, strategic, systematic, collaborative way.”
That collaboration includes working with the international Digital Education Council and also the American Association of Colleges and Universities, which has an Institute on AI, Pedagogy and the Curriculum. MUSC has also been deliberate about documenting its process as it incorporates AI.
Fowlin said that as MUSC continues to position itself as a national leader in the scholarship of teaching and learning related to AI, the institution remains committed to a vision where technology augments, not replaces, the human expertise, critical reasoning and ethical judgment that define excellence in healthcare education.
