The emergence of generative artificial intelligence has sparked ongoing conversations and debates across higher education. While AI offers significant opportunities to enhance learning, creativity, and productivity, educators and institutions are also grappling with important questions about academic integrity, critical thinking, and student preparedness for an AI-enabled future. At the Medical University of South Carolina (MUSC), we strongly believe that AI tools should not replace the human connections, expertise, and critical reasoning that are foundational to the educational experience.
To help educators and students navigate this evolving landscape together, MUSC developed the AI Acceptable Use Framework for Academic Tasks which is included in the Plagiarism and Artificial Intelligence Guideline. The framework was intentionally designed as a shared language for both educators and students to support transparent conversations about AI use while keeping learning, critical thinking, and professional preparation at the center.
This is NOT a policy statement. It is a guiding framework that reflects our pedagogical stance and our commitment to developing competent, ethical, and reflective healthcare professionals.
In an academic health sciences institution, the stakes of learning are uniquely high. A clinician who cannot think critically under pressure, a researcher who cannot evaluate evidence independently, or a student who has outsourced their reasoning to a machine represents more than an academic integrity concern. These are matters of patient safety, professional judgment, and professional formation.
That is why MUSC deliberately uses the term academic tasks rather than simply assignments or assessments. This inclusive language reflects the full range of learning activities where AI may play a role, from clinical simulations and case analyses to laboratory reports, research projects, presentations, and reflective writing. It signals that AI use guidance at MUSC is not a narrow compliance exercise; it is a university-wide conversation about what it means to learn, think, and grow as a healthcare professional in an AI-enabled world.
The video below introduces students to the framework and the shared principles that guide AI Acceptable Use in Academic Tasks at MUSC.
How Was the Framework Developed?
The MUSC AI Acceptable Use Framework was developed through a collaborative effort led by the Center for the Advancement of Teaching and Learning (CATL) in partnership with academic leadership, central administrative offices, faculty, instructional designers, and student support professionals across the institution. This collaborative approach helped ensure the framework reflected diverse perspectives and aligned with the realities of teaching, learning, and professional preparation at MUSC.
The framework was initially informed by The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment (2024), published in the Journal of University Teaching and Learning Practice, and was further adapted using the updated AI Assessment Scale from Perkins, Furze, Roe, and MacVaugh. Rather than adopting these frameworks directly, MUSC contextualized them for health sciences education and expanded them into a shared language for educators and students to guide conversations about AI use, learning, and academic integrity.
The framework was reviewed and approved by MUSC's Education Advisory Council (EAC), Student Affairs Advisory Council (SAAC), and Provost Council, reflecting broad institutional support for implementation.
Related sources that informed the framework: AIAS article DOI; Updated AI Assessment Scale
Contributors
- Ashley Bondurant, Assistant Professor and Instructional Designer, College of Health Professions
- Ragan DuBose-Morris, Professor and Director of Education and Information Technology, College of Health Professions
- Chelsea Dyer, Former Instructor and Instructional Designer, College of Health Professions
- Julaine Fowlin, Assistant Professor and Executive Director, Center for the Advancement of Teaching and Learning (CATL)
- April Heyward, Assistant Professor, Director of Planning and Research, Department of Public Health Sciences
- Lisa Langdale, Assistant Professor, Office of Interprofessional Initiatives (OII)
- Kasey Larson, Instructor and Director for Supplemental Instruction, Center for Academic Excellence/Writing Cente
- Julia Liebenrood, Education Development Lead, Center for the Advancement of Teaching and Learning (CATL)
- E'lise Nissen, Instructor and Director of Artificial Intelligence Education and Scholarship, CATL
- Mary Smith, Assistant Professor, Director of Instructional Design and Technology, CATL
- Tom Smith, Professor and Executive Director, Center for Academic Excellence and Writing Center
Additional feedback and guidance were provided by Gigi Smith, former Associate Provost for Education Innovation and Student Life and Professor in the College of Nursing, as well as colleagues from the Office of Interprofessional Initiatives.
We are pleased to share the MUSC AI Acceptable Use Framework with colleagues.
To request access to the detailed framework, including key considerations and Creative Commons licensing information, please complete the brief form below. Upon submission, you will receive access to a resource folder containing the framework and supporting materials for download.
Support for MUSC Educators
Turning AI Guidance into Teaching Practice
Guidance documents, however well-crafted, only go so far. The real test of any framework is how it is used and operationalized in the learning environment. At MUSC, our approach extends beyond guidance documents and AI use designations. Through the leadership of CATL, we have developed practical tools, resources, and consultation services to help educators make learning outcome-based decisions about AI integration and communicate clear expectations.
Whether you are redesigning an assignment, updating your syllabus, developing AI literacy expectations, or exploring new forms of assessment, support is available.
Syllabus Language, Communication Resources, and Brightspace Icons
To promote consistency and transparency, CATL has developed a resource collection that includes:
- Ready-to-use syllabus language
- Assignment and academic task language aligned with the framework
- Student communication resources
- Brightspace icons for AI use designations
- Implementation guidance and supporting materials
These resources are designed to help faculty clearly communicate expectations and create a shared understanding of AI use across courses and programs.
(NetID & password required to view MUSC educator resources below.)
MUSC 6-Step Assessment Reimagining Process and Bot
The MUSC Assessment Reimaginer Bot was developed using BoodleBox AI to help educators apply the 6-Step MUSC Assessment Reimagining Process when designing or revising academic tasks in an AI-enabled learning environment.
Developed under the leadership of the Director of AI in Education and Scholarship, the bot assists faculty in exploring assessment strategies that promote critical thinking, authentic learning, and alignment with course outcomes while supporting implementation of the MUSC AI Acceptable Use Framework. Rather than designing assignments around AI avoidance, faculty are encouraged to design assignments around learning outcomes, authentic demonstration of learning, and the professional competencies students must ultimately develop. By guiding educators through each of the six steps, the bot helps transform framework principles into practical, classroom-ready teaching and assessment strategies and ready to implement resources such rubrics and instructions.
The 6-step process encourages educators to ask:
- What am I really trying to assess? Clarify the core learning objective beneath the assignment.
- Would I trust a professional who could only do this using AI? Identify which elements require genuine human expertise.
- What can students now accomplish with AI that they previously had to do on their own? Consider how AI changes the validity of the assignment.
- How could AI help me assess this learning in richer or more meaningful ways? Reimagine the assignment to leverage AI appropriately.
- What thinking or judgment must I now make visible to grade? Define observable evidence of authentic student reasoning.
- Which MUSC AI Acceptable Use category fits, and how will I explain my decision to students? Select the appropriate designation and communicate expectations clearly: No AI, AI Planning, AI Limited, AI Extensive, or AI Exploration.
To access the bot, click below. Though specifically designed for the MUSC context, this bot is publicly available and is best used with a BoodleBox subscription to avoid chat limitations.
Academic Integrity and Course Design Go Hand in Hand
At MUSC, students are responsible for understanding and complying with the MUSC Plagiarism and Artificial Intelligence Guidelines, which include the AI Acceptable Use Framework for Academic Tasks. That responsibility is supported by clear expectations, transparent communication, and thoughtfully designed learning experiences. The MUSC AI Acceptable Use Framework was intentionally developed not only as a guide for AI use, but also as a course design tool that helps faculty align academic tasks with learning outcomes while clearly communicating expectations to students.
By bridging course design and academic integrity, the framework encourages educators to move beyond simply identifying inappropriate AI use and instead create learning environments that promote authentic learning, critical thinking, and responsible AI use. Documentation practices such as AI interaction logs, reflections, and explanations of how AI output was evaluated are not intended as surveillance mechanisms. Rather, they serve as learning artifacts that make student thinking visible, encourage reflection, and reinforce the transparency and professional judgment expected in research and clinical practice.
In this way, academic integrity becomes more than a response to misconduct. It becomes an integral part of the learning process and the design of the educational experience itself.
Educators and students are encouraged to review MUSC's Academic Integrity resources available through the MUSC Honor Code website. The Honor Code orientation video below, developed by Honor Council University Advisors Erica Bligen and Dante Pelzer, provides an overview of MUSC's centralized academic integrity process and the role of the University Honor Council in supporting a consistent and equitable approach across the institution. While originally created for student orientation, the video can also help educators better understand the processes, resources, and support structures available to students. Together, the University Honor Council's centralized infrastructure and the AI Acceptable Use Framework support a comprehensive approach that combines clear expectations, thoughtful course design, and a commitment to academic integrity.
Honor Code Orientation Video
What This Looks Like in Practice
For educators, the framework provides clarity without rigidity. The AI use categories, implementation resources, and course design tools help educators make intentional decisions about learning outcomes, assessment design, and AI integration.
For students, the framework provides transparency. Rather than navigating a patchwork of inconsistent instructor policies, students encounter a coherent institutional stance that acknowledges AI's presence in their professional futures while being honest about where human expertise remains non-negotiable. The expectation to acknowledge AI use according to instructor expectations and citation style guides mirrors professional norms students will encounter throughout their careers.
For institutions, the framework offers a practical model for integrating AI governance, course design, and academic integrity. Rather than treating AI as solely a compliance issue, the framework encourages educators to consider how learning outcomes, assessment design, student expectations, and responsible AI use work together to shape the learning experience. By openly sharing its approach, MUSC contributes to the growing conversation about how higher education can support both innovation and authentic learning in an AI-enabled world.
In the News
Since its launch, the MUSC AI Acceptable Use Framework has been featured in interviews, webinars, presentations, and professional conversations focused on artificial intelligence in higher education.
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