A UMB faculty task force representing all seven schools turned 24 artificial intelligence ideas into three institutional priorities and a peer-reviewed case study.
What began as a three-month University service initiative has resulted in three institutionwide artificial intelligence (AI) recommendations, a new approach for managing innovation overload, a peer-reviewed case study, and now the selection of the first Teaching and Learning AI initiative for implementation at the University of Maryland, Baltimore (UMB).
From January through March 2026, the UMB AI Teaching and Learning Task Force brought together faculty and academic leaders representing all seven UMB schools, along with partners in teaching and learning, to identify high-value opportunities for responsible use of AI across the University. The interdisciplinary group represented the Schools of Dentistry, Graduate Studies, Law, Nursing, Medicine, Pharmacy, and Social Work, ensuring that recommendations reflected the needs and perspectives of UMB's diverse professional education programs.
The challenge was significant. The task force began with 24 proposed AI use cases, far more ideas than could reasonably be advanced simultaneously. Rather than simply selecting the most popular proposals, the group developed and tested a systematic five-stage faculty-led pipeline incorporating structured ideation, transparent scoring and prioritization, faculty deliberation and consensus-building, collaborative refinement, and leadership-facing recommendations. The process generated 234 scored entries and allowed the group to identify overlap among promising ideas, refine them, and ultimately synthesize them into three coherent institutional priorities.
The resulting recommendations were:
- Enterprise Virtual Teaching Assistant (VTA): A course-specific, LMS-integrated AI assistant providing students and faculty with 24/7 academic and logistical support.
- AI-Enabled Interprofessional Virtual Practice Lab: A scalable environment for formative patient and client encounters, communication practice, rehearsal, and reflection across UMB disciplines.
- The Educationalist: A faculty-facing AI resource providing source-grounded pedagogical guidance, faculty development support, and easier access to institutional teaching resources.
The Enterprise VTA recommendation has especially strong roots at the University of Maryland School of Nursing (UMSON). Its design is closely modeled on JAIMIE the VTA, which UMSON has been developing and evaluating for approximately two and a half years. JAIMIE demonstrates how a course-grounded virtual teaching assistant can support both sides of the learning environment by giving students on-demand access to course-specific guidance while helping faculty manage repetitive questions, clarify course expectations, and focus their time on higher-value student interactions. The task force identified JAIMIE's experience as foundational evidence for the feasibility of expanding the VTA concept into a sustainable enterprise capability.
The task force also looked beyond individual technologies. Additional recommendations addressed responsible and equitable AI integration across UMB, including continued academic oversight of AI's implications for curriculum, assessment, professional preparation, faculty development, and institutional accountability.
In August, the work reached an important milestone. After reviewing proposals from all six UMB AI task forces, the AI Ideation Steering Committee selected The Educationalist as the first implementation priority for the Teaching and Learning mission area. The remaining Teaching and Learning recommendations remain part of the University's ongoing AI roadmap, with task force members expected to continue contributing subject matter expertise as implementation proceeds.
The significance of the work extends beyond the three use cases themselves. The task force recognized that universities everywhere are confronting a similar challenge: innovation overload, where the pace and volume of new AI opportunities exceed an institution's ability to evaluate, prioritize, and responsibly implement them. The process developed at UMB provides one potential solution.
Rather than allowing that process to remain an internal committee exercise, the task force transformed its service into scholarship. The group published the work as the peer-reviewed case study, “From AI Ideas to Institutional Priorities: A Case Study of a Faculty-Led Pipeline for AI-Enhanced Learning,” in AI-Enhanced Learning. Authors include all task force members, with representation spanning every UMB school and university teaching and learning partners.
The case study makes the five-stage pipeline available as a transferable model that other task forces, universities, and academic systems can adapt when they face more promising innovations than they can reasonably pursue at once. In that sense, the project produced more than three AI recommendations. It created and tested a structured way for faculty to help institutions move from ideas to priorities, from priorities to action, and from university service to scholarship.
As Interim Provost and Executive Vice President Mark A. Reynolds, DDS, PhD, MA, noted in his letter of appreciation to the task force, the work provided UMB with a practical roadmap for AI and demonstrated the value of bringing a broad range of perspectives together to determine where AI can make a meaningful difference and how it can be implemented responsibly.
Task Force Members
The UMB AI Teaching and Learning Task Force reflected broad interdisciplinary participation across the University and its teaching and learning partners:
- University of Maryland School of Nursing: Cory Stephens, Susan Bindon, Cheryl A. Fisher
- University of Maryland School of Medicine: Philip Dittmar, Sarah B. Murthi
- University of Maryland School of Pharmacy: Shannon Tucker
- University of Maryland School of Dentistry: Patricia A. Tordik
- University of Maryland School of Social Work: Paul Sacco
- University of Maryland Francis King Carey School of Law: Benjamin Yelin
- University of Maryland School of Graduate Studies: Mary Jo Bondy
- Faculty Center for Teaching and Learning: Eric S. Belt
- University System of Maryland: Jenny Owens, Roger J. Ward
Together, these members brought perspectives spanning every UMB school as well as university-level teaching, learning, and academic leadership, strengthening both the task force recommendations and the resulting scholarship.
References
Stephens, C., Tucker, S., Belt, E., Bindon, S., Bondy, M.J., Dittmar, P., Fisher, C., Murthi, S., Owens, J., Sacco, P., Tordik, P., Yelin, B., and Ward, R. (2026). From AI Ideas to Institutional Priorities: A Case Study of a Faculty-Led Pipeline for AI-Enhanced Learning. AI Enhanced Learning, 2(2), 307-333. Association for the Advancement of Computing in Education (AACE). Retrieved Aug. 25, 2026, from https://www.learntechlib.org/primary/p/2129921/