Implementing the NURSING-AI Challenge to Transform Real-World Nursing Education, Practice, and Research Through AI Innovation
Background
The NURSING-AI Challenge is an initiative designed to integrate artificial intelligence (AI) into nursing education, practice, and research by directly addressing real-world challenges faced by nursing faculty. This initiative fosters interdisciplinary collaboration between School of Nursing and other units like the McCombs School of Business, Computer Science, Electrical and Computer Engineering, School of Information, and various interdisciplinary research centers on campus. Our objective is to create AI-driven solutions that enhance nursing education, practice, and research.
Process
The challenge is structured in multiple phases to ensure comprehensive problem identification, solution development, and real-world impact:
- Phase 1: Nursing faculty submit detailed descriptions of challenges via a Qualtrics survey.
- Phase 2: A review committee curates the submissions, identifying key issues or grouping similar challenges.
- Phase 3: Graduate students, collaborating with their faculty advisors, review the curated list and submit proposals demonstrating how AI tools can resolve these challenges.
- Phase 4: Faculty review the proposals and select teams for further development.
- Phase 5: Public showcase event and winner announcement.
Download Call for Proposals: AI Solutions for Nursing Challenges.
Progress To Date
- Phase 1: In Phase 1, a Qualtrics survey was disseminated to School of Nursing faculty to collect detailed descriptions of challenges encountered in teaching, clinical practice, and research. To date, we have received 13 distinct submissions, covering a broad range of real-world issues.
- Phase 2: In Phase 2, a review committee curated the challenges, and the curated list will be shared with graduate students (and their faculty advisors) who will submit proposals outlining AI-based solutions by the end of March 2025. Teams will further develop their solutions in April and winners will be selected and announced in May.
Discussion and Implications
This case study highlights the feasibility and early success of the NURSING-AI Challenge as an innovative approach to addressing practical challenges in nursing education through AI. The integration of nursing faculty insights with interdisciplinary technical expertise provides a replicable model for other institutions seeking to leverage AI for health IT innovations. By emphasizing the role of faculty advisors in ensuring accountability and sustained project support, the challenge fosters robust, real-world implementations that can ultimately enhance educational outcomes and improve health care delivery. The lessons learned and data gathered from this initiative will be refined and disseminated in future reports to inform broader adoption and adaptation of similar models in the health IT and analytics community.
NURSING-AI Challenge Showcase:
Date: Thursday, May 8, 2025
Time: 12:00 pm - 2:00 pm | Lunch will be provided
Location: NUR 1.108 | UT Austin School of Nursing | 1710 Red River, Austin, TX
For additional details or questions, please feel free to contact:
Bo Xie, PhD, FGSA | Professor
Fellow, Dolores V. Sands Chair in Nursing Research
Director, Center for Healthcare Innovation and Technology Advancements (CHITA)
The University of Texas at Austin | School of Nursing; School of Information
Email: boxie@utexas.edu
Wonshik Chee, PhD | Research Professor
Co-Director, Center for Healthcare Innovation and Technology Advancements (CHITA)
The University of Texas at Austin | School of Nursing
Email: wonshik.chee@austin.utexas.edu
Nicole S. Murry, PhD, RN | Clinical Associate Professor
Director, Center for Professional Development & Scholarship (CPDS)
The University of Texas at Austin | School of Nursing
Email: nicole.murry@austin.utexas.edu