Other Titles
Developing AI Competencies in Nursing [Poster Title]
Abstract
Artificial intelligence (AI) is rapidly reshaping healthcare through its influence on documentation, decision support, communication, and workflow efficiency. As generative AI (GenAI) tools become increasingly embedded in clinical practice, graduate nursing students must be prepared to use them safely, ethically, and effectively. There are significant gaps in AI literacy among both faculty and students, including limited understanding of AI capabilities, risks, and appropriate applications in practice (Simms, 2024, 2025; Phillips, 2025). Nursing programs similarly lack structured learning outcomes and competency-mapped activities to support responsible AI use. National guidance reinforces the urgency of this work: the AACN Essentials (2021) highlight the importance of across Domains 1, 2, 4, 5, 7, 8, 9, and 10, while the ANA (2022) underscores the ethical obligations of nurses using AI-enhanced technologies.
This study aims to design, implement, and evaluate a structured AI literacy intervention for 57 (n = 57) graduate nursing students enrolled in asynchronous online courses. The intervention includes targeted instructional modules and competency-based learning activities mapped to the AACN Essentials. A mixed-methods evaluation will assess changes in students’ foundational AI knowledge, applied AI skills, and ethical readiness. The pre/post questionnaire is designed using two measurement approaches. General AI literacy will be assessed with the validated Critical AI Literacy Scale (CAILS) (Ranieri et al., 2025), while domain-specific clinical reasoning and AI-supported judgment will be measured using items adapted from the NCSBN Clinical Judgment Measurement Model (CJMM) (Simms, 2024) and the Human-in-the-Loop (HITL) Framework (Kobeissi et al., 2025).
Expected findings include increased understanding of AI concepts and limitations, strengthened ability to critically evaluate AI-generated content, and improved alignment with the AACN’s advanced-level nursing competencies. Integrating AI literacy within graduate nursing education has the potential to support ethical practice, enhance clinical judgment, and prepare students for an AI-enabled healthcare workforce. The results will guide the development of scalable, competency-based AI learning activities across the graduate nursing curriculum and may offer a transferable framework for other programs seeking to build AI literacy among future nurse clinicians and leaders.
Notes
References:
American Association of Colleges of Nursing. (2021). The Essentials: Core competencies for professional nursing education. https://www.aacnnursing.org/Essentials
American Nurses Association (ANA). (2022). The ethical use of artificial intelligence in nursing practice [Position statement]. American Nurses Association. https://www.nursingworld.org
Kobeissi, M. M., Santa Maria, D. M., & Park, J. I. (2025). Artificial intelligence 101: Building literacy with the AI-ABCs framework. Nursing Outlook, 73(4), Article 102445.
Phillips, B. (2025). Embracing a thoughtful integration of artificial intelligence into nursing education. Computers, Informatics, Nursing. https://doi.org/10.1097/CIN.0000000000001315
Ranieri, M., Biagini, G., & Cuomo, S. (2025). AI literacy in higher education: A systematic approach to questionnaire development and validation. International Journal of Digital Literacy and Digital Competence (IJDLDC), 16(1), 1-25. https://doi.org/10.4018/IJDLDC.388469
Simms, R. C. (2024). Work with ChatGPT, not against: 3 teaching strategies that harness the power of artificial intelligence. Nurse Educator, 49(3), 158–161. https://doi.org/10.1097/NNE.0000000000001634
Sigma Membership
Gamma Psi at-Large
Type
Poster
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Other
Keywords:
Competence, Ethics, Virtual Learning, Graduate Nursing Education, Nursing Education, Artificial Intelligence
Recommended Citation
Pair, Vincent and Katz, Shayna, "Developing AI Competencies in Nursing: An AACN-Aligned Approach" (2026). International Nursing Research Congress (INRC). 112.
https://www.sigmarepository.org/inrc/2026/posters_2026/112
Conference Name
37th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Toronto, Ontario, Canada
Conference Year
2026
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-09-23
Developing AI Competencies in Nursing: An AACN-Aligned Approach
Toronto, Ontario, Canada
Artificial intelligence (AI) is rapidly reshaping healthcare through its influence on documentation, decision support, communication, and workflow efficiency. As generative AI (GenAI) tools become increasingly embedded in clinical practice, graduate nursing students must be prepared to use them safely, ethically, and effectively. There are significant gaps in AI literacy among both faculty and students, including limited understanding of AI capabilities, risks, and appropriate applications in practice (Simms, 2024, 2025; Phillips, 2025). Nursing programs similarly lack structured learning outcomes and competency-mapped activities to support responsible AI use. National guidance reinforces the urgency of this work: the AACN Essentials (2021) highlight the importance of across Domains 1, 2, 4, 5, 7, 8, 9, and 10, while the ANA (2022) underscores the ethical obligations of nurses using AI-enhanced technologies.
This study aims to design, implement, and evaluate a structured AI literacy intervention for 57 (n = 57) graduate nursing students enrolled in asynchronous online courses. The intervention includes targeted instructional modules and competency-based learning activities mapped to the AACN Essentials. A mixed-methods evaluation will assess changes in students’ foundational AI knowledge, applied AI skills, and ethical readiness. The pre/post questionnaire is designed using two measurement approaches. General AI literacy will be assessed with the validated Critical AI Literacy Scale (CAILS) (Ranieri et al., 2025), while domain-specific clinical reasoning and AI-supported judgment will be measured using items adapted from the NCSBN Clinical Judgment Measurement Model (CJMM) (Simms, 2024) and the Human-in-the-Loop (HITL) Framework (Kobeissi et al., 2025).
Expected findings include increased understanding of AI concepts and limitations, strengthened ability to critically evaluate AI-generated content, and improved alignment with the AACN’s advanced-level nursing competencies. Integrating AI literacy within graduate nursing education has the potential to support ethical practice, enhance clinical judgment, and prepare students for an AI-enabled healthcare workforce. The results will guide the development of scalable, competency-based AI learning activities across the graduate nursing curriculum and may offer a transferable framework for other programs seeking to build AI literacy among future nurse clinicians and leaders.
Description
This study develops and evaluates an AI literacy intervention for graduate nursing students, addressing gaps in knowledge, ethics, and clinical application. Using competency-based modules and validated assessments, the project aims to improve students’ understanding of AI, enhance clinical judgment, and support safe, ethical use of AI in practice.