Other Titles
The SAfER-AI Use™ Heuristic: A Systems Approach for Ethical and Responsible AI in Nursing Education [Title Slide]
Abstract
Purpose - To introduce the SAfER-AI Use™ Framework, a systems-based conceptual model to guide educators in the ethical and responsible use of generative AI.
Background - Generative AI is entering nursing education faster than leaders can evaluate its risks. Educators increasingly use large language models (LLMs) for assessments, scenarios, rubrics, and accreditation tasks, yet LLMs generate fluent but non–human-derived output that may contain inaccuracies or bias. Recent scholarship describes AI exemplars and risks (1), faculty needs and readiness (2), and calls for policy engagement (3). Yet discipline-specific conceptual models are lacking, especially those addressing the human–AI interface and safeguards at system and individual levels (4). The SAfER-AI Use™ Framework addresses this gap by identifying essential domains for ethical AI integration.
Methods - Researchers developed the framework through Design-Based and Practice-Inquiry methods during iterative creation of a custom GPT simulation design tool. Data sources included AI output, user feedback, and research team reflections. Human Factors Engineering and Sociotechnical Systems Theory guided analysis across conceptual domains of human cognition, AI behavior, and organizational structures.
Results - Seven interrelated constructs emerged: System Pressures, AI Behavior, Faculty and Staff Responsibilities, Ethical Integrity, Human–AI Relationships, Appropriate Use, and Implementation Safeguards. Together, they define overarching domains for evaluating generative AI use and support a systematic approach to implementation.
Discussion - These constructs provide a more holistic perspective than currently represented in the literature. The framework clarifies the human–AI relationship within system pressures, AI tendencies, and ethical boundaries, strengthening conceptual understanding of responsible AI use. Although theoretically grounded, the constructs reflect real educational contexts, offering guidance that is rigorous yet practical.
Implications - The framework benefits mid-level leaders who translate institutional expectations into practice, guides professional development teams in designing training that reflects human, technological, and system demands, and supports ethical AI adoption across diverse academic, clinical, and global educational environments.
Notes
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References:
1. Ostick, M., Mariani, B., Lovecchio, C., & Moriarty, H. (2025). Nursing student and faculty attitudes, perceptions, and behavioral intentions of artificial intelligence use in nursing education: An integrative review. Nursing Education Perspectives, 46(2), E7–E11. https://doi.org/10.1097/01.NEP.0000000000001372
2. Ehmke, S. D., Bridges, J., & Patel, S. E. (2025). Nursing educators’ perspectives on the integration of artificial intelligence into academic settings. SAGE Open Nursing, 11, 1–17. https://doi.org/10.1177/23779608251342931
3. Dornan, M. (2025). Every nurse an AI nurse: A framework for integrating artificial intelligence across nursing practice, education, research, and policy. Digital Health, 11, 1–6. https://doi.org/10.1177/20552076251377939
4. Nair, M., Nygren, J., Nilsen, P., Gama, F., Neher, M., Larsson, I., & Svedberg, P. (2025). Critical activities for successful implementation and adoption of AI in healthcare: Towards a process framework for healthcare organizations. Frontiers in Digital Health, 7, 1550459. https://doi.org/10.3389/fdgth.2025.1550459
Sigma Membership
Eta Gamma
Type
Presentation
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Other
Keywords:
Theory, Implementation Science, Faculty Development, Artificial Intelligence, Nursing Ethics, Nursing Education
Recommended Citation
Lindley, Marie Kelly; Cole, Heather S.; and Senetza, Amber, "The SAfER-AI Use™ Framework: A Systems Approach for Ethical and Responsible AI in Nursing Education" (2026). International Nursing Research Congress (INRC). 278.
https://www.sigmarepository.org/inrc/2026/presentations_2026/278
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
2026-09-16
The SAfER-AI Use™ Framework: A Systems Approach for Ethical and Responsible AI in Nursing Education
Toronto, Ontario, Canada
Purpose - To introduce the SAfER-AI Use™ Framework, a systems-based conceptual model to guide educators in the ethical and responsible use of generative AI.
Background - Generative AI is entering nursing education faster than leaders can evaluate its risks. Educators increasingly use large language models (LLMs) for assessments, scenarios, rubrics, and accreditation tasks, yet LLMs generate fluent but non–human-derived output that may contain inaccuracies or bias. Recent scholarship describes AI exemplars and risks (1), faculty needs and readiness (2), and calls for policy engagement (3). Yet discipline-specific conceptual models are lacking, especially those addressing the human–AI interface and safeguards at system and individual levels (4). The SAfER-AI Use™ Framework addresses this gap by identifying essential domains for ethical AI integration.
Methods - Researchers developed the framework through Design-Based and Practice-Inquiry methods during iterative creation of a custom GPT simulation design tool. Data sources included AI output, user feedback, and research team reflections. Human Factors Engineering and Sociotechnical Systems Theory guided analysis across conceptual domains of human cognition, AI behavior, and organizational structures.
Results - Seven interrelated constructs emerged: System Pressures, AI Behavior, Faculty and Staff Responsibilities, Ethical Integrity, Human–AI Relationships, Appropriate Use, and Implementation Safeguards. Together, they define overarching domains for evaluating generative AI use and support a systematic approach to implementation.
Discussion - These constructs provide a more holistic perspective than currently represented in the literature. The framework clarifies the human–AI relationship within system pressures, AI tendencies, and ethical boundaries, strengthening conceptual understanding of responsible AI use. Although theoretically grounded, the constructs reflect real educational contexts, offering guidance that is rigorous yet practical.
Implications - The framework benefits mid-level leaders who translate institutional expectations into practice, guides professional development teams in designing training that reflects human, technological, and system demands, and supports ethical AI adoption across diverse academic, clinical, and global educational environments.
Description
The SAfER-AI Use™ Framework guides educators and leaders through ethical and responsible integration of generative AI in nursing education. Developed through design-based and practice-inquiry research methods and grounded in Human Factors Engineering and Sociotechnical Systems Theory, resulted in seven constructs related to system pressures, necessary oversight, Human-AI relationship, and safeguards to reduce risk and support globally relevant, safe AI use while preserving educational integrity.