Conceptualizing AI-resistant assessment strategies in online graduate nursing education through a theoretical and practical framework
TL;DR
This conceptual paper proposes a framework for redesigning asynchronous assessments in online graduate nursing education in response to generative artificial intelligence, and proposes four strategies grounded in the community of inquiry framework, authentic assessment theory, experiential learning theory, and transformative learning theory: experience-anchored analysis, multimodal documentation portfolios, asynchronous verbal exchange, and asynchronous collaborative process.
Abstract
This conceptual paper proposes a framework for redesigning asynchronous assessments in online graduate nursing education in response to generative artificial intelligence (AI). Traditional text-based assignments are vulnerable to AI-generated submissions, and conventional plagiarism detection software performs poorly against such content. We argue for redesigning assessments, rather than increasing surveillance. We propose four strategies grounded in the community of inquiry framework, authentic assessment theory, experiential learning theory, and transformative learning theory: experience-anchored analysis, multimodal documentation portfolios, asynchronous verbal exchange, and asynchronous collaborative process. None of the strategies are new, and each has a long history in nursing and health professions education. The contribution of this work is its integration of the four strategies into a single design rationale that would be responsive to the pressure placed by AI on asynchronous assessments. We use ‘AI-resistant’ in this paper in a bounded sense, that is, for designs that can reduce the feasibility of the substitution of students’ work with AI, rather than designs that can prevent any form of AI involvement.