Nursing academics report limited, unclear and inconsistently applied institutional guidance on GenAI use in teaching and assessment¶

Claim · All claims
Evidence · 1 study (2 entries) · qualitative r3 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2031: baselines and conditions change

Subclaims¶

q2 i? Participants described a lack of clear, timely and practical institutional guidance on GenAI, leaving them unclear about expectations. → Dabkowski 2026 q2 i? Participants reported inconsistent acknowledgement or permission of GenAI across different units and teaching teams. → Dabkowski 2026 (2)

Evidence¶

Dabkowski 2026¶

Dabkowski, Elissa; Missen, Karen; Allen, Louise; Whitehead, Dean; Worn, Ryan. (2026). 'Co-pilot won't teach you to be a nurse': nursing academics' perspectives on GenAI use in undergraduate education. International Journal for Educational Integrity. https://doi.org/10.1007/s40979-026-00220-2

q2 · i? · qualitative · r3

Qualitative descriptive interview study of 22 nursing academics in Australia and New Zealand; reflexive thematic analysis under Theme One. Participants described "a lack of clear, timely and practical guidance" on GenAI use.

"Participants described a sense of uncertainty surrounding the use of GenAI in their institutions, highlighting a lack of clear, timely and practical guidance."

Dabkowski 2026 (2)¶

Dabkowski, Elissa; Missen, Karen; Allen, Louise; Whitehead, Dean; Worn, Ryan. (2026). 'Co-pilot won't teach you to be a nurse': nursing academics' perspectives on GenAI use in undergraduate education. International Journal for Educational Integrity. https://doi.org/10.1007/s40979-026-00220-2

q2 · i? · qualitative · r3

Same interview study, Theme One minor theme on inconsistent application. Participants reported "significant variation in how the use of GenAI was acknowledged or permitted across different units", which they saw as disadvantaging students.

"Participants reported significant variation in how the use of GenAI was acknowledged or permitted across different units."

Discussion¶

Related Claims¶

• Nursing academics link student GenAI misconduct to future unprofessional and unsafe clinical conduct — related
• AI integration in academic settings has outpaced regulatory frameworks, leaving ethical practices, assessment, and institutional responses inconsistent — a broader claim this one bears on
• Academic integrity, professional development, and equity concerns about AI span all surveyed units, though interpreted differently — related
• Students most often urge institutions to teach responsible AI use (N=41) and provide clear rules and guidelines (N=27) — related
• Associate deans report that neither their institution nor their units have implemented official AI policies, prompting localized unit-level guidelines — a narrower finding that bears on this claim
• Institutions have expanded student access to generative AI tools faster than they have developed policies, guidance, and shared learning goals for their use — a broader claim this one bears on
• AI adoption in higher education appears to progress faster than institutional policies, ethical frameworks, and governance structures, creating a policy–practice gap — a broader claim this one bears on
• Institutional ambiguity varies sharply across universities, and binding silence does not determine ambiguity — related
• Student reflections revealed tensions between efficiency and academic integrity, and uncertainty about institutional expectations of GenAI use — related
• Students' moral rationalizations of AI use are typically post hoc and often internally inconsistent — related
• Awareness of GenAI governance and regulation remains limited, appearing in a small transitional cluster — related
• Most higher education educators report lacking institutional support and feeling unequipped for GenAI integration — a broader claim this one bears on
• Assessment validity is a deeper problem than AI-output detection, and disallowed use clusters with time pressure and unclear expectations — related