Role reconstruction among double-qualified nursing educators in the generative AI era: a qualitative study

Area of Science:

• Nursing Education
• Artificial Intelligence in Healthcare
• Pedagogy

Background:

• Generative AI (GenAI) is increasingly used in nursing education.
• Its integration poses risks to social interactions and clinical contextualization.
• Double-qualified nursing educators (DQNEs) are key to navigating this shift.

Purpose of the Study:

• Examine GenAI's pedagogical boundaries in nursing education using a social constructivist framework.
• Analyze how DQNEs adapt their roles for knowledge co-construction and clinical meaning-making.

Main Methods:

• Qualitative study employing semi-structured focus group interviews.
• Conducted with 18 DQNEs at a medical college in eastern China.
• Thematic analysis of focus group data.

Main Results:

• GenAI acts as a scaffolding tool, aiding differentiated instruction and bridging theory with practice.
• Challenges include over-reliance on AI, diminished teacher authority, and institutional ambiguity.
• DQNEs use deconstruction/reconstruction strategies and social rules to anchor AI-generated content in clinical practice.

Conclusions:

• GenAI is a dual-use tool, expanding learning potential while threatening social learning.
• DQNEs must transition from information transmitters to contextual anchors.
• Validating AI outputs against clinical reality is crucial for mitigating epistemic risks.