Network structure of artificial intelligence anxiety among nursing students and educational implications: A...

Area of Science:

• Nursing Education
• Artificial Intelligence in Healthcare
• Mental Health in Academia

Background:

• Artificial intelligence (AI) is transforming nursing practice, yet nursing students report significant profession-specific AI anxiety.
• Understanding the structure of this anxiety and its relationship with learning needs is crucial for effective educational strategies.

Purpose of the Study:

• To map the network structure of AI anxiety among nursing students.
• To compare AI anxiety network structures between students with associate degrees or lower and those with bachelor's degrees or higher.
• To investigate the association between AI anxiety and students' learning needs.

Main Methods:

• A multi-center, cross-sectional survey involving 1113 nursing students from 93 universities across China.
• Utilized the Artificial Intelligence Anxiety Scale (AIAS) and a learning needs assessment.
• Employed Gaussian graphical network and bridge centrality analysis to identify core anxiety symptoms and pathways.

Main Results:

• AI anxiety among nursing students presents a complex, stable network structure.
• Key anxiety drivers include learning interaction difficulties and concerns over AI misuse.
• Students with associate degrees or lower exhibited more pronounced cross-dimensional anxiety, linking fears like job displacement to learning motivation.

Conclusions:

• AI anxiety in nursing students is a cohesive network requiring targeted interventions focused on learning processes and ethical AI use.
• Educational curricula should be reformed to address the heightened anxiety susceptibility in students pursuing associate degrees or lower.
• Leveraging concerns about occupational threats can potentially catalyze learning motivation.