Functional connectivity theory-based neurophysiological profiles associated with emotional competence and clinical...

Artificial intelligence (AI) is transforming nursing education by supporting information processing and clinical reasoning; however, human-centred competencies such as emotional competence and clinical judgment remain essential for psychiatric nursing practice. Guided by Functional Connectivity Theory, this study investigated whether neurophysiological profiles based on hemispheric balance and interhemispheric connectivity were associated with emotional competence and clinical judgment among nursing students.

To identify neurophysiological profiles using a person-centred cluster analytic approach and to examine differences in emotional competence and clinical judgment across the identified profiles.

A secondary analysis using a cross-sectional, person-centred cluster analytic design.

A total of 212 fourth-year nursing students who completed psychiatric nursing practicum at a university in the Republic of Korea.

Hierarchical cluster analysis (Ward's method) was performed using hemispheric balance and interhemispheric connectivity measured with a portable two-channel EEG device. Independent-samples t-tests compared emotional competence and clinical judgment between the identified neurophysiological profiles.

Two distinct neurophysiological profiles were identified: an integrated profile (n = 124) and a less-integrated profile (n = 88). Students in the integrated profile demonstrated significantly higher emotional utilization (p = .033), emotional self-efficacy (p = .009), and overall clinical judgment (p = .028), including the domains of noticing, interpreting, responding, and reflecting. No significant difference was observed in overall emotional competence.

Guided by Functional Connectivity Theory, this study demonstrates that functional brain organization is associated with higher-order human-centred competencies rather than with emotional competence as a global construct. Neurophysiological profiling may provide a neuroscience-informed framework for understanding individual differences in nursing students' learning and may support the development of personalized educational strategies in the AI era.