Abstract
Background
The rapid integration of artificial intelligence (AI) into healthcare has transformed how health professionals learn, communicate, and make clinical decisions. However, AI-generated images and digital outputs often reproduce societal stereotypes, particularly regarding gender and race.
Aim
This study examined how nursing students’ perceptions of gender, race, and professional roles are shaped by AI-generated images of healthcare professionals, and how these perceptions influence their communication styles and ethical awareness in interactions with AI.
Design
A multimethod, cross-sectional design integrating quantitative and qualitative approaches was used. Quantitative data assessed gender attitudes in the nursing profession, while qualitative data explored visual interpretations and language patterns in AI interactions.
Participant Population
The sample included 132 second- and fourth-year nursing students from a health sciences faculty in Türkiye.
Ethical Consideration
The study was approved by the institutional ethics committee.
Findings
Results indicated that nursing students largely relied on visual cues particularly clothing and posture when identifying professional roles in AI-generated images. Despite claiming objectivity, students frequently associated doctors with men and nurses with women, reflecting persistent gender schemas. Female participants demonstrated greater sensitivity to both gender and racial imbalances, whereas males perceived AI-generated visuals as more neutral. Language analysis revealed two main communication styles in chatbot interactions: polite and direct. Quantitative findings showed that being female, having lower income, and higher GPA were associated with more egalitarian attitudes.
Conclusion
Nursing students’ perceptions and interactions with AI are influenced by implicit gender and racial stereotypes embedded in visual and linguistic representations. Integrating AI ethics, gender equity, and digital literacy into nursing curricula will foster critical awareness, algorithmic fairness, and equitable professional identity formation among future nurses.
Keywords
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