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Nursing students’ perceptions of gender and race in AI healthcare imagery

Nursing Ethics · Mayıs 2026

Özet
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.
0 atıf Mayıs 2026 DOI
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YÖKSİS Kayıtları
Nursing students’ perceptions of gender and race in AI healthcare imagery
Nursing Ethics · 2026 SCI-Expanded
Dr. Öğr. Üyesi BİRSEL MOLU →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Bu dergide (ISSN eşleşmesi) kurumun 2 kaydı bulundu.
Nursing students’ perceptions of gender and race in AI healthcare imagery
2026 ISSN: 0969-7330 SCI-Expanded Q1
Dr. Öğr. Üyesi BİRSEL MOLU →
Effect of digital storytelling-case studies patient privacy: A randomized controlled study
2025 ISSN: 0969-7330 SCI-Expanded Q1
Dr. Öğr. Üyesi GÜLCAN EYÜBOĞLU →

Makale Bilgileri

Toplam Atıf 0 atıf · Scopus
ISSN09697330
Yayın TarihiMayıs 2026
Cilt / Sayfa33 · 813-830

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey

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Scimago Dergi (ISSN Eşleşmesi)
Nursing Ethics
Q1
SJR Skoru1,270
H-Index89
YayıncıSAGE Publications Ltd
ÜlkeUnited Kingdom
Issues, Ethics and Legal Aspects (Q1)
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