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  1. Home
  3. Use Of Artificial Intelligence Image Generation To Promote Self-reflection And Recognition Of Unconscious Bias: A Cross-sectional Study Of Nursing Students.
  1. Home
  3. Use Of Artificial Intelligence Image Generation To Promote Self-reflection And Recognition Of Unconscious Bias: A Cross-sectional Study Of Nursing Students.

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Use of artificial intelligence image generation to promote self-reflection and recognition of unconscious bias: A

Leanne Mullan1, Bethany Arbuckle1, Wendy Luck1

  • 1Faculty of Health Sciences, School of Nursing, Midwifery and Paramedicine, Australian Catholic University, 1100 Nudgee Road, Banyo, Queensland 4014, Australia..

Nurse Education in Practice
|October 10, 2025

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
AIArtificial IntelligenceHigher educationNursingSelf-reflectionTechnology-enabled learningUniversity

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Artificial intelligence (AI)-image generation effectively enhanced nursing students' self-reflection and bias recognition regarding disability. This innovative tool helps students explore personal views and their impact on clinical practice.

Area of Science:

  • Nursing Education
  • Artificial Intelligence in Healthcare
  • Bias Recognition

Background:

  • Self-reflection and bias recognition are crucial for nursing students' clinical reasoning and self-awareness.
  • Teaching these skills to diverse student populations presents challenges, necessitating innovative technological solutions.
  • Developing and evaluating novel tools to support reflective practice is essential.

Purpose of the Study:

  • To evaluate the effectiveness of an artificial intelligence (AI)-image generation learning sequence.
  • To assess its impact on higher-education nursing students' self-reflection capabilities.
  • To determine its value in recognizing unconscious bias related to disability.

Main Methods:

  • A multi-methods approach was employed, collecting both quantitative and qualitative data via surveys.
  • Twenty-nine nursing students participated in the study.
  • Data analysis included content and inductive thematic analysis for qualitative data and descriptive statistics for quantitative data, adhering to STROBE guidelines.

    • Main Results:

    • 90% of participants reported AI-image generation aided self-reflection on personal views about disability.
    • 70% recognized potential personal and societal biases towards disability.
    • 80% felt AI-image generation prompted consideration of how biases influence nursing practice.

    Conclusions:

    • AI-image generation shows promise as a valuable tool for fostering student self-reflection.
    • It can effectively support the identification of unconscious biases related to disability.
    • The technology may help students understand how personal perspectives impact clinical nursing practice.