Content area
Aim
To determine the value of an artificial intelligence (AI)-image generation learning sequence on higher-education nursing student self-reflection and recognition of unconscious bias in the context of disability.
Background
Self-reflection and recognition of bias amongst undergraduate nursing students enhances reasoning skills and self-awareness in clinical situations. Teaching self-reflection to a diverse cohort can be challenging, making it essential to develop and assess innovative technological tools that support engagement in reflective practice.
Design
A multi-methods approach was adopted, obtaining both quantitative and qualitative data for analysis through a survey.
Methods
Twenty-nine nursing students from the Australian Catholic University were surveyed. Qualitative data underwent both content and inductive thematic analysis. Quantitative data were summarised using descriptive statistics. The study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) cross-sectional study guideline.
Results
AI-image generation aided self-reflection on personal views about disability and recognition of potential personal and society biases towards disability amongst 90 % (n = 26) and 70 % of participants respectively. Visualisation of thoughts supported self-reflection and identification of generalisations held about disability. Eighty percent of respondents felt AI-image generation prompted them to consider how views and biases about disability may influence nursing practice. AI-image generation was identified to be an interesting and novel tool for self-reflection.
Conclusion
Findings suggest AI-image generation may be a useful tool in supporting students to practice self-reflection and identify unconscious biases. AI-image generation may assist students to consider how personal views can impact on clinical practice.
Details
Data Collection;
Nursing Education;
Confidentiality;
Chronic Illness;
Artificial Intelligence;
Instructional Effectiveness;
Learner Engagement;
Learning Modules;
Algorithms;
Focus Groups;
Units of Study;
Nurses;
Influence of Technology;
Learning Strategies;
Learning Theories;
Learning Experience;
Instructional Materials;
Ethics;
Feedback (Response);
Beliefs;
Electronic Learning;
Data Analysis;
Grounded Theory;
Higher Education
Students;
Clinical training;
Curricula;
College students;
Clinical skills;
Professional identity;
Image generation;
Distance learning;
Unconsciousness;
Cross-sectional studies;
Medical education;
Artificial intelligence;
Professional practice;
Clinical medicine;
Bias;
Data;
Teaching;
Nurses;
Learning;
Qualitative research;
Higher education;
Nursing education;
Catholics;
Observational studies;
Disability;
Epidemiology;
Selfreflection;
Recognition;
Chronic illnesses;
Reflective practice;
Acknowledgment;
Generative artificial intelligence;
Nursing;
Polls & surveys;
Selfawareness;
Respondents;
People with disabilities;
Data collection;
Clinical nursing;
Statistics;
Reflection