End-of-life education: An explorative study using artificial intelligence simulations in undergraduate nursing

BACKGROUND: Generative Artificial Intelligence (Gen AI) enables nursing students to practice complex, sensitive end-of-life care discussions in a simulated environment. This approach provides opportunities to develop person-centred communication and feedback with students without involving real patients, supporting preparation for clinical practice. PURPOSE: To explore Australian undergraduate nursing students' satisfaction and competence in conducting end-of-life (EOL) conversations after interacting with AI-generated characters representing diverse backgrounds. METHODS: An exploratory pre- and post-test survey design incorporating both quantitative and qualitative data. Third-year undergraduate nursing students from one Australian university participated. Pre-test surveys assessed self-reported competence in sensitive EOL conversations. Post-test surveys measured self-reports of competence in EOL conversations, confidence, and satisfaction with the learning experience. A total of 109 students completed the pre-test, and 82 completed the post-test. Data were analysed using descriptive statistics and thematic analysis. DISCUSSION: The intervention positively impacted students' confidence and competence in EOL conversations (respondents who stated, 'Strongly Agree' or 'Agree' increased by 15% to 34%). The identified qualitative themes included content-specific learning, as well as the development of knowledge, skills, and attributes. It was perceived as a safe learning environment that offered interactivity and engagement, realism, but also had challenges and emotional limitations. CONCLUSION: Gen AI simulations enhanced students' competence and engagement in EOL discussions. While some participants noted a lack of emotional depth in AI responses, the overall experience was found to be safe, informative, and promising for nursing education.