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

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.

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.

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.

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.

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.