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

Area of Science:

• Nursing Education
• Artificial Intelligence in Healthcare
• Simulation-Based Learning

Background:

• Generative AI (Gen AI) offers a simulated environment for nursing students to practice sensitive end-of-life (EOL) care discussions.
• This approach facilitates the development of person-centred communication and feedback without patient involvement, aiding clinical preparation.

Purpose of the Study:

• To evaluate Australian undergraduate nursing students' satisfaction and competence in conducting EOL conversations after interacting with diverse AI-generated characters.
• Assessing the impact of AI simulation on student preparedness for EOL care.

Main Methods:

• Exploratory pre- and post-test survey design utilizing quantitative and qualitative data.
• Third-year nursing students (n=109 pre-test, n=82 post-test) from an Australian university participated.
• Data analysis included descriptive statistics and thematic analysis of self-reported competence, confidence, and satisfaction.

Main Results:

• A significant increase in students reporting 'Strongly Agree' or 'Agree' for enhanced confidence and competence in EOL conversations (15% to 34%).
• Qualitative themes highlighted content-specific learning, skill development, and the creation of a safe, engaging, and realistic learning environment.
• Participants perceived the Gen AI simulation as interactive and informative, despite noting limitations in emotional depth.

Conclusions:

• Gen AI simulations positively impacted nursing students' competence and engagement in EOL discussions.
• The learning experience was perceived as safe and promising for nursing education, enhancing preparedness for clinical practice.
• While acknowledging limitations, the study underscores the value of AI in developing crucial communication skills for EOL care.