Professor of Nursing Kennesaw State University Kennesaw, GA, United States
Disclosure(s):
Mary Beth R. Maguire, DNS, RN, CNE, CHSE: No financial relationships to disclose
Anne White, PhD, RN, CNE: No financial relationships to disclose
Abstract : Competency-based education will require nursing programs to provide evidence learners have the knowledge, skills, and attitudes expected of them to enter the profession (AACN, 2023). Knowledge and skills are easily quantified in the simulated environment; however, evaluating learners’ attitudes can be elusive (Bejarano et al., 2022). Quantified measures to analyze attitudes may force responses that do not capture the essence of the learner’s viewpoint. Conversely, qualitative measures allow learners to share deep insight into their reactions to a simulated event (Johnsen, 2021). Deep insight creates big data and is challenging to process efficiently using traditional methods.
This study aimed to evaluate the effectiveness of artificial intelligence (AI)-driven technology to explore undergraduate nursing students’ attitudes toward older adults after participation in a virtual simulation.
The triangulation research framework was employed to explore the effectiveness of AI-driven qualitative insights. The use of ATLAS.ti AI-generated coding, the pre-post simulation UCLA Geriatrics Attitude Survey (GAS), and random sampling were used to confirm the AI-driven insights.
Undergraduate nursing students (N=151) participated in a screen-based virtual simulation. Participants completed a pre-post UCLA GAS and end-of-simulation open-ended survey. A paired sample t-test was used to compare scores and was statistically significant at the 0.10 significance level (t = 1.88, p = 0.06). Three themes emerged from AI-generated coding of the open-ended survey: Empathy, Safety, and Respect. Random sampling was used to confirm the findings.
This study demonstrates AI-driven technologies are a solution for the qualitative analysis of big data, thus ensuring every participant’s voice is heard.
Learning Objectives:
Upon completing the learning activity, participants will appreciate the importance of analyzing qualitative data to support the change in attitudes after participating in an older adult simulation-based learning event.
Upon completing the learning activity, participants will analyze the use of AI-driven technology to explore large qualitative data sets.
Upon completing the learning activity, participants will explore the themes related to the change in learners’ attitudes after participation in a simulated learning event with an older adult.
