With the projected shortage of nursing faculty, formalized programs are needed to provide mentorship programs that will encourage and support faculty as they move from the role of novice to expert educator. The purpose of this qualitative study is to explore the experience of protégés participating in a mentorship program for novice faculty. An interpretive phenomenological research study was conducted to illuminate the meaning of the experiences of the protégés participating in the program. The study of the experience of protégés participating in a mentor-protégé program led to the emergence of three main themes: Creating a Meaningful Mentor-Protégé Relationship, Transitioning as a New Nurse Educator, and the Mentor-Protégé Program—Lessons Learned. Data from the study will provide insight into the meaning of receiving mentorship in the role of novice nurse educator.

Background/Objectives: The projected increase from 58 million older adults in 2022 to 82 million by 2050 in the United States highlights the urgency of preparing nursing students to care for this aging population. However, studies reveal negative attitudes among nursing students toward older adults. A three-phased educational intervention that included an artificial intelligence (AI)-driven virtual simulation was implemented to address this. AI-generated simulations promise to expose marginalized groups and strengthen future nurses’ knowledge, skills, and attitudes. Methods: A convergent mixed-method design was used to measure the change in nursing students’ attitudes toward older adults, as measured by the UCLA Geriatrics Attitudes Survey and a Guided Reflection survey after participating in an Artificial Intelligence in Education learning event (n = 151). Results: The results indicate that post-intervention scores (M = 35.07, SD = 5.34) increased from pre-intervention scores (M = 34.50, SD = 4.86). This difference was statistically significant at the 0.10 significance level (t = 1.88, p = 0.06). The qualitative analysis indicated that the attitudes impacted were challenging and overcoming ageism, increased empathy and patience, and enhanced communication skills. Conclusions: Utilizing artificial intelligence technology during educational events effectively yields measurable learning outcomes. Cultivating positive attitudes toward older adults is essential for competent care in an aging society. This study was prospectively approved by the university’s Institutional Review Board (IRB) on 30 July 2021, IRB-FY22-3.

As the global population ages, nurses with a positive attitude toward caring for older adults is crucial. However, studies indicate that nursing students often exhibit negative attitudes toward older adults. This study aimed to determine if a three-phased educational intervention significantly improved nursing students’ attitudes toward older adults. A pre/post-test study design was used to measure the change in nursing students’ attitudes toward older adults, as measured by the UCLA Geriatrics Attitudes Survey, after participating in an Artificial Intelligence in Education learning event (n = 151). Results indicate that post-intervention scores (M = 35.07, SD = 5.34) increased from pre-intervention scores (M = 34.50, SD = 4.86). This difference was statistically significant at the 0.10 significance level (t = 1.88, p = 0.06). Incorporating artificial intelligence technology in a learning event is an effective educational strategy due to its convenience, repetition, and measurable learning outcomes. Improved attitudes toward older adults are foundational for delivering competent care to a rapidly growing aging population. This study was prospectively registered with the university’s Institutional Review Board (IRB) on 30 July 2021 with the registration number IRB-FY22-3.

Background/Objectives: Technological advancements are revolutionizing nursing education by improving precision, patient outcomes, and learning experiences. There is an urgent need for systematic frameworks to help nurse educators effectively integrate advanced technologies into their teaching methods. This manuscript introduces the Artificial Intelligence-Driven Technology Adoption in Nursing Education (AID-TANE) framework and operationalizes its use through a pilot study with undergraduate nursing students. Methods: The framework was tested through a convergent mixed-methods pre/post-test study design involving 160 senior-level community health nursing students who participated in an AI-driven educational intervention. Quantitative data were collected using the Facts on Aging quiz, while qualitative data were gathered from a reflective survey. Statistical analyses included paired-sample t-tests and a qualitative content analysis. Results: The study revealed a statistically significant increase in learners’ knowledge about older adults, with mean scores improving from 33.29 (SD = 5.33) to 36.04 (SD = 6.76) post-intervention (t = 5.05, p < 0.001). The qualitative analysis identified four key themes: communication and understanding, patience and empathy, respect for independence, and challenging stereotypes. Conclusions: This study found that AI-driven educational tools significantly improved nursing students’ knowledge about older adults and positively influenced their learning experiences. The findings highlight the need for targeted frameworks like AID-TANE to effectively integrate AI into nursing education, ensuring that students are ready for a technologically advanced practice setting.

Background and Purpose: Prelicensure nursing students lack the situational awareness to promote timely intervention with a patient in septic shock. This study evaluated a multifaceted educational project that determined the impact on nursing students' knowledge retention and time to task (TTT). Methods: A quasi-experimental, repeated-measures design was used to evaluate students' knowledge retention and TTT. Eighty-four prelicensure nursing students participated in groups of 4 students to participate in a high-fidelity simulation. Results: Results show knowledge retention was significant between the pretest scores and 2 repeated assessment scores. The repeated-measures analysis of variance time effect P value was .02. The overall TTT group response suggested most (64/84, 76%) students responded within 5 minutes of patient deterioration. Conclusions: A multifaceted approach was effective to influence knowledge of septic shock over time and demonstrate students' ability to intervene with a septic shock patient in a timely manner.