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.

Density functional theory methods are used to calculate the structures and energies of silicon trihydride $({\mathrm{SiH}}_{\mathrm{3}})$ and dihydride $({\mathrm{SiH}}_{\mathrm{2}})$ species on the Si(100)-(2×1) surface. These species are intermediates in the growth of silicon films by chemical vapor deposition of silane and disilane. The lowest-energy trihydride species is metastable with respect to the lowest-energy dihydride species, but two surface dangling bonds must be available to affect the transformation to the dihydride. In the lowest-energy configurations, dimers either have both dangling bonds occupied or both unoccupied. While the energy difference between isomers with fully occupied and partially occupied dimers will strongly favor fully occupied dimers at low temperatures, there will be a distribution of dimer occupations at high temperatures. The structures and energies of some other local minima corresponding to tri- and dihydrides are also described. While these species are energetically unfavorable and should only exist transiently, they illustrate the relative energetics of some alternative bonding behavior of the silicon surface.

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.

In statistical process control applications, situations may arise in which several presumably identical processes or “streams” are desired to be simultaneously monitored. Such a monitoring scenario is commonly referred to as a “Multiple Stream Process (MSP).” Traditional MSP charting techniques rely on the assumption of normality, which may or may not be met in practice. Thus, a cumulative summation nonparametric MSP control charting technique, based on a modification of the classical extended median test was developed and is referred to as the “Nonparametric Extended Median Test Cumulative Summation (NEMT-CUSUM) chart.” Chart development, including calculation of control limits, is given. Through simulation, the NEMT-CUSUM is shown to perform consistently in the presence of normal and non-normal data. Moreover, it is shown to perform more optimally than parametric alternatives in certain circumstances. Results suggest the NEMT-CUSUM may be an attractive alternative to existing parametric MSP monitoring techniques in the case when distributional assumptions about the underlying monitored process cannot reasonably be made.