Jun 2026· International Journal of Innovative Science and Research Technology· pp. 1213· 0 citations
TL;DR
The mediating role of selfregulated learning in the relationship between the use of artificial intelligence and clinical competence among nursing students is determined and the mechanism by which AI use contributes to the development of clinical competence remains underexplored.
Abstract
Artificial Intelligence (AI) has emerged as a valuable educational tool that supports nursing students’ learning
processes, clinical preparation, and independent learning. However, the mechanism by which AI use contributes to the
development of clinical competence remains underexplored. This study aimed to determine the mediating role of selfregulated learning in the relationship between the use of artificial intelligence and clinical competence among nursing
students. The study was conducted among Bachelor of Science in Nursing students enrolled during Academic Year 2025–
2026 at a higher education institution in Western Mindanao, Philippines. An explanatory sequential mixed-methods design
was utilized. The quantitative phase involved 252 nursing students selected through simple random sampling, while the
qualitative phase involved eight (8) purposively selected participants who participated in semi-structured interviews. Data
were collected using three researcher-adapted questionnaires measuring AI utilization, self-regulated learning, and clinical
competence, along with an interview guide for the qualitative component. Quantitative data were analyzed using Jamovi
software, including frequencies, percentages, means, standard deviations, Pearson product-moment correlations, and
mediation analyses.
Initial evidence is provided that the NAIRS is a valid and reliable instrument for assessing nursing students' readiness for artificial intelligence across knowledge/awareness, willingness to use AI, self-efficacy, and ethical awareness domains and may be useful for educational needs assessment and curriculum planning in nursing education.
Sumeyye Akçoban, Gülay Koca, S. Berşe· BMC Nursing· 0 citations
It is indicated that both self-directed learning and AI acceptance are associated with perceived clinical competence, with AI acceptance acting as a mediating factor.
Boshra Karem Mohamed El-Sayed, Ayman Ateq Alamri, M. G. R. Asal et al.· BMC Medical Education· 0 citations
In nursing practice, clinical decision-making is a critical competency that students must possess to ensure patient safety and the quality of nursing care. This process is influenced not only by cognitive abilities but also by emotional intelligence, which helps students remain objective when facing complex and high-pressure clinical situations. Although various studies have examined the relationship between the two, the results obtained remain inconsistent. Therefore, this study aims to analyze the relationship between emotional intelligence and clinical decision-making among students in the Professional Nursing Program using a descriptive correlational design and a cross-sectional approach. A total of 107 students from three nursing education institutions in Banda Aceh were selected as participants using consecutive sampling. Emotional intelligence was measured using the Schutte Self-Report Emotional Intelligence Test, while clinical decision-making ability was measured using the Clinical Decision-Making in Nursing Scale. Data analysis was performed using Spearman’s correlation test. The results of the study indicate a positive and significant relationship between emotional intelligence and clinical decision-making (r=0.259; p= 0.007). Among all dimensions of emotional intelligence, Understanding of Emotion showed the strongest and most significant relationship with all components of clinical decision-making (p = 0.001), whereas the dimensions of Utilisation of Emotion and Regulation of Emotion did not show a significant relationship. Emotional intelligence plays a role in supporting clinical decision-making abilities among students in the Professional Nursing Program. Therefore, the development of emotional intelligence needs to be integrated into nursing education to support the improvement of students’ clinical decision-making abilities.
Irfanita Nurhidayah, Yullyzar, Nani Safuni et al.· Jurnal Ners dan Kebidanan (J...· 0 citations
BACKGROUND
Artificial intelligence (AI)-based competency assessments have been incorporated into nurse recruitment processes in South Korea; however, nursing applicants' and educators' experiences with these assessments, as well as their perceptions of the effectiveness of these assessments in nurse selection, have not been examined.
PURPOSE
The aim of this study was to comprehensively explore the perceptions of nursing applicants and educators of the effectiveness of AI-based competency assessments in the process of selecting new nurses for patient care roles.
METHODS
A qualitative descriptive design was used in this study. Data were collected between February and August 2024 in South Korea. Semistructured focus group interviews were conducted with 10 nursing applicants and 10 nurse educators, including nursing professors and nurse managers, regarding their experience participating in AI-based competency assessments. Focus group interviews were conducted in five groups, each consisting of three to five participants. Data were analyzed using conventional content analysis. The COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines were used to assess study rigor.
RESULTS
The analysis of the interviews revealed eight subthemes and four themes derived from 32 codes. The following four themes were identified: (a) doubts about the evaluation method and criteria, (b) efficiency of AI-based competency assessments, (c) challenges in preparing for AI-based competency assessments, and (d) improvements and alternative approaches to nurse selection.
CONCLUSIONS/IMPLICATIONS FOR PRACTICE
This study highlights the potential of AI-based competency assessments to improve fairness and efficiency in the process of selecting and hiring new nurses. To ensure validity, developing tailored algorithms that reflect core nursing competencies and establishing clear evaluation criteria is necessary. Also, interdisciplinary collaboration is needed to support the ethical and practical integration of AI in the process of selecting and hiring new nurses.
Hyeongsuk Lee, Hyeongju Ryu, Hye Jin Yoo· Journal of Nursing Research· 0 citations
Findings show that nursing students who feel more prepared for AI tend to be less anxious about technology and add AI training more consistently throughout the nursing curriculum and building digital skills may help reduce fear and make it easier for students to use AI tools in the future.
The findings indicate that nursing students had generally positive levels of AI literacy and attitudes toward AI, and higher AI literacy was associated with more positive attitudes toward AI.
M. Çil, Berna Eren Fidancı, D. Yildiz· Journal of Education and Res...· 0 citations