Artificial intelligence and predictive analytics in nursing education

The relationship between nursing students' attitudes toward artificial intelligence and their creative personality traits

AIM

The relationship between nursing students' attitudes toward artificial intelligence and their creative personality traits was examined in this study.

DESIGN

This study, conducted with 492 nursing students enrolled at a university in Turkey, was designed using a descriptive and relational methodology. The data were gathered through the "Personal Information Form," the "General Attitude Scale toward Artificial Intelligence," and the "Creative Personality Traits Scale."

METHODS

The data for the research were gathered from surveys conducted between January 2024 and May 2024.

FINDINGS

The average score for students' attitudes toward artificial intelligence was 74.52 ± 10.29, while the score for creative personality traits was 67.20 ± 10.34. Correlation analysis results indicate a strong relationship between these two factors (p < 0.05).

CONCLUSIONS

Nursing students' attitudes toward artificial intelligence and creative personality traits are above average.

IMPLICATIONS FOR NURSING AND HEALTH POLICY

The development of creativity is crucial for effectively integrating artificial intelligence technologies into nursing practice. Additionally, this research highlights the need for policy development regarding regulations and ethical practices related to using artificial intelligence in healthcare services.

Bibliometric and content analysis of ChatGPT research in nursing education: The rabbit hole in nursing education

AIM

This study was conducted to perform the bibliometric and content analysis of ChatGPT studies in nursing education.

BACKGROUND

ChatGPT is an artificial intelligence-based chatbot developed by OpenAI. The benefits and limitations of the use of ChatGPT in nursing education are still discussed; however, it is a tool having potential to be used in nursing education.

DESIGN

Bibliometric and content analysis.

METHODS

The study data were scanned through Scopus and Web of Science. Bibliometric analysis was carried out with VOSViewer and Bibliometrix software. In the bibliometric analysis, science mapping and performance analysis techniques were used. Various bibliometric data, including most cited publications, journals and countries, were analyzed and visualized. The synthetic knowledge synthesis method was used in content analysis.

RESULTS

We analyzed 53 publications to which 151 authors contributed. The publications had been published in 29 different journals. The average number of citations of publications is 8.2. It was determined that most of the articles were published in Nurse Education Today and Nurse Educator journals and that the leading countries were the USA and Canada. It was observed that international cooperation on the issue was weak. The most frequently mentioned keywords in the publications were "ChatGPT", "artificial intelligence" and "nursing". The following three themes emerged after the content analysis: (1) Integration of ChatGPT into nursing education; (2) Potential benefits and limitations of ChatGPT; and (3) Stepping down the rabbit hole.

CONCLUSIONS

We expect that the results of the study can give nursing faculties and academics ideas about the current status of ChatGPT in nursing education and enable them to make inferences for the future.

Performance of ChatGPT on Registered Nurse License Exam in Taiwan: A Descriptive Study

(1) Background: AI (artificial intelligence) chatbots have been widely applied. ChatGPT could enhance individual learning capabilities and clinical reasoning skills and facilitate students' understanding of complex concepts in healthcare education. There is currently less emphasis on its application in nursing education. The application of ChatGPT in nursing education needs to be verified. (2) Methods: A descriptive study was used to analyze the scores of ChatGPT on the registered nurse license exam (RNLE) in 2022~2023, and to explore the response and explanations of ChatGPT. The process of data measurement encompassed input sourcing, encoding methods, and statistical analysis. (3) Results: ChatGPT promptly responded within seconds. The average score of four exams was around 51.6 to 63.75 by ChatGPT, and it passed the RNLE in 2022 1st and 2023 2nd. However, ChatGPT may generate misleading or inaccurate explanations, or it could lead to hallucination; confusion or misunderstanding about complicated scenarios; and languages bias. (4) Conclusions: ChatGPT may have the potential to assist with nursing education because of its advantages. It is recommended to integrate ChatGPT into different nursing courses, to assess its limitations and effectiveness through a variety of tools and methods.

Effect of Artificial Intelligence Course in Nursing on Students' Medical Artificial Intelligence Readiness: A Comparative Quasi-Experimental Study

BACKGROUND

It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research.

PURPOSE

This study examined the impact of an AI course in the nursing curriculum on students' medical AI readiness.

DESIGN AND METHODS

This comparative quasi-experimental study was conducted with a total of 300 3rd-year nursing students, 129 in the control group and 171 in the experimental group. Students in the experimental group received 28 hours of AI training. The students in the control group were not given any training. Data were collected by a socio-demographic form and the Medical Artificial Intelligence Readiness Scale.

RESULTS

An AI course should be included in the nursing curriculum, according to 67.8% of students in the experimental group and 57.4% of students in the control group. The mean score of the experimental group on medical AI readiness was higher ( P < .05) and the effect size of the course on readiness was -0.29.

CONCLUSIONS

An AI nursing course positively affects students' readiness for medical AI.

Need assessment for history-taking instruction program using chatbot for nursing students: A qualitative study using focus group interviews

Purpose

A comprehensive health history contributes to identifying the most appropriate interventions and care priorities. However, history-taking is challenging to learn and develop for most nursing students. Chatbot was suggested by students to be used in history-taking training. Still, there is a lack of clarity regarding the needs of nursing students in these programs. This study aimed to explore nursing students' needs and essential components of chatbot-based history-taking instruction program.

Methods

This was a qualitative study. Four focus groups, with a total of 22 nursing students, were recruited. Colaizzi's phenomenological methodology was used to analyze the qualitative data generated from the focus group discussions.

Results

Three main themes and 12 subthemes emerged. The main themes included limitations of clinical practice for history-taking, perceptions of chatbot used in history-taking instruction programs, and the need for history-taking instruction programs using chatbot. Students had limitations in clinical practice for history-taking. When developing chatbot-based history-taking instruction programs, the development should reflect students' needs, including feedback from the chatbot system, diverse clinical situations, chances to practice nontechnical skills, a form of chatbot (i.e., humanoid robots or cyborgs), the role of teachers (i.e., sharing experience and providing advice) and training before the clinical practice.

Conclusion

Nursing students had limitations in clinical practice for history-taking and high expectations for chatbot-based history-taking instruction programs.

Theories informing technology enhanced learning in nursing and midwifery education: A systematic review and typological classification

BACKGROUND

Learning is a complex process involving internal cognitive processes and external stimuli from curricula, pedagogical strategies, and the learning environment. Theories are used extensively in higher education to understand the intricacies of adult learning and improve student outcomes. Nursing and midwifery education uses a range of technology enhanced learning (e-learning) approaches, some of which are underpinned by theoretical frameworks.

OBJECTIVE

Synthesise literature on theories that inform technology enhanced learning in nursing and midwifery education.

DESIGN

A systematic review.

DATA SOURCE

CINAHL, ERIC, MEDLINE and PubMed were searched for relevant studies (2000-2021). Reference lists of related literature reviews were hand searched.

REVIEW METHODS

Title and abstract, followed by full texts were screened by two reviewers independently using predefined eligibility criteria. Quality appraisal was not undertaken. Data were extracted and Merriam and Bierema's typology of adult learning theories used to categorise theories in each study.

RESULTS

Thirty-three studies were included, incorporating twenty-nine distinct learning theories from the behaviourist, cognitivist, constructivist, and social cognitivist domains, with constructivist being the most widely used. Kolb's Experiential Learning Theory and Driscoll's Constructivist Learning Theory were the most commonly reported theories. The population of learners were mainly undergraduate nursing students who used a range of online, mobile, blended or computerised learning, virtual reality, or digital forms of simulation, primarily in university settings. Theories were employed to inform the technology enhanced learning intervention or to help explain how these could improve student learning.

CONCLUSION

This review highlighted a range of theories, particularly constructivist approaches, that underpin research on technology enhanced learning in nursing education, by informing or explaining how these digital interventions support learning. More rigorous research that examines the myriad of theoretical frameworks and their effectiveness in informing and explaining technology enhanced learning is needed to justify this approach to pedagogical nursing research and practice.

Integration of artificial intelligence into nursing practice

Background

Methods

Results

Conclusions

Artificial intelligence in nursing and midwifery: A systematic review

BACKGROUND

Artificial Intelligence (AI) techniques are being applied in nursing and midwifery to improve decision-making, patient care and service delivery. However, an understanding of the real-world applications of AI across all domains of both professions is limited.

OBJECTIVES

To synthesise literature on AI in nursing and midwifery.

METHODS

CINAHL, Embase, PubMed and Scopus were searched using relevant terms. Titles, abstracts and full texts were screened against eligibility criteria. Data were extracted, analysed, and findings were presented in a descriptive summary. The PRISMA checklist guided the review conduct and reporting.

RESULTS

One hundred and forty articles were included. Nurses' and midwives' involvement in AI varied, with some taking an active role in testing, using or evaluating AI-based technologies; however, many studies did not include either profession. AI was mainly applied in clinical practice to direct patient care (n = 115, 82.14%), with fewer studies focusing on administration and management (n = 21, 15.00%), or education (n = 4, 2.85%). Benefits reported were primarily potential as most studies trained and tested AI algorithms. Only a handful (n = 8, 7.14%) reported actual benefits when AI techniques were applied in real-world settings. Risks and limitations included poor quality datasets that could introduce bias, the need for clinical interpretation of AI-based results, privacy and trust issues, and inadequate AI expertise among the professions.

CONCLUSION

Digital health datasets should be put in place to support the testing, use, and evaluation of AI in nursing and midwifery. Curricula need to be developed to educate the professions about AI, so they can lead and participate in these digital initiatives in healthcare.

RELEVANCE FOR CLINICAL PRACTICE

Adult, paediatric, mental health and learning disability nurses, along with midwives should have a more active role in rigorous, interdisciplinary research evaluating AI-based technologies in professional practice to determine their clinical efficacy as well as their ethical, legal and social implications in healthcare.