Student nurses' attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study

AIM

The aim of this study was to investigate the attitudes and intentions of student nurses towards Artificial Intelligence (AI) in the context of nursing practice and to explore the relationship between their attitudes towards AI, their perceptions of AI utilization in nursing practice, and their intentions to adopt AI technology. The study hypothesized that perceived utilization of AI in nursing practice would positively influence the intention to use AI and that attitudes towards AI would mediate this relationship.

BACKGROUND

AI has the potential to revolutionize various aspects of healthcare, including nursing practice. As AI technology continues to advance, it becomes increasingly important for nurse education to prepare student nurses to leverage AI technology and be willing to adopt it in their nursing practice.

DESIGN

Cross-sectional design.

METHODS

A total of 200 student nurses from two government-owned nursing schools participated in the study. Mediation testing was performed using Hayes' PROCESS macro in SPSS (Model 4).

RESULTS

Perceived AI utilization in nursing practice had a significant positive effect on student nurses' attitudes towards AI (β = 0.450, p < 0.001) and their intention to adopt AI technology (β = 0.458, p < 0.001). Attitudes towards AI partially mediated the relationship between perceived AI utilization in nursing practice and the intention to adopt AI technology (β = 0.255).

CONCLUSION

The findings suggest that student nurses had favorable perceptions of AI utilization in nursing practice, expressed high intentions to adopt AI technology, and held positive attitudes towards AI. Furthermore, student nurses' perceptions of AI utilization in nursing practice influenced their attitudes towards AI, which, in turn, affected their intentions to adopt AI technology. Nursing education programs should incorporate AI-focused coursework, training, and experiential learning to further enhance students' readiness and proficiency in utilizing AI technology. Additionally, healthcare institutions should consider creating a supportive environment for nursing students to explore and embrace AI, ultimately preparing them for the evolving landscape of AI-enhanced healthcare practice.

TWEETABLE ABSTRACT

Student nurses' attitudes towards AI technology were influenced by their perceptions of AI utilization in nursing practice, which subsequently influenced their intentions to adopt AI technology.

Factors influencing student nurses' readiness to adopt artificial intelligence (AI) in their studies and their perceived barriers to accessing AI technology: A cross-sectional study

BACKGROUND

With the projected significant increase in the use of AI in nursing education, it becomes vital for nurse faculty to adequately equip student nurses with the necessary competences to effectively utilize AI in their studies. Ensuring that student nurses are prepared and ready to embrace AI technology is imperative for their successful integration into the healthcare workforce.

OBJECTIVE

This study aimed to examine student nurses' readiness to embrace AI technology, explore associated factors, and identify perceived barriers to accessing AI technology.

DESIGN

Cross sectional study.

SETTINGS

One public-owned nursing school in the Philippines.

PARTICIPANTS

Three hundred twenty-three student nurses.

METHODS

Data were collected using structured questionnaires. Descriptive statistics and multivariable analysis were performed to analyze the data.

RESULTS

The results revealed that student nurses demonstrated moderate readiness to embrace AI in their studies (M = 2.906, SD = 0.692) and perceived moderate barriers to accessing AI technology (M = 2.336, SD = 0.719). Factors associated with students' readiness to embrace AI included self-rated technological proficiency (β = 0.170, p = 0.014), understanding of AI-powered technologies (β = 0.260, p < 0.001), and perceived AI use in nursing practice (β = 0.153, p = 0.022). The study also identified potential barriers to accessing AI technology, such as lack of computer skills to navigate AI, lack of AI knowledge and awareness, and time constraints.

CONCLUSION

The findings of this study provided valuable insights into the factors influencing student nurses' attitudes towards AI and shed light on their perceived barriers to accessing AI technology. By enhancing technological proficiency, increasing AI understanding, and providing practical experiences, nurse faculty can better prepare future nurses to effectively navigate the AI-driven healthcare environment and contribute to improved patient care outcomes.

Clearing the way for participatory data stewardship in artificial intelligence development: a mixed methods approach

Participatory data stewardship (PDS) empowers individuals to shape and govern their data via responsible collection and use. As artificial intelligence (AI) requires massive amounts of data, research must assess what factors predict consumers' willingness to provide their data to AI. This mixed-methods study applied the extended Technology Acceptance Model (TAM) with additional predictors of trust and subjective norms. Participants' data donation profile was also measured to assess the influence of individuals' social duty, understanding of the purpose and guilt. Participants (N = 322) completed an experimental survey. Individuals were willing to provide data to AI via PDS when they believed it was their social duty, understood the purpose and trusted AI. However, the TAM may not be a complete model for assessing user willingness. This study establishes that individuals value the importance of trusting and comprehending the broader societal impact of AI when providing their data to AI.Practitioner summary: To build responsible and representative AI, individuals are needed to participate in data stewardship. The factors driving willingness to participate in such methods were studied via an online survey. Trust, social duty and understanding the purpose significantly predicted willingness to provide data to AI via participatory data stewardship.

Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model

Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.

Attitudes Toward the Adoption of 2 Artificial Intelligence-Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study

BACKGROUND

Despite growing efforts to develop user-friendly artificial intelligence (AI) applications for clinical care, their adoption remains limited because of the barriers at individual, organizational, and system levels. There is limited research on the intention to use AI systems in mental health care.

OBJECTIVE

This study aimed to address this gap by examining the predictors of psychology students' and early practitioners' intention to use 2 specific AI-enabled mental health tools based on the Unified Theory of Acceptance and Use of Technology.

METHODS

This cross-sectional study included 206 psychology students and psychotherapists in training to examine the predictors of their intention to use 2 AI-enabled mental health care tools. The first tool provides feedback to the psychotherapist on their adherence to motivational interviewing techniques. The second tool uses patient voice samples to derive mood scores that the therapists may use for treatment decisions. Participants were presented with graphic depictions of the tools' functioning mechanisms before measuring the variables of the extended Unified Theory of Acceptance and Use of Technology. In total, 2 structural equation models (1 for each tool) were specified, which included direct and mediated paths for predicting tool use intentions.

RESULTS

Perceived usefulness and social influence had a positive effect on the intention to use the feedback tool (P<.001) and the treatment recommendation tool (perceived usefulness, P=.01 and social influence, P<.001). However, trust was unrelated to use intentions for both the tools. Moreover, perceived ease of use was unrelated (feedback tool) and even negatively related (treatment recommendation tool) to use intentions when considering all predictors (P=.004). In addition, a positive relationship between cognitive technology readiness (P=.02) and the intention to use the feedback tool and a negative relationship between AI anxiety and the intention to use the feedback tool (P=.001) and the treatment recommendation tool (P<.001) were observed.

CONCLUSIONS

The results shed light on the general and tool-dependent drivers of AI technology adoption in mental health care. Future research may explore the technological and user group characteristics that influence the adoption of AI-enabled tools in mental health care.