Cited by 291Artificial Intelligence in Healthcare and EducationHealthcare Education and Workforce IssuesTechnology Adoption and User Behaviour

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index; none of the 15 sources its claims cite is checked for support yet.

Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions

Open access

Abstract

Background: Artificial intelligence (AI) technology has recently seen rapid advancement, with an expanding role and scope in nursing education and healthcare. This study identifies the influence of AI ethics awareness, attitude toward AI, anxiety, and self-efficacy on nursing students’ behavioral intentions to use AI-based healthcare technology. Methods: The participants included 189 nursing students in Gyeonggi-do, with data collected from November to December 2021 using self-reported questionnaires. We analyzed the data using the SPSS/WIN 26.0 program, including a t-test, Pearson’s correlation coefficient, and hierarchical multiple linear regression. Results: The results revealed that AI ethical awareness (t = − 4.32, p < .001), positive attitude toward AI (t = − 2.60, p = .010), and self-efficacy (t = − 2.65, p = .009) scores of the third and fourth-year nursing students were higher, while their anxiety scores were lower (t = 2.30, p = .022) compared to the scores of the first and second-year nursing students. The factors influencing behavioral intention included a positive attitude toward AI (β = 0.58) and self-efficacy (β = 0.22). The adjusted R 2 was 0.42. Conclusion: It is necessary to inculcate a positive attitude toward AI and self-efficacy by providing educational programs on AI-based technology in healthcare settings.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

In 189 Korean nursing students, positive attitude toward AI and self-efficacy were the factors significantly associated with behavioral intention to use AI-based healthcare technology, while AI ethics awareness, negative attitude, and anxiety were not. Third- and fourth-year students had higher AI ethics awareness, positive attitude, and self-efficacy and lower anxiety than first- and second-year students.

Key findings

  • In hierarchical multiple linear regression, positive attitude toward AI (β = 0.58 in the abstract/text; standardized β = 0.49 in the regression table) and self-efficacy (β = 0.22) significantly predicted behavioral intention; the model was significant (F = 28.38, p < .001) with adjusted R² = 0.42. AI ethics awareness (β = −0.04, p = .590), negative attitude toward AI (β = −0.02, p = .747), and anxiety (β = −0.11, p = .102) were not significant.
  • Third- and fourth-year students showed significantly higher AI ethics awareness (t = −4.32, p < .001), positive attitude toward AI (t = −2.60, p = .010), and self-efficacy (t = −2.65, p = .009), and significantly lower anxiety (t = 2.30, p = .022) than first- and second-year students; behavioral intention was higher but close to significance (p = .053).
  • Behavioral intention correlated most strongly with positive attitude toward AI (r = .62) and self-efficacy (r = .51). Self-efficacy negatively correlated with negative attitude toward AI (r = −.31) and anxiety (r = −.34); anxiety positively correlated with negative attitude toward AI (r = .44).
  • Only 36 students (19.0%) had participated in AI-related education; no statistically significant differences in measured variables were found by gender, age, Internet use time, or AI education experience.

Methods

  • Cross-sectional study of 189 nursing students in Gyeonggi-do, Korea, recruited from first to fourth year; self-reported questionnaires were used. The abstract states data were collected November–December 2021, while the methods state February 16–23, 2022. G*Power indicated a minimum sample of 178; 195 questionnaires were distributed and 189 were analyzed (dropout rate 3.1%).
  • Measures included the Test for Artificial Intelligence Ethics Awareness (TAIEA; 24 items, eight categories; Cronbach’s α = 0.67 in this study), the General Attitudes toward Artificial Intelligence Scale (GAAIS; positive α = 0.85, negative α = 0.76), and TAM-based items for anxiety (4 items, α = 0.86), self-efficacy (4 items, α = 0.75), and behavioral intention (3 items, α = 0.66).
  • Analysis used SPSS/WIN 26.0 with t-tests, Pearson’s correlation coefficient, and hierarchical multiple linear regression.

Limitations

  • The paper states that data came from a single nursing college and most participants had no prior AI education, limiting generalizability. It also states that the TAIEA lacked factor analysis at development, items were excluded after factor analysis, and internal reliability was low (0.69 in the discussion; 0.67 in the methods), requiring further tool revision and validation.
  • Evident from the text: the cross-sectional design cannot establish causality; behavioral intention was self-reported rather than observed use; the sample was 85.7% female and 65.1% second-year students, which may further limit generalizability; and data collection dates are inconsistent between the abstract and methods.
  • Evident from the text: low reliability for behavioral intention (α = 0.66) and AI ethics awareness (α = 0.67) may attenuate the findings.

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Integrity signals

Registry

Crossref · 10.1186/s12912-022-01048-0

Cites

24 of 24 references verified0 citation errors · 0 not checkable

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Nothing to recompute.

Verification

Claims the paper makes

16 Own result · 12 Not checked yet · 6 Own method · 4 Own interpretation

Verify all 15 references — free account

Show all 21 claims

  • It may state two separate claims as one. 12
  • Probably not a substantial claim. 9
17 of 17
  • The paper relies on this without support.

    No studies have identified the self-efficacy, attitude, and intention to use AI in providing nursing care.

    “In other words, while nurses demonstrated a high degree of awareness regarding the usefulness and efficiency of AI technology, no studies have identified the self-efficacy, attitude, and intention to use AI in providing nursing care.”
  • The paper relies on this without support.

    The nursing education curriculum in Korea specifies that third- and fourth-year students participate in clinical practice training.

    “The nursing education curriculum in Korea specifies that third and fourth-year students participate in clinical practice training.”
  • The paper relies on this without support.

    Clinical practice requires a basic knowledge of technologies related to nursing skills and medical equipment.

    “Clinical practice requires a basic knowledge of technologies related to nursing skills and medical equipment.”
3 of 3

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9 of 9

Citations

Cites

Research works printed but not linked: 3

  1. Lee JW. Can we impose responsibilities on artificial intelligence? To seek accountability-oriented ethics for artificial intelligence. Korean J Philos Sci. 2019;22(2):79–104.
  2. Ministry of Science and ICT (MSIT). Artificial Intelligence Ethics Standards - Press release. 2020. https://www.msit.go.kr/bbs/view.do?sCode=user&mPid=112&mId=113&bbsSeqNo=94&nttSeqNo=3179742 Accessed 7 July 2022.
  3. Swan BA. Assessing the knowledge and attitudes of registered nurses about artificial intelligence in nursing and health care. Nurs Econ. 2021;39(3):139–43.

Metadata

Authors
Kwak, Y., Ahn, J.-W., & Seo, Y. H.
Affiliations
  • Chung-Ang University (KR)
  • Gangneung–Wonju National University (KR)
  • Yeoju University (KR)
Corresponding author
Seo, Yon Hee
Venue
BMC Nursing
Published
2022-09-30
Volume
21
Issue
1
Pages
267-267
ISSN
1472-6955 (ISSN-L)
OpenAlex
W4298147439
Cited by
291
References
24
Citations by year
2026: 102 · 2025: 108 · 2024: 65 · 2023: 15
FWCI
14.17
Type
journal article
Language
en
Open access
Locations
  • BMC Nursing (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Chung-Ang University Research Grants in 2021
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Nursing › Leadership and Management › Healthcare Education and Workforce Issues
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
AI ethics, AI ethics awareness, attitudes toward AI, AI anxiety, self-efficacy, behavioral intention, nursing students, nursing education

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