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

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

Open access
Kwak, Yeunhee, Ahn, Jung-Won, Seo, Yon Hee·2022·BMC Nursing
DOI 10.1186/s12912-022-01048-0PMC PMC9526272
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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.

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.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1186/s12912-022-01048-0

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 15 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Anxiety had a positive correlation with negative attitude toward AI (r = .44).

    “Anxiety had a positive correlation with a negative attitude toward AI (r = .44).”
    Quote in the paperNot a substantial claim
  • Findingown data

    There were statistically significant correlations among all measured variables.

    “There were statistically significant correlations among all measured variables.”
    Quote in the paperSubstantial claim
  • Findingown data

    Positive attitude toward AI scores were significantly higher in third- and fourth-year nursing students than in first- and second-year nursing students.

    “However, the third and fourth-year students showed significantly higher AI ethics awareness, positive attitudes toward AI, self-efficacy scores, and significantly lower anxiety scores than first and second-year students.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Anxiety scores were significantly lower in third- and fourth-year nursing students than in first- and second-year nursing students.

    “However, the third and fourth-year students showed significantly higher AI ethics awareness, positive attitudes toward AI, self-efficacy scores, and significantly lower anxiety scores than first and second-year students.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Self-efficacy had a significant positive influence on nursing students' behavioral intentions to use AI-based healthcare technology (β = 0.22).

    “The regression analysis showed that a positive attitude toward AI (β = 0.58) and self-efficacy (β = 0.22) were the two variables that had a significant influence on nursing students’ behavioral intentions to use AI-based healthcare technology.”
    Quote in the paperSubstantial claim
  • May combine two claims. 12
  • Classified as probably not a substantial claim. 9

  • May combine two claims.

    Findingown data

    Positive attitude toward AI and self-efficacy showed the highest correlation with behavioral intention (as reported: r = − .62 and r = .51, respectively).

    “Positive attitude toward AI (r = − .62) and self-efficacy (r = .51) showed the highest correlation with behavioral intention.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The regression model was statistically significant (F = 28.38, p < .001) and explained 42.1% of the variance in behavioral intention.

    “The regression model was statistically significant (F = 28.38, p < .001) and had an explanatory power of 42.1%.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    There were no statistically significant differences in AI ethics awareness, positive and negative attitudes toward AI, anxiety, self-efficacy, and behavioral intention scores according to gender, age, Internet use time, and AI education experience.

    “Analysis of the measured variables according to demographic characteristics showed no statistically significant differences in AI ethics awareness, positive and negative attitudes toward AI, anxiety, self-efficacy, and behavioral intention scores according to gender, age, Internet use time, and AI education experience.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Cronbach's alpha for AI ethics awareness in this study was 0.67.

    “Cronbach’s α of the tool was 0.81 at the time of development [6] and 0.67 in this study.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [6] Kim GS, Shin YJ. Study on the development of a test for artificial intelligence ethical awareness. J Korean Assoc Artif Intell Educ. 2021;2(1):1–19.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    The final analysis included 189 questionnaires, with a dropout rate of 3.1%, after excluding one insincere response, one abnormal Z-score, and four outliers.

    “We had 189 questionnaires in the final analysis, indicating a dropout rate of 3.1%; we excluded one questionnaire with insincere responses, one with an abnormal Z-score, and four with outliers in the regression analysis.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    The sample size was determined a priori using G*Power; the minimum sample size was 178 for multiple regression with effect size 0.15, alpha 0.05, power 0.95, and 11 predictors.

    “Based on the effect size of 0.15, significance level (⍺) of 0.05, statistical power (1-β) of 0.95, and 11 arbitrary predictors (six demographic characteristics, AI ethics awareness, two attitudes, anxiety, and self-efficacy), we calculated the minimum sample size as 178.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Nurses and nursing students are potential users of AI-based technology and are in a pivotal position to shape and lead the use of AI within the nursing field.

    “Nurses and nursing students are potential users of AI-based technology and are in a pivotal position to shape and lead the use of AI within the nursing field [14].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [14] McGrow K. Artificial intelligence: Essentials for nursing. Nurs. 2019;49(9):46–9. doi:https://doi.org/10.1097/01.NURSE.0000577716.57052.8d. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    More than 70% of nurses and nursing students did not understand AI in clinical practice.

    “However, a recent study reported that more than 70% of nurses and nursing students did not understand AI in clinical practice.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [15] 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.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    The study received IRB approval and ensured anonymity of responses.

    “We conducted this study after receiving the institutional review board’s approval (1041078-202112-HR-342-01). We informed all voluntary participants about the purpose of the research and ensured that all responses were anonymous.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Cronbach's alpha for negative attitude toward AI in this study was 0.76.

    “The Cronbach’s α of the positive and negative attitudes were 0.88 and 0.83, respectively, at the time of development [17], and 0.85 and 0.76, respectively, in this study.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] Schepman A, Rodway P. Initial validation of the general attitudes towards artificial intelligence scale. Comput Hum Behav Rep. 2020;1:100014. doi:https://doi.org/10.1016/j.chbr.2020.100014. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Fourth-year nursing students showed the most positive attitudes toward technology because they had more opportunities to participate in technology education.

    “A previous study reported that fourth-year nursing students showed the most positive attitudes toward technology, as they had already had more opportunities to participate in technology education [20].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [20] Tubaishat A. An investigation into the attitudes of nursing students toward technology. J Nurs Res. 2014;22(2):119–25. doi:https://doi.org/10.1097/jnr.0000000000000029. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Data were collected from nursing students at a single nursing college, and most participants had no previous AI education, limiting generalizability.

    “First, we collected data from nursing students at a single nursing college, and most participants did not have previous experience in AI education. Therefore, there are limitations in generalizing this study’s findings.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    The TAIEA tool initially only reported expert validity and reliability; many items were deleted for construct validation, and its internal reliability in this study was only 0.69.

    “Regarding the TAIEA tool used in this study, the researchers initially only reported expert validity and reliability. Accordingly, we deleted many items for construct validation in this study. Nevertheless, the internal reliability of the tool was only 0.69, indicating a need for the tool’s further revision and verification.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    A learner's positive attitude toward information technology reduced their anxiety about IT, promoted IT use or interactions, and improved confidence in problem-solving through IT.

    “For instance, Wagner and Sherwood [10] reported that a learner’s positive attitude toward information technology (IT) reduced their anxiety about IT, promoted IT use or interactions, and improved confidence in problem-solving through IT.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [10] Wagner RV, Sherwood JJ. The study of attitude change. Belmont: Brooks; 1969.

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Nursing students' positive attitudes toward AI and improvements in self-efficacy can increase their behavioral intentions toward using AI-based healthcare technology applications.

    “Nursing students’ positive attitudes toward AI and improvements in self-efficacy can increase their behavioral intentions toward using AI-based healthcare technology applications.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Introducing AI content in the nursing curriculum will improve nursing students' positive attitudes and self-efficacy toward AI technology within the clinical field.

    “In addition, introducing AI content in the nursing curriculum will improve nursing students’ positive attitudes and self-efficacy toward AI technology within the clinical field.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    Providing nursing students with education on AI technology's performance, usefulness, and social influence in health settings will lead to a positive attitude.

    “Providing nursing students with education on AI technology’s performance, usefulness, and social influence in health settings will thus lead to a positive attitude.”
    Quote in the paperNot a substantial claim
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

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Conclusion calls a nonsignificant behavioral-intention difference significant

    Major

    The conclusion says students with clinical training had significantly higher behavioral-intention scores. The results give the year-group comparison p = .053 and describe it only as close to significance.

  • AI ethics awareness reliability differs

    Minor

    The measurement section reports Cronbach's α of 0.67 in this study, while the discussion reports internal reliability of 0.69.

  • Data collection dates conflict

    Minor

    The abstract says data were collected in November-December 2021, whereas the methods give February 16-23, 2022.

  • Third- and fourth-year student count is inconsistent

    Minor

    Table 1 lists 48 third-year and 16 fourth-year students, totaling 64; Table 2 reports 66 in the combined group.

    Reported 66 · recomputed 64

  • Ethics-positive-attitude correlation differs from Table 3

    Minor

    The results text gives the correlation as 0.31, whereas Table 3 gives 0.52 for these variables.

  • AI education counts exceed the sample size

    Minor

    Table 1 lists 36 students with AI education experience and 157 without it, totaling 193 rather than the stated 189 participants. Table 2 instead lists 153 without experience.

    Reported 189 · recomputed 193

  • Positive-attitude correlation has the wrong sign in the text

    Minor

    The results describe the correlation between positive attitude toward AI and behavioral intention as −0.62, while Table 3 reports +0.62.

  • Positive-attitude standardized regression coefficient differs

    Minor

    The results report β = 0.58 for positive attitude, but Table 4 places 0.58 under the unstandardized B column and reports β = 0.49.

  • Self-efficacy-ethics correlation conflicts with Table 3

    Minor

    The text calls this correlation positive but gives it as −0.25; Table 3 instead reports +0.35.

9 of 9

Cites

Flagged first, then by citations

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.

Cited by

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Related

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
ORCID
Venue
BMC Nursing
Publisher
Springer Science and Business Media LLC
Published
2022-09-30
Volume
21
Issue
1
Pages
267-267
ISSN
1472-6955 (ISSN-L)
DOI
10.1186/s12912-022-01048-0
PMC
PMC9526272
OpenAlex
W4298147439
Semantic Scholar
3f50d28f31a8eb5df6554ad52c038d332542e60f
Cited by
291
References
24
Citations by year
2026: 102 · 2025: 108 · 2024: 65 · 2023: 15 · 2020: 1
FWCI
14.17
Type
journal article
Language
en
Open access
Licence
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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