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 accessAbstract
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.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
16 Own result · 12 Not checked yet · 6 Own method · 4 Own interpretation
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Anxiety had a positive correlation with a negative attitude toward AI (r = .44).”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Correlations between AI Ethics Awareness, attitude toward AI, anxiety, Self-Efficacy, and behavioral intention
plaincorrelationaln = 189 ·- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There were statistically significant correlations among all measured variables.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Correlations between AI Ethics Awareness, attitude toward AI, anxiety, Self-Efficacy, and behavioral intention
plaincorrelationaln = 189 ·- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Differences in measured variables by demographic characteristics
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Differences in measured variables by demographic characteristics
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Factors Associated with the behavioral intention to use AI-based Healthcare Technologies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Differences in measured variables by demographic characteristics
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Factors Associated with the behavioral intention to use AI-based Healthcare Technologies
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, the behavioral intention score of the third and fourth-year nursing students was higher than in other years, and the difference in scores was close to the significance level (p = .053) (Table 2).”
- Also stated in: § Conclusion · plain
“However, students with practical clinical training showed significantly higher AI ethics, positive attitudes toward AI, self-efficacy, and behavioral intention scores while having significantly lower anxiety scores than first and second-year students.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Differences in measured variables by demographic characteristics
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Conclusion · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Differences in measured variables by demographic characteristics
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, AI ethics awareness had a positive correlation with a positive attitude toward AI (r = .31) (Table 3).”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Correlations between AI Ethics Awareness, attitude toward AI, anxiety, Self-Efficacy, and behavioral intention
plaincorrelationaln = 189 ·- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 12
- Probably not a substantial claim. 9
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Positive attitude toward AI (r = − .62) and self-efficacy (r = .51) showed the highest correlation with behavioral intention.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Correlations between AI Ethics Awareness, attitude toward AI, anxiety, Self-Efficacy, and behavioral intention
plaincorrelationaln = 189 ·- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The regression model was statistically significant (F = 28.38, p < .001) and had an explanatory power of 42.1%.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Factors Associated with the behavioral intention to use AI-based Healthcare Technologies
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Differences in measured variables by demographic characteristics
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Cronbach’s α of the tool was 0.81 at the time of development [6] and 0.67 in this study.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Measurements > AI Ethics Awareness
What the sources say
- Not checked yetcites [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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Method > Study participants
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Method > Study participants
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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].”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Background
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“However, a recent study reported that more than 70% of nurses and nursing students did not understand AI in clinical practice.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Background
What the sources say
- Not checked yetcites [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.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Measurements > Ethical considerations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Measurements > AI Ethics Awareness
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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].”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Discussion
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Background
What the sources say
- Not checked yetcites [10] Wagner RV, Sherwood JJ. The study of attitude change. Belmont: Brooks; 1969.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nursing students’ positive attitudes toward AI and improvements in self-efficacy can increase their behavioral intentions toward using AI-based healthcare technology applications.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Providing nursing students with education on AI technology’s performance, usefulness, and social influence in health settings will thus lead to a positive attitude.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Concerns include unawareness, mistrust, anxiety, worries about the patient relationship and job replacement [5, 8, 10, 13, 25–27].”
From Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany (2024) · cites this paper as [25]
Experimental — accuracy not yet measuredIn the citing paper
“The current study results about the conservative attitudes and concerns toward AI technology come in contrary to the results reported by Kwak et al., 31 as they examined the attitudes of student nurses toward AI technology and resulted in the positive willingness of the students to work with AI-integrated applications in nursing practice with ease.”
From Exploring nurses’ awareness and attitudes toward artificial intelligence: implications for nursing practice (2024) · cites this paper as [31]
In the citing paper
“Integrating AI is hindered by fears, missing sensitization, and a lack of data [8, 22, 23, 25, 27, 30].”
From Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany (2024) · cites this paper as [25]
In the citing paper
“As students and psychotherapists in training have limited to no experience interacting with AI-generated feedback, they may base their decision-making on intuitive, emotional reactions better represented by AI anxiety than trust in the tools [56].”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [56]
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.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Conclusion
“students with practical clinical training showed significantly higher AI ethics, positive attitudes toward AI, self-efficacy, and behavioral intention scores”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Differences in measured variables by demographic characteristics
“the difference in scores was close to the significance level (p = .053)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Measurements > AI Ethics Awareness
“Cronbach’s α of the tool was 0.81 at the time of development [6] and 0.67 in this study.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Discussion
“Nevertheless, the internal reliability of the tool was only 0.69”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Abstract > Methods
“with data collected from November to December 2021 using self-reported questionnaires”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Method > Study participants
“We collected data from February 16−23, 2022”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“| Education year | Year 3&4 | 66(33.9) |”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Differences in measured variables by demographic characteristics
Reported 66 · recomputed 64
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Correlations between AI Ethics Awareness, attitude toward AI, anxiety, Self-Efficacy, and behavioral intention
“AI ethics awareness had a positive correlation with a positive attitude toward AI (r = .31)”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Correlations between AI Ethics Awareness, attitude toward AI, anxiety, Self-Efficacy, and behavioral intention
“| 2 | 3.66 ± 0.47 | 0.52(< 0.001) | 1 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“| AI education experience | Yes | 36(19.0) | | AI education experience | No | 157(81.0) |”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > General characteristics of the participants
Reported 189 · recomputed 193
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Correlations between AI Ethics Awareness, attitude toward AI, anxiety, Self-Efficacy, and behavioral intention
“Positive attitude toward AI (r = − .62) and self-efficacy (r = .51) showed the highest correlation with behavioral intention.”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Correlations between AI Ethics Awareness, attitude toward AI, anxiety, Self-Efficacy, and behavioral intention
“| 6 | 3.77 ± 0.56 | 0.31(0.009) | 0.62(< 0.001) | −0.24(0.001) | −0.27(< 0.001) | 0.51(< 0.001) | 1 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Abstract > Results
“a positive attitude toward AI (β = 0.58) and self-efficacy (β = 0.22)”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Factors Associated with the behavioral intention to use AI-based Healthcare Technologies
“| Positive attitude toward AI | 0.58 | 0.09 | 0.49 | 6.77 | < 0.001 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Correlations between AI Ethics Awareness, attitude toward AI, anxiety, Self-Efficacy, and behavioral intention
“Self-efficacy positively correlated with AI ethics awareness (r = − .25)”
§ Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions > Results > Correlations between AI Ethics Awareness, attitude toward AI, anxiety, Self-Efficacy, and behavioral intention
“| 5 | 3.53 ± 0.55 | 0.35(0.001) | 0.54(< 0.001) | −0.31(< 0.001) | −0.34(< 0.001) | 1 |”
Citations
Cites
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579
Initial validation of the general attitudes towards Artificial Intelligence Scale
Schepman, A., & Rodway, P. (2020) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2020.100014 · Cited by 785
Medical students' attitude towards artificial intelligence: a multicentre survey
Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019) · European Radiology · doi:10.1007/s00330-018-5601-1 · Cited by 754
Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L., & Poon, D. S. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0830-7 · Cited by 507
Ronquillo, C. E., Peltonen, L., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., et al. (2021) · Journal of Advanced Nursing · doi:10.1111/jan.14855 · Cited by 453
Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2021) · JMIR Nursing · doi:10.2196/23933 · Cited by 326
Artificial intelligence: Essentials for nursing
McGrow, K. (2019) · Nursing · doi:10.1097/01.nurse.0000577716.57052.8d · Cited by 146
Alhasan, A., Audah, L., Ibrahim, I., Al-Sharaa, A., Al-Ogaili, A. S., & Mohammed, J. M. (2020) · International Journal of Pervasive Computing and Communications · doi:10.1108/ijpcc-10-2020-0175 · Cited by 69
Research works printed but not linked: 3
- 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.
- 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.
- 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
Sorted by citations
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100014 · Cited by 488
Artificial intelligence self-efficacy: Scale development and validation
Wang, Y., & Chuang, Y.-W. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12015-w · Cited by 435
Jia, X.-H., & Tu, J.-C. (2024) · Systems · doi:10.3390/systems12030074 · Cited by 218
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., Sabio, J. B., & de los Santos, J. A. (2023) · Nurse Education in Practice · doi:10.1016/j.nepr.2023.103815 · Cited by 196
Abou Hashish, E. A., & Alnajjar, H. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05482-3 · Cited by 185
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., & Sabio, J. B. (2023) · Nurse Education Today · doi:10.1016/j.nedt.2023.105945 · Cited by 145
Zhu, W., Huang, L., Zhou, X., Li, X., Shi, G., Ying, J., & Wang, C. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2323277 · Cited by 143
Chen, D., Liu, W., & Liu, X. (2024) · Acta Psychologica · doi:10.1016/j.actpsy.2024.104442 · Cited by 173
Lukić, A., Kudelić, N., Antičević, V., Mosler, E. L., Gluncić, V., Hren, D., & Lukić, I. K. (2023) · Nurse Education in Practice · doi:10.1016/j.nepr.2023.103735 · Cited by 129
Alruwaili, M. M., Abuadas, F. H., Alsadi, M., Alruwaili, A. N., Elsayed Ramadan, O. M., Shaban, M., Al Thobaity, A., Alkahtani, S. M., & El Arab, R. A. (2024) · DIGITAL HEALTH · doi:10.1177/20552076241271803 · Cited by 114
“…current study results about the conservative attitudes and concerns toward AI technology come in contrary to the results reported by Kwak et al., 31 as they examined the attitudes of student nurses toward AI technology and resulted in the positive willingness of the students to work with…”
ResultEl Arab, R. A., Alshakihs, A. H., Alabdulwahab, S. H., Almubarak, Y. S., Alkhalifah, S. S., Abdrbo, A., Hassanein, S., & Sagbakken, M. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1666005 · Cited by 113
El-Sayed, B. K. M., El‐Sayed, A. A. I., Alsenany, S. A., & Asal, M. G. R. (2024) · Nurse Education in Practice · doi:10.1016/j.nepr.2024.104208 · Cited by 136
Falebita, O. S., & Kok, P. J. (2024) · Journal for STEM Education Research · doi:10.1007/s41979-024-00132-1 · Cited by 154
Cai, L., Msafiri, M. M., & Kangwa, D. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13112-0 · Cited by 104
Chen, C., Hu, W., & Wei, X. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2024.2440877 · Cited by 116
Rony, M. K. K., Numan, S. M., Akter, K., Tushar, H., Debnath, M., Johra, F. T., Akter, F., Mondal, S., Das, M., Uddin, M. J., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36702 · Cited by 93
Dingel, J., Kleine, A., Cecil, J., Sigl, A., Lermer, E., & Gaube, S. (2024) · Journal of Medical Internet Research · doi:10.2196/57224 · Cited by 116
Sommer, D., Schmidbauer, L., & Wahl, F. (2024) · BMC Nursing · doi:10.1186/s12912-024-01884-2 · Cited by 91
Nursing Students’ Perceptions and Use of Generative Artificial Intelligence in Nursing Education
Han, S., Kang, H. S., Gimber, P., & Lim, S. (2025) · Nursing Reports · doi:10.3390/nursrep15020068 · Cited by 86
The effect of source disclosure on evaluation of AI-generated messages
Lim, S., & Schmälzle, R. (2024) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2024.100058 · Cited by 113
Personalized learning through AI: Pedagogical approaches and critical insights
Vorobyeva, K. I., Belous, S. V., Savchenko, N. V., Smirnova, L. M., Nikitina, S. A., & Zhdanov, S. P. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/16108 · Cited by 144
Subaşı, D. Ö., Sümengen, A. A., Semerci, R., Şimşek, E., Çakır, G. N., & Temizsoy, E. (2024) · Journal of Advanced Nursing · doi:10.1111/jan.16335 · Cited by 83
Omari, O. A., Alshammari, M., Jabri, W. A., Yahyaei, A. A., Aljohani, K. A., Sanad, H. M., Al‐Jubouri, M. B., Bashayreh, I., Fawaz, M. A., ALBashtawy, M., et al. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06452-5 · Cited by 84
Daniel, K., Msambwa, M. M., & Wen, Z. (2025) · European Journal of Education · doi:10.1111/ejed.70036 · Cited by 71
Bozkurt, S. A., Aydoğan, S., Dursun Ergezen, F., & Türkoğlu, A. (2025) · International Nursing Review · doi:10.1111/inr.70018 · Cited by 63
Atalla, A. D. G., El‐Ashry, A. M., & Mohamed, S. M. S. (2024) · BMC Nursing · doi:10.1186/s12912-024-02143-0 · Cited by 72
Atalla, A. D. G., El-Gawad Mousa, M. A., Hashish, E. A. A., Elseesy, N. A. M., Abd El kader Mohamed, A. I., & Sobhi Mohamed, S. M. (2025) · BMC Nursing · doi:10.1186/s12912-025-03306-3 · Cited by 58
Yalcinkaya, T., Ergin, E., & Yucel, S. C. (2024) · Teaching and Learning in Nursing · doi:10.1016/j.teln.2024.07.008 · Cited by 57
Influence of augmented reality on shopping behavior
Dogra, P., Kaushik, A. K., Kalia, P., & Kaushal, A. (2023) · Management Decision · doi:10.1108/md-02-2022-0136 · Cited by 75
Salem, G. M. M., El‐Gazar, H. E., Mahdy, A. Y., Alharbi, T. A. F., & Zoromba, M. A. (2024) · Journal of Nursing Management · doi:10.1155/2024/6992824 · Cited by 67
Artificial Intelligence in Nursing: New Opportunities and Challenges
Ramírez‐Baraldes, E., García‐Gutiérrez, D., & Salido, C. G. (2025) · European Journal of Education · doi:10.1111/ejed.70033 · Cited by 69
Arvai, N., Katonai, G., & Mesko, B. (2025) · Journal of Medical Internet Research · doi:10.2196/66986 · Cited by 57
Migdadi, M. K., Oweidat, I. A., Alosta, M. R., Al‐Mugheed, K., Alabdullah, A. A. S., & Abdelaliem, S. M. F. (2024) · Digital Health · doi:10.1177/20552076241301958 · Cited by 68
Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1293437 · Cited by 54
Abuadas, M. H., Albikawi, Z. F., & Rayani, A. M. (2025) · BMC Nursing · doi:10.1186/s12912-025-03458-2 · Cited by 72
Kleine, A.-K., Kokje, E., Lermer, E., & Gaube, S. (2023) · JMIR Human Factors · doi:10.2196/46859 · Cited by 49
Fan, J., & Zhang, Q. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37158 · Cited by 71
Wu, H., Liu, W., & Zeng, Y. (2024) · Acta Psychologica · doi:10.1016/j.actpsy.2024.104376 · Cited by 54
Wu, H., Li, D., & Mo, X. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13312-8 · Cited by 55
Ethical decision-making and artificial intelligence in nursing education: An integrative review
Şengül, T., Sarıköse, S., & Gül, A. (2025) · Nursing Ethics · doi:10.1177/09697330251366600 · Cited by 55
Diller, S. J., Stenzel, L.-C., & Passmore, J. (2024) · Human Resource Development International · doi:10.1080/13678868.2024.2375934 · Cited by 49
Dai, Q., Li, M., Yang, M., Shi, S., Wang, Z., Liao, J., Li, Z., Weinan, E., Tao, L., & Tang, Y. (2025) · Journal of Medical Internet Research · doi:10.2196/75343 · Cited by 58
Shi, S., & Zhang, H. (2025) · Learning and Motivation · doi:10.1016/j.lmot.2025.102151 · Cited by 41
Fan, J., Zhang, L., Li, N., & Man, S. (2025) · BMC Nursing · doi:10.1186/s12912-025-03265-9 · Cited by 50
Abubakar, S., Jeilani, A., & Yusuf, M. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2591503 · Cited by 86
Karaarslan, D., Kahraman, A., & Ergın, E. (2024) · Journal of Pediatric Nursing · doi:10.1016/j.pedn.2024.04.031 · Cited by 40
AI Image-Generation as a Teaching Strategy in Nursing Education
Reed, J., Alterio, B., Coblenz, H., O'Lear, T., & Metz, T. (2023) · Journal of Interactive Learning Research · doi:10.70725/729255gdiorw · Cited by 38
Wen, F., Li, Y., Zhou, Y., An, X., & Zou, Q. (2024) · Journal of Educational Technology Development and Exchange · doi:10.18785/jetde.1701.07 · Cited by 39
“Third, teachers need to educate students about the value of AI, which can positively influence students’ attitudes towards AI (Jatileni et al., 2023; Wang et al., 2023), thereby reducing their AI anxiety and promoting their use behavior (Kwak et al., 2022).”
BackgroundThe Mediating Effect of AI Trust on AI Self-Efficacy and Attitude Toward AI of College Students
Obenza, B. N., Baguio, J. S. I. E., Bardago, K. M. W., Granado, L. B., Loreco, K. C. A., Matugas, L. P., Talaboc, D. J., Zayas, R. K. D. D., Caballo, J. H. S., & Caangay, R. B. R. (2023) · International Journal of Metaverse · doi:10.54536/ijm.v2i1.2286 · Cited by 49
Khan, N. A. (2024) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-10-2023-0725 · Cited by 27
Zeng, Q., Huang, X., Zhu, J., Su, S., Hu, Y., & Zhang, X. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1622802 · Cited by 42
Si, J. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07766-8 · Cited by 46
Türk, N., Batuk, B., Kaya, A., & Yıldırım, O. (2025) · BMC Psychology · doi:10.1186/s40359-025-03559-2 · Cited by 38
Bai, S., Zhang, X., Yu, D., & Yao, J. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1449561 · Cited by 47
Liu, S., Xiao, Y., Nie, M., Yuan, X.-Q., Wang, L., Wang, M., & Wu, Z. (2025) · BMC Nursing · doi:10.1186/s12912-025-04142-1 · Cited by 43
Labrague, L. J., & Alharrasi, M. (2025) · Teaching and learning in nursing · doi:10.1016/j.teln.2025.02.032 · Cited by 48
Mohamed, M. G., Göktaş, P., Khalaf, S. A., Küçükkaya, A., Al‐Faouri, I., seleem, E. A. E. S., Ibraheem, A., Abdelhafez, A. M., Abdullah, S. O., Zaki, H. N., et al. (2025) · BMC Nursing · doi:10.1186/s12912-025-03436-8 · Cited by 46
Mekawy, S. H., Ismail, S. A. M., & Mohamed, M. Z. (2020) · Egyptian Journal of Health Care · doi:10.21608/ejhc.2020.274757 · Cited by 30
Maraş, G., Albayrak, E., & Sürme, Y. (2024) · Journal of Clinical Nursing · doi:10.1111/jocn.17562 · Cited by 36
Chun, Z., Ning, Y., Chen, J., & Wijaya, T. T. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13617-2 · Cited by 38
Kong, S. C., & Zhu, J. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100447 · Cited by 52
Khan, N. A., Maialeh, R., Akhtar, M., & Ramzan, M. (2024) · Public Organization Review · doi:10.1007/s11115-024-00770-4 · Cited by 31
Luo, C., Mao, B., Wu, Y., & He, Y. (2024) · Nurse Education Today · doi:10.1016/j.nedt.2024.106321 · Cited by 35
Trust and acceptance of AI caregiving robots: The role of ethics and self-efficacy
Lin, C. S., Kuo, Y.-F., & Wang, T.-Y. (2024) · Comput. Hum. Behav. Artif. Humans · doi:10.1016/j.chbah.2024.100115 · Cited by 37
Nursing Students’ Attitudes Toward Artificial Intelligence: Palestinian Perspectives
Salameh, B. S., Qaddumi, J. A. S., Hammad, B. M., Eqtit, F., Ayed, A., Fashafsheh, I., ALBashtawy, M., Reshia, F. A. A., & Lukić, I. K. (2025) · SAGE Open Nursing · doi:10.1177/23779608251343297 · Cited by 34
Luo, Y., Weng, H., Yang, L., Ding, Z., & Wang, Q. (2023) · JMIR Formative Research · doi:10.2196/50413 · Cited by 28
Trusting AI: does uncertainty visualization affect decision-making?
Reyes, J., Batmaz, A. U., & Kersten-Oertel, M. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1464348 · Cited by 27
Ethical Considerations and Equipoise in Cancer Surgery
Vakili-Ojarood, M., Naseri, A., Shirinzadeh-Dastgiri, A., Saberi, A., HaghighiKian, S. M., Rahmani, A., Farnoush, N., Nafissi, N., Heiranizadeh, N., Antikchi, M. H., et al. (2024) · Indian Journal of Surgical Oncology · doi:10.1007/s13193-024-02023-8 · Cited by 27
Ostick, M., Mariani, B. A., Lovecchio, C. P., & Moriarty, H. (2025) · Nursing Education Perspectives · doi:10.1097/01.nep.0000000000001372 · Cited by 30
Abuadas, M. H., & Albikawi, Z. F. (2025) · Journal of Human Behavior in the Social Environment · doi:10.1080/10911359.2025.2472852 · Cited by 34
Students Experience on Self-Study through AI
Namjoo, F., Liaghat, E., Shabaziasl, S., Modabernejad, Z., & Morshedi, H. (2023) · AI and Tech in Behavioral and Social Sciences · doi:10.61838/kman.aitech.1.3.6 · Cited by 31
Musyaffi, A. M., Adha, M. A., Mukhibad, H., & Oli, M. C. (2024) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2024.101168 · Cited by 43
A systematic review of AI anxiety in education
Wu, X., & Li, H. (2025) · AI and Ethics · doi:10.1007/s43681-025-00783-9 · Cited by 24
Nashwan, A. J., & Abujaber, A. A. (2024) · Evidence-Based Nursing · doi:10.1136/ebnurs-2023-103906 · Cited by 24
Yang, J., Xie, W., & Ni, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-21977-5 · Cited by 23
Kong, S. C., Zhu, J., & Yang, Y. N. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100359 · Cited by 28
Navigating the future: artificial intelligence's impact on transformational nurse leadership
Sullivan, D., Hall, V. P., & Morrison, J. (2024) · Teaching and learning in nursing · doi:10.1016/j.teln.2024.04.017 · Cited by 24
Hamad, N. I., El‐Ashry, A. M., Ibrahim, I. M., & Hassan, E. A. (2024) · Teaching and learning in nursing · doi:10.1016/j.teln.2024.12.017 · Cited by 25
Berdida, D. J. E., Grande, R. A. N., Serag, R. M., & Fathy, D. M. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104673 · Cited by 28
Generative Artificial Intelligence
Hawk, H., Coriasco, M., & Jones, J. R. (2024) · Nurse Educator · doi:10.1097/nne.0000000000001736 · Cited by 20
Abuadas, M. H., & Albikawi, Z. F. (2025) · Ethics & Behavior · doi:10.1080/10508422.2025.2511336 · Cited by 28
Al-Rawashdeh, M., Keikhosrokiani, P., Belaton, B., Alawida, M., & Zwiri, A. M. A. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.109012 · Cited by 20
Huang, T., & Wu, C. (2025) · Computers and Education Open · doi:10.1016/j.caeo.2025.100275 · Cited by 38
Artificial Intelligence Anxiety in Nursing Students
Varol, B. (2025) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001250 · Cited by 20
Alenazi, L., & Alhalal, E. (2025) · Nurse Education Today · doi:10.1016/j.nedt.2025.106780 · Cited by 18
Zhu, Y., & Siquan, X. (2025) · European Journal of Education · doi:10.1111/ejed.70160 · Cited by 23
Chai, T., & Sha, C. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105979 · Cited by 20
Şengül, T., Bilgiç, Ş., Macit, B., Sevim, F., Alik, S., & Kirkland‐Kyhn, H. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104432 · Cited by 19
Integration of artificial intelligence in nursing education: a cross-national exploration
Almalki, M., Sacgaca, L. P., Pangket, P. P., Pasay-an, E. A., Deep, S. H., Maskay, P. G. P., Benjamin, L. S., Hashim, S. M. A., Gonzales, A., Rosal, R., et al. (2025) · BMC Nursing · doi:10.1186/s12912-025-03689-3 · Cited by 15
Hua, J. H. (2023) · International Journal of Educational Management and Development Studies · doi:10.53378/353030 · Cited by 30
Dietrich, L. K., & Grassini, S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13658-7 · Cited by 14
Chan, M. S., Tan, C. H., Cheng, T. C. E., & Chen, T. B. (2025) · Industry and Higher Education · doi:10.1177/09504222251354885 · Cited by 21
The Effect of AI Education on Nursing Student AI Literacy and Anxiety
LaGore, A., Colby, L., Sinsabaugh, K., Grobbel, C. C., & Piscotty, R. (2025) · Journal of Nursing Education · doi:10.3928/01484834-20250415-04 · Cited by 16
Kong, S. C., & Yang, Y. P. (2024) · Research and Practice in Technology Enhanced Learning · doi:10.58459/rptel.2025.20024 · Cited by 22
Molu, B. (2024) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.14286 · Cited by 14
Maulana, A., Fenitra, R. M., Sutrisno, S., & Kurniawan, K. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100413 · Cited by 27
Student and Faculty Perceptions and Use of Generative Artificial Intelligence in Nursing Education
Blomquist, J., Alderden, J., Llewellyn, S., Haynes, M., & Dworak, E. (2025) · Journal of Nursing Education · doi:10.3928/01484834-20250312-06 · Cited by 13
Zhu, Y., Wen, F., Wu, J., Huang, P., & Zhang, Y. (2025) · Nurse Education Today · doi:10.1016/j.nedt.2025.106855 · Cited by 17
Luo, J., Peng, Q., Cao, H., Li, W., & Tan, S. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08388-w · Cited by 18
Şahin, M. G., & Yıldırım, Y. (2024) · International Journal of Assessment Tools in Education · doi:10.21449/ijate.1369023 · Cited by 21
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)
- PMC
- PMC9526272
- OpenAlex
- W4298147439
- Semantic Scholar
- 3f50d28f31a8eb5df6554ad52c038d332542e60f
- 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
- gold
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
