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?
In 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
“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]
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
Items 201–291 of 291
Malik, A., Butt, F. S., & Mahmood, K. (2026) · The Electronic Library · doi:10.1108/el-10-2025-0432 · Cited by 1
Fan, H.-Y., Zhou, Q., Deng, L.-L., Sun, L.-L., Tam, I. L., Wan, Z., Zhang, X.-L., Wang, M.-Q., Ho, H. K., Ng, C. H., et al. (2026) · BMC Nursing · doi:10.1186/s12912-026-04970-9 · Cited by 1
Garcia, P. R. B., Alhejaili, A. A., Muslihi, A., Alshahrani, B., Lamphon, H. T., Alhefnawy, K. A. H., Saleh, W., Natividad, M. J. B., Aljohani, M., & Fadlalmola, H. A. (2026) · International Journal of Nursing Studies Advances · doi:10.1016/j.ijnsa.2026.100636 · Cited by 1
Zeng, Q., Zhang, S., Hu, J., Wu, Y., Yang, M., & Hu, Y. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1918134 · Cited by 1
القضايا الأمنية والأخلاقية لاستخدام الذكاء الاصطناعي وعلاقتها بالاتجاه نحو تطبيقاته لدى طلاب الجامعة
Elfiel, H. (2024) · ARAB JOURNAL FOR SECURITY STUDIES · doi:10.26735/zubh6282 · Cited by 1
AI-Powered Emotional Intelligence in Nursing
da Silva, T. M. H. R. (2024) · Advances in human and social aspects of technology book series · doi:10.4018/979-8-3693-6190-0.ch009 · Cited by 1
Factors Affecting University Students’ Behavioral Intention to Use Teaching Application
Wang, S., & Li, C. (2025) · International Journal of Sociologies and Anthropologies Science Reviews · doi:10.60027/ijsasr.2025.5254 · Cited by 1
Kleine, A., Kokje, E., Gaube, S., & Lermer, E. (2023) · doi:10.31234/osf.io/c8yr3 · Cited by 1
Hawks, M., McCauley, L., Simpson, R. L., Arriaga, R. I., & Kaligotla, L. M. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104591 · Cited by 1
Zhang, J., Shi, Y., Ma, J., Luo, C., Song, M., Xu, X., Xu, L., & Shang, L. (2025) · Research Square · doi:10.21203/rs.3.rs-8128531/v1 · Cited by 1
Musyaffi, A. M., Adha, M. A., Mukhibad, H., & Oli, M. C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4885498
To Explore Nurses' Attitudes Towards Artificial Intelligence Technology: A Scoping ReviewPreprint
Yang, C. Q., He, Y., Li, P., Luo, C., & Yuan, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5135604
Kim, Y., Ahn, S.-Y., & Kim, J.-Y. (2025) · Journal of information and communication convergence engineering · doi:10.56977/jicce.2025.23.1.48
Wijaya, G., Taslim, M. C., Yosevina, C., & Amelia, N. (2025) · Kajian Branding Indonesia · doi:10.21632/kbi.7.1.9-21
Embracing a Thoughtful Integration of Artificial Intelligence Into Nursing Education
Phillips, B. E. (2025) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001315
Examining of factors influencing AI acceptance: self-efficacy, trust and hindrance Appraisal
Mao, Y., & Liu, Y. (2024) · International Conference on Information Education and Artificial Intelligence (ICIEAI) · doi:10.1145/3724504.3724630
Standards and policies adoption for AI-based healthcare systems
Javeed, D., Shoukat, S., & Qureshi, K. N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26476-4.00010-1
HEMŞİRELİK ÖĞRENCİLERİNİN YAPAY ZEKAYA YÖNELİK GENEL TUTUMLARI VE ETKİLEYEN FAKTÖRLER
Yazıcı, H. G., Ökten, Ç., & Akdulum, G. H. (2025) · Sağlık Bilimleri Dergisi · doi:10.34108/eujhs.1541870
刘, 骞. (2025) · Advances in Education · doi:10.12677/ae.2025.1571377
Hu, E., & Sallam, M. H. (2025) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste64638.2025.11091933
Artificial Intelligence in UK Medical Education: A Framework for Curriculum Reform (Preprint)Preprint
Gaur, A., Shah, M. H., Rao, M. S., Tan, J. K., Fuad, M., Bhatti, T., Bharadwaj, H. R., & Ahmed, K. A. H. M. (2025) · doi:10.2196/preprints.81953
Tasijawa, F. A., Addiarto, W., Hartono, D., Munir, Z., & Pandin, M. G. R. (2025) · Research Square · doi:10.21203/rs.3.rs-8033889/v1
Liu, L., Xiong, M., Hong, F., Zhang, Y., & Zhang, C. (2025) · PLoS ONE · doi:10.1371/journal.pone.0336925
Aydınlı, A., Uslusoy, E. Ç., & Uzdıl, N. (2025) · Teaching and learning in nursing · doi:10.1016/j.teln.2025.11.026
Sommer, D., Schmidbauer, L., & Wahl, F. (2024) · Figshare · doi:10.6084/m9.figshare.26698520
Hashish, E. A. A., & Alnajjar, H. A. (2024) · Figshare · doi:10.6084/m9.figshare.26717385
Bridging the Ai Gap: Unveiling Intensive Care Unit Nurses' Attitudes in Chinese Tertiary HospitalsPreprint
Liu, Y., Chang, Y., Yang, S., Sun, X., & Chen, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5392179
Fundamentals of artificial intelligence for nursing students: Educational innovation
Lukić, A., & Lukić, I. K. (2025) · Teaching and learning in nursing · doi:10.1016/j.teln.2025.08.033
Artificial Intelligence Among Palestinian Nursing Students: Attitude and Awareness
Aqqad, S. A. (2025) · 2025 International Conference on Smart Learning Courses (SCME) · doi:10.1109/scme62582.2025.11104875
Dawood, B. M. A. E., Abouelfettoh, A. M., Zewiel, M. A. A., Perumal, L., Alibrahim, L., Boayesha, A., Alaklabi, H., Alrashed, A., Alsuwaylih, A., Alashoor, E., et al. (2025) · Nursing Forum · doi:10.1155/nuf/2848944
Artificial Intelligence Experiences of Nurse Educators
Aydoğan, S., Ergezen, F. D., & Bozkurt, S. A. (2026) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001457
AI-Driven Instruction and Its Influence on EFL Learners' Self-efficacy, Self-regulation, and GritPreprint
Li, L., & Wang, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-8282837/v1
Mensah, E. G., Amutenya, T., Nyamekye, E., Ampem, I. O., & Sarfo, F. O. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00805-9 · Cited by 1
Avinash, M. N., & Muthu, P. (2026) · doi:10.2196/preprints.92212
Reliability generalization of the artificial intelligence anxiety scale
Yıldırım, Y., GÜNDÜZ, T., & Şahin, M. G. (2026) · Current Psychology · doi:10.1007/s12144-025-08736-5 · Cited by 1
Hemşirelerde Yapay Zekâ Kaygısı: Teknolojik Gelişmeler Bağlamında Tanımlayıcı Bir Araştırma
AYDIN, E., & Kahraman, H. A. (2026) · Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi · doi:10.37989/gumussagbil.1733188 · Cited by 1
Sarı, B. (2026) · Anadolu Üniversitesi Sosyal Bilimler Dergisi · doi:10.18037/ausbd.1735626
Ahmed, A. K., Chandio, I. A., Khan, I. M., Kumar, P. R., Bahadur, R., & Malhi, S. D. (2026) · NURSEARCHER (Journal of Nursing & Midwifery Sciences) · doi:10.54393/nrs.v6i1.225
Khandelwal, R., & Kapoor, D. (2026) · Asian Education and Development Studies · doi:10.1108/aeds-06-2025-0269
Golahi, G., Antonio, F., & Annisa, D. N. (2026) · JMIR Medical Education · doi:10.2196/preprints.100828
Agentic AI and the Algorithm of Good
Gounaris, A., & Kosteletos, G. (2026) · AI · doi:10.3390/ai7090352
Alhumaidi, B. N., & Alharbi, T. A. F. (2026) · Healthcare · doi:10.3390/healthcare14101407 · Cited by 2
He, X., Liu, J., Li, S., Lou, Y., Chen, L., Zhan, Y., Zhou, K., Liu, H. F., Wang, L., & Cha, R. (2026) · Research Square · doi:10.21203/rs.3.rs-9626863/v1
Albikawi, Z. F., & Abuadas, M. H. (2026) · BMC Nursing · doi:10.1186/s12912-026-04815-5 · Cited by 1
Basu, B. K., Sani, M. S. A., Salehin, M., Amin, M. B., Azad, M. M., Sony, M. M. A. A. M., & Bács, Z. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.103008
Zhang, H., Hu, Q., Yu, S., Shi, H., Xia, Z., & Meng, F. (2026) · BMC Nursing · doi:10.1186/s12912-026-04853-z
IŞIK, C., & BENLİ, T. E. (2026) · Acibadem Universitesi Saglik Bilimleri Dergisi · doi:10.31067/acusaglik.1933796
Saeedi¹, P., Abdi, N., Pendar², R., Malekzadeh, M., Moghadam², M. B., & Mirzaee, M. S. (2026) · Research Square · doi:10.21203/rs.3.rs-9895083/v1
Aydın, F., Dadandı, İ., Çıtak, Ş., Saki, V., & DADANDI, P. U. (2026) · BMC Psychology · doi:10.1186/s40359-026-05014-2
van Treek, A., Verhagen, S. J. W., Lechner, L., Ruysseveldt, J. V., & Spronken, M. (2026) · doi:10.2196/preprints.105085
Masot, O., Alzuria‐Alós, R. M., Roca, J., Torné‐Ruiz, A., Bonet, A., Selva‐Pareja, L., Gimeno, E. B., & Pueyo, E. P. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107263
Younis, M. B., Ayed, A., Alqudah, H., Alkhalaileh, M. A., Habahbeh, A., Khaled, M. H. B., & Abu-Shennar, J. A. (2026) · The Open Nursing Journal · doi:10.2174/0118744346487793260629120139
Managerial attitudes toward AI: evaluating transparency, sense of control and fairness perceptions
Falkenreck, C., Leszczyński, G., & Gaczek, P. (2026) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-07-2025-0698
Betkus, M. H., Banner, D., Currie, L. M., Jackson, P. J., & Freeman, S. (2026) · Canadian Journal of Nursing Research · doi:10.1177/08445621261463882
Kim, C., & Kim, Y.-M. (2026) · Journal of Digital Contents Society · doi:10.9728/dcs.2026.27.6.1585
Torreno, F., & FLORES, F. (2026) · Research Square · doi:10.21203/rs.3.rs-10357554/v1
Zhao, K., Li, L., Chen, W., & Liu, H. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2701958
Miyake, K., Ozaki, K., Maehigashi, A., & Yamada, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-25032-2_28
Sheikhzakaryaee, N., Shali, M., & Saveh, I. B. (2026) · BMC Nursing · doi:10.1186/s12912-026-05041-9
Karimtabar, H., Alee, M., & Oghli, S. H. (2026) · BMC Nursing · doi:10.1186/s12912-026-05053-5
GLOBAL RESEARCH TRENDS IN ARTIFICIAL INTELLIGENCE IN NURSING EDUCATION
Jelemie, C. S., Baddiri, B., Ahmad, N., Sator, P., Kassim, S. A., Majin, R., & Johari, L. B. (2026) · International Journal of Modern Education · doi:10.35631/ijmoe.830032
Berdida, D. J. E., Alhudaib, N. A. A., Grande, R. A. N., Alsharafi, S. A., Agunod, C. D., Santos, A. M., Alanazi, H. H., Al‐Moteri, M., Alqarawi, N., & Alasqah, I. (2026) · Journal of Nursing Management · doi:10.1155/jonm/5338801
Shen, L., Zh, W., T’ng, S. T., & Ho, K. H. (2026) · SAGE Open · doi:10.1177/21582440261462976
Şahin, R. S., Kudubeş, A. A., Özbay, S. Ç., & Sarıkahya, S. D. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107313
Luo, X., Zhang, C., Xia, J., Li, F., Ao, Q., & Xu, P. (2026) · BMC Nursing · doi:10.1186/s12912-026-05122-9
Are Artificial Intelligence and Robot Nurses a Threat to Student Nurses?: A Quasi‐Experimental Study
Ergın, E., Yildiz, N., & İncekara, İ. (2026) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.70540
Černeka, D., & Žvanut, B. (2026) · Pielegniarstwo XXI wieku / Nursing in the 21st Century · doi:10.12923/pielxxiw-2026-0034
Tan, S. F., Chan, J. I. L., Chew, C. M., & Eak, A. D. (2026) · Discover Education · doi:10.1007/s44217-026-01979-9
Factors Influencing Attitude Toward Artificial Intelligence Among Nursing Students
Kim, H. (2026) · Journal of Digital Contents Society · doi:10.9728/dcs.2026.27.7.2011
Facilitators and Fears: What Influences Students′ Continued Use of AI in Learning Systems?
Al-Maroof, R. S., Tawafak, R. M., Al-Rahmi, W. M., & Alyoussef, I. Y. (2026) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/8512492
Zeng, Y., Izhar, N. A., & Zeng, D. (2026) · Discover Education · doi:10.1007/s44217-026-01994-w
Sun, X., Gao, Y., Zhou, D., Wang, Z., & Ji, H. (2026) · Research Square · doi:10.21203/rs.3.rs-10281161/v1 · Cited by 1
Ağmaz, R. F., Ergüleç, F., & Küçükaydın, M. A. (2026) · Quality & Quantity · doi:10.1007/s11135-026-03050-z
Gong, Z., Zhao, Y., Wang, Y., Zhang, T., Wang, S., & Meng, Z. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-92-1030-5_3
Effectiveness of AI-Assisted Orthopedic Nursing Education: A Mixed-Methods Randomized ControlledPreprint
Yao, Z., Lin, G., Li, Y., He, S., Chen, Y., Pan, J., Dong, G., Ouyang, H., Li, Y., Qin, F., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10274926/v1
AI Dependency and Brain Rot Among Nursing Students: A Mixed-Methods Study
Sağlam, R., & Kalanlar, B. (2026) · AJN American Journal of Nursing · doi:10.1097/ajn.0000000000000364
Mejía-Trejo, J. (2026) · Journal of Further and Higher Education · doi:10.1080/0309877x.2026.2720475
Responsible AI use in faith-based educational institutions in Southeast Asia
Koroitubuna, J. N. (2026) · doi:10.63201/cqia2586
Sarıtaş, E., & Öztürk, P. Ç. (2026) · Sakarya Üniversitesi Holistik Sağlık Dergisi · doi:10.54803/sauhsd.1924219
Li, H., Zhang, Z., Yang, B., Liu, C., Sun, J., Zhou, J., & Cao, X. (2026) · BMC Nursing · doi:10.1186/s12912-026-05275-7
Hijazi, H., Alsadi, M., Nashwan, A., & Alzaatreh, M. (2026) · Advances in Nursing Science · doi:10.1097/ans.0000000000000693
Alslaibi, N. A., Qawasmeh, R., Samara, H., Hussein, A., & Hussein, W. N. (2026) · Discover Global Society · doi:10.1007/s44282-026-00592-3
Yao, S., Ng, W. C., Teh, W. C., Cheng, W. F., & Soong, Y. Q. (2026) · Property Management · doi:10.1108/pm-09-2025-0131
Jallad, S. T., Kassabry, M. F., & Takruri, A. T. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001737
Yang, B., Hu, J., & Zeng, Q. (2026) · Journal of Nursing Management · doi:10.1155/jonm/8080856
Nasser, A. A., Alasiri, A., Zagzoog, G., Bokhari, R., Alharbi, R., & Mansury, N. (2026) · MedEdPublish · doi:10.12688/mep.21828.1
Alnomasy, N., Pasay-an, E. A., Mostoles, R., Pangket, P. P., Alrashedi, H., Hobensack, M., Vyas, P., Uy, M. M., Sacgaca, L. P., Gonzales, A., et al. (2026) · Nursing Forum · doi:10.1155/nuf/7709522
Labib, W. A., Abowardah, E. S., Abdelsattar, A., & Abdelalim, M. F. (2026) · Lecture notes in civil engineering · doi:10.1007/978-981-92-2006-9_16
Cohort-level growth modeling of ethical engagement with artificial intelligence in education
Ayanwale, M. A., Omeh, C. B., Mnguni, L. E., & Oyeniran, F. M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02298-6
Artificial Intelligence Supported Teaching Approaches in Nursing Education: A Scoping Review.
Güngör, D., & Orgun, F. (2026) · The Journal of Nursery Education · doi:10.3928/01484834-20260827-04
Yılmaz, D. U., & Yılmaz, D. (2026) · Health Sciences Quarterly · doi:10.26900/hsq.3199
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
