From awareness to application: Unveiling the interplay between dependence on artificial intelligence and achievement motivation among nursing students: A multicenter study.
The findings indicate a significant negative association between AI dependence and achievement motivation, emphasizing the need to consider students' technology usage patterns concerning academic motivation.
Abstract
Background
Artificial intelligence (AI) has emerged as a transformative, fundamentally reshaping how we access and apply knowledge. Achievement motivation (AM) remains a critical component of students' academic and personal development. Examining the association between nursing students' dependence on AI and their AM is a pertinent area of research, particularly in light of the growing incorporation of AI within educational settings.
Aim
OF THE STUDY
To explore the association between undergraduate nursing students' dependence on artificial intelligence and achievement motivation.
Methods
A multicenter descriptive correlational study was conducted across five nursing faculties affiliated with Port-Said, Damietta, Alexandria, Mansoura, and Sohag universities, involving 1412 nursing students. Data were collected using a Personal and Academic Characteristics Questionnaire, the Achievement Motivation Scale (AMS), and the Dependence on Artificial Intelligence Scale (DIA). Data were analyzed using descriptive statistics, Pearson correlation, and simple linear regression.
Results
Nursing students demonstrated a moderate level of dependence on AI (M = 19.46, SD = 11.33) and a moderate level of AM (M = 6.32, SD = 2.13). A statistically significant moderate negative association was found between AI dependence and AM (r = -0.389, p < .001). This indicates that increased dependence on AI is associated with lower achievement motivation. Regression analysis indicated that AI dependence accounted for 19.6% of the variance in AM (R2 = 0.196), while the remaining variance is likely influenced by other unmeasured factors.
Conclusion
Nursing students exhibited moderate AI dependence and achievement motivation. The findings indicate a significant negative association between AI dependence and achievement motivation, emphasizing the need to consider students' technology usage patterns concerning academic motivation.
IMPLICATIONS FOR PRACTICE
Educational programs should promote informed and balanced use of AI by clarifying its benefits and limitations as a learning support tool. Learning strategies should also be formulated to foster independent thinking, self-regulation, and goal-directed behavior to support students' achievement motivation.
The findings indicate that nursing students had generally positive levels of AI literacy and attitudes toward AI, and higher AI literacy was associated with more positive attitudes toward AI.
M. Çil, Berna Eren Fidancı, D. Yildiz· Journal of Education and Res...· 0 citations
Initial evidence is provided that the NAIRS is a valid and reliable instrument for assessing nursing students' readiness for artificial intelligence across knowledge/awareness, willingness to use AI, self-efficacy, and ethical awareness domains and may be useful for educational needs assessment and curriculum planning in nursing education.
Sumeyye Akçoban, Gülay Koca, S. Berşe· BMC Nursing· 0 citations
Objective: As the growing use of artificial intelligence in healthcare creates both new opportunities and emerging concerns for nursing practice, this study aimed to examine nursing students’ attitudes toward artificial intelligence and to explore the extent to which AI-related anxiety and technological dependency predict these attitudes.Methods: A descriptive, cross-sectional, and correlational design was employed with a sample of 373 nursing students studying at a public university in Türkiye. The data were collected using three instruments: the General Attitudes Toward Artificial Intelligence Scale, the Artificial Intelligence Anxiety Scale, and the Artificial Intelligence Dependency Scale. The statistical analyses encompassed a range of methodologies, including descriptive measures, Pearson correlation coefficients, and multiple regression models.Results: The mean score for the General Attitudes toward Artificial Intelligence Scale Positive Attitudes subscale was 42.17 ± 8.71, while the mean score for the Negative Attitudes subscale was 24.44 ± 6.69. The mean total scores for the Artificial Intelligence Anxiety Scale and the Dependence on Artificial Intelligence Scale were 73.57 ± 25.54 and 12.39 ± 4.28, respectively. Multiple regression analyses revealed that AI-related anxiety and dependency significantly predicted positive attitude scores (R² = 0.060, F = 11.722, p < 0.001) and negative attitude scores (R² = 0.349, F = 98.985, p < 0.001).Conclusions: The findings underscore the notion that nursing students’ perceptions of artificial intelligence are shaped by a complex interplay of emotional, cognitive, and behavioral factors. In accordance with a holistic nursing framework, incorporating artificial intelligence into education should extend beyond the acquisition of technical skills to encompass the maintenance of core care values, such as empathy, ethical awareness, and effective communication. Educational approaches that encourage critical engagement with technology and its balanced use may facilitate the ethical and efficient integration of artificial intelligence into nursing practice.
Eren Sarıtaş, Pınar ÇİÇEKOĞLU ÖZTÜRK· Sakarya Üniversitesi Holisti...· 0 citations
Background: Artificial intelligence (AI) has emerged as a transformative technology in healthcare, offering significant opportunities to improve clinical decision-making, patient care, and healthcare efficiency. As AI applications continue to expand across healthcare settings, nursing students must develop adequate awareness and readiness to effectively utilize these technologies in future professional practice.
Aim: This study aimed to assess the level of awareness and readiness toward artificial intelligence among nursing students and to identify the factors associated with AI readiness.
Methods: A quantitative cross-sectional survey was conducted among 396 nursing students at Riyadh Elm University, Saudi Arabia. Data were collected using a structured questionnaire consisting of demographic characteristics, the Artificial Intelligence Awareness Scale, and the Artificial Intelligence Readiness Scale. Descriptive statistics, independent t-test, one-way ANOVA with Tukey HSD post hoc analysis, Pearson correlation, and multiple linear regression were performed using SPSS version 27. Statistical significance was set at p < 0.05.
Results: Nursing students demonstrated a moderate level of AI awareness (mean = 3.43 ± 0.44, 68.57%) and a high level of AI readiness (mean = 3.93 ± 0.45, 78.52%). Significant differences in AI awareness were observed according to academic level and self-rated computer skills. AI readiness was significantly associated with age, academic level, previous use of AI-related tools, and self-rated computer skills. A strong positive correlation was found between AI awareness and AI readiness (r = 0.685, p < 0.001). Multiple linear regression analysis identified AI awareness as the strongest independent predictor of AI readiness (B = 0.681, p < 0.001), with the model explaining 56.9% of the variance in AI readiness (R² = 0.569).
Conclusion: Nursing students demonstrated positive readiness toward artificial intelligence despite having only moderate awareness. Integrating AI-related education, practical training, and digital competency development into undergraduate nursing curricula may enhance students' awareness and strengthen their readiness to effectively utilize AI technologies in future nursing practice.
Saada Taktikh Alshammary, Fatimah Salman Zoghbi, D. Alanazi et al.· International Journal of Adv...· 0 citations
Findings show that nursing students who feel more prepared for AI tend to be less anxious about technology and add AI training more consistently throughout the nursing curriculum and building digital skills may help reduce fear and make it easier for students to use AI tools in the future.
For nursing students, emotional competence, the capacity to identify, comprehend, communicate, and control emotions, is essential because it promotes empathy, successful academic performance, and effective communication. It is also essential for professional growth and stress management in nursing education. There is, however, little data on the connection between Indian nursing undergraduates’ academic achievement and emotional intelligence. The purpose of this study was to evaluate the emotional intelligence of undergraduate nursing students at Yenepoya Nursing College in Mangaluru and its relationship to academic success.
94 B.Sc. nursing students who were chosen by non-probability convenience sampling participated in a descriptive quantitative design. The standardized Profile of Emotional Competence (PEC) tool, which assesses five domains: identification, comprehension, expression, regulation, and utilization of emotions, was used to collect data. Exam results from the first and second semesters were used to gauge academic performance. High reliability was shown by the PEC (Cronbach’s
α
= 0.929). Using IBM Statistical Package for the Social Sciences (IBM SPSS) Statistics for Windows (version 29.0.10), descriptive statistics, Pearson’s correlation, and chi-square tests were used to analyse the data.
Most participants were under 20 years old (51.1%) and female (81.9%). The utilization of one’s own emotions domain had the highest scores (15.48 ± 3.82), with the mean overall emotional competence score being 146.16. Of the group, 13.8% received a distinction and 67% received a first-class grade. A significant association (
p
<0.05) was found between emotional competence and students who passed both semesters, but no statistically significant correlation (
p
>0.05) was found between emotional competence and academic achievement.
Students with greater emotional competence performed better academically over time, even though there was no clear relationship between emotional competence and academic success. Better learning outcomes could be a result of increased emotional awareness and control. It is advised that nursing curricula incorporate emotional intelligence training to support students’ professional development, resilience, and adaptability.
G. R. Gireesh, Viji Prasad, Sneha V. Binu et al.· Journal of Health and Allied...· 0 citations
