Nurse education today · 2026
AIlessphobia, AI-supported self-regulated learning, and academic self-efficacy in nursing students: A structural equation modeling approach.
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Semerci Şahin Remziye, Akdeniz Kudubeş Aslı, Çınar Özbay Sevil, Durmuş Sarıkahya Selma
Аннотация
BACKGROUND: Artificial intelligence (AI) technologies are increasingly integrated into higher education and influence students' learning behaviors. Although AI tools can support learning processes, excessive reliance on them may lead to AIlessphobia, defined as anxiety about completing academic tasks without AI support. Understanding how this emerging psychological factor relates to students' self-regulated learning and academic self-efficacy is particularly important in nursing education.
AIM: This study aimed to examine the relationships between AIlessphobia, AI-enhanced self-regulated learning, and academic self-efficacy among undergraduate nursing students.
DESIGN: A quantitative, descriptive, cross-sectional correlational design was employed.
SETTING: The study was conducted in two universities in Türkiye.
PARTICIPANTS: A total of 503 undergraduate nursing students participated in the study.
METHODS: Data were collected using a Demographic Information Form, the AIlessphobia in Education Scale (AILPES), the AI-Enhanced Self-Regulated Learning Scale (AI-SRL), and the Academic Nurses' Self-Efficacy Scale (ANSEs). Descriptive statistics, Pearson correlation analysis, confirmatory factor analysis, and structural equation modeling were conducted using SPSS and AMOS to test the hypothesized relationships among the variables.
RESULTS: Participants had a mean age of 20.88 ± 2.61 years, and 66.20% were female. Structural equation modeling indicated that AIlessphobia had a statistically significant but practically negligible negative effect on academic self-efficacy (β = -0.049, f2 = 0.002), accounting for less than 1% of its variance. AIlessphobia did not significantly predict AI-enhanced self-regulated learning (β = 0.046, p = .089), and AI-enhanced self-regulated learning did not significantly influence academic self-efficacy (β = -0.008, p = .729). Model fit indices indicated an acceptable, although not ideal, model fit (χ2/df = 3.899, RMSEA = 0.076, CFI = 0.936, TLI = 0.916).
CONCLUSION: The findings suggest that AIlessphobia may be modestly associated with lower academic self-efficacy among nursing students; however, this relationship appears to have limited practical impact. AI-enhanced self-regulated learning did not mediate this relationship. These findings highlight the importance of promoting balanced AI use and supporting students' independent learning skills in nursing education.
AIM: This study aimed to examine the relationships between AIlessphobia, AI-enhanced self-regulated learning, and academic self-efficacy among undergraduate nursing students.
DESIGN: A quantitative, descriptive, cross-sectional correlational design was employed.
SETTING: The study was conducted in two universities in Türkiye.
PARTICIPANTS: A total of 503 undergraduate nursing students participated in the study.
METHODS: Data were collected using a Demographic Information Form, the AIlessphobia in Education Scale (AILPES), the AI-Enhanced Self-Regulated Learning Scale (AI-SRL), and the Academic Nurses' Self-Efficacy Scale (ANSEs). Descriptive statistics, Pearson correlation analysis, confirmatory factor analysis, and structural equation modeling were conducted using SPSS and AMOS to test the hypothesized relationships among the variables.
RESULTS: Participants had a mean age of 20.88 ± 2.61 years, and 66.20% were female. Structural equation modeling indicated that AIlessphobia had a statistically significant but practically negligible negative effect on academic self-efficacy (β = -0.049, f2 = 0.002), accounting for less than 1% of its variance. AIlessphobia did not significantly predict AI-enhanced self-regulated learning (β = 0.046, p = .089), and AI-enhanced self-regulated learning did not significantly influence academic self-efficacy (β = -0.008, p = .729). Model fit indices indicated an acceptable, although not ideal, model fit (χ2/df = 3.899, RMSEA = 0.076, CFI = 0.936, TLI = 0.916).
CONCLUSION: The findings suggest that AIlessphobia may be modestly associated with lower academic self-efficacy among nursing students; however, this relationship appears to have limited practical impact. AI-enhanced self-regulated learning did not mediate this relationship. These findings highlight the importance of promoting balanced AI use and supporting students' independent learning skills in nursing education.