Trial reportBMC medical education2025
Effect of using artificial intelligence chatbot about electronic fetal monitoring on maternity nursing students' performance.
Trial report in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07051343 (Effect of Using Artificial Intelligence Chatbot About Electronic Fetal Monitoring on Maternity Nursing Students' Performance), which is not on this map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Effect of Using Artificial Intelligence Chatbot About Electronic Fetal Monitoring on Maternity Nursing Students' Performance
Who cites it
2 citing papers in PubMed.
- The association of artificial intelligence use on academic stress and academic achievement among nursing students in palestine.BMC nursing · 2026Article
- The psychological mechanisms and behavioral determinants of academic integrity in the age of artificial intelligence.Frontiers in psychology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe integration of artificial intelligence chatbots in education has led to numerous possibilities, providing a focused, personalized, and result-oriented learning environment that enhances students’ cognitive and interpretive skills. Electronic fetal monitoring-related tasks require professional knowledge and clinical insight; with the aim of supporting students in making accurate decisions when dealing with real cases, it is necessary to engage them in authentic problem-solving contexts. AIM OF THE STUDY: This study aims to evaluate the effectiveness of an electronic fetal monitoring (EFM) chatbot in improving maternity nursing students’ theoretical knowledge, practical interpretation skills, confidence in clinical reasoning, academic motivation, and satisfaction with feedback.
methodsA quasi-experimental design was conducted among 84 undergraduate nursing students at the Faculty of Nursing, Mansoura University, Egypt, allocated into an intervention group (n = 42), received artificial intelligence chatbot–based education, and a control group (n = 42), received traditional teaching method. Data were collected using: (1) a self-administered questionnaire to assess students’ knowledge, interpretation skills, and clinical reasoning confidence regarding electronic fetal monitoring; (2) the Academic Motivation Scale; and (3) a students’ satisfaction survey. Data were analyzed with SPSS v22.0. Normality and homogeneity of variance were assessed with the Shapiro–Wilk and Levene’s tests, respectively. Continuous variables were reported as means and standard deviations, and categorical variables as numbers and percentages. Chi-square, Fisher’s exact, and paired t-tests were employed for group comparisons as appropriate. Effect sizes were estimated using Cohen’s d and Eta squared (η²). Statistical significance was set at p < 0.05.
resultsCompared with the control group, the current research revealed statistically significant improvements in students’ knowledge, practical interpretation, and critical reasoning regarding fetal monitoring, as well as academic motivation among students who received chatbot education at the post and follow-up tests (p < 0.001). In addition, 90.5% of the participants in the intervention group reported high levels of satisfaction with the chatbot.
conclusionChatbot education significantly improved students’ theoretical knowledge, practical interpretation skills, confidence in clinical reasoning, and academic motivation, with sustained gains observed at both the post and follow-up tests. Additionally, students reported high satisfaction with the feedback provided by the chatbot.
trial registrationClinicalTrials.gov, NCT07051343, registered on June 6, 2025.
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