Effect of using artificial intelligence chatbot about electronic fetal monitoring on maternity nursing students’ performance

TL;DR

An AI chatbot improved maternity nursing students' knowledge, skills, and motivation in electronic fetal monitoring compared to traditional teaching.

Contribution

Demonstrates the effectiveness of AI chatbots in enhancing clinical education for nursing students.

Findings

Chatbot education significantly improved students' knowledge and interpretation skills in fetal monitoring.

Students in the chatbot group showed higher academic motivation and satisfaction with feedback.

Improvements were sustained at follow-up tests, indicating long-term benefits.

Abstract

The 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.

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.

A 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.

Compared 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.

Chatbot 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.

ClinicalTrials.gov, NCT07051343, registered on June 6, 2025.

The online version contains supplementary material available at 10.1186/s12909-025-08391-1.

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