* The study aims to investigate the effect of using artificial intelligence Chatbot education about electronic fetal monitoring on maternity nursing students' performance. * The aim will be achieved through the following, 1. Designing AI Chatbot about electronic fetal monitoring. 2. Exploring the effect of using AI Chatbot about electronic fetal monitoring on students' performance, interest in education, self-directed learning \& feedback satisfaction. * The students will be divided into two groups, the intervention group will use EFM Chatbot, and the control group will receive the traditional learning
Effect of Using Artificial Intelligence Chatbot about Electronic Fetal Monitoring on Maternity Nursing Students' Performance Rapid advances in information and technology have led to the advancement of interactive learning environments. In line with these changes, artificial intelligence (AI) has emerged as a primary area of interest. AI is the simulation of human intelligence processes by machines to maximize their chance to achieve certain goal, it has actively permeated many aspects of live. The integration of artificial intelligence (AI) into education and research has become more prevalent in recent years. With the evolution of medical technology, content of medical and nursing education has changed; thus, continuously enhancing medical student's knowledge and capabilities is a critical educational objective. The incorporation of AI into the education field has led to many possibilities, benefiting both educators and students. AI takes on various roles as an intelligent tutor, a learning partner, and even an adviser in influencing educational policies also, provide a focused, personalized, and result-oriented online learning environment. The current generation of nursing students, having been raised in an era of networking \& highly familiar with internet technology. As such, it is anticipated that their learning preferences may differ from previous generations. Thus, strategies for improving students' self-directed learning, and efforts for promoting interactions between instructors and students were needed. This has led to a growing interest in using AI powered technology. Among the various forms of AI, Chatbots represent one of the most commonly encountered AI-based tools in the education field The aims of nursing training include not only mastering skills but also fostering the competence to make decisions for problem solving. Regarding essential nursing techniques in midwifery health nursing, education on installing EFM equipment and interpreting its results is required. Electronic fetal monitoring (EFM) is a method to assess fetal health, utilized to prevent fetal hypoxia and provide interventions at an early stage by observing changes in fetal heartbeat. Since EFM-related tasks, require professional knowledge \& understanding, nursing students should be provided with sufficient learning and training in EFM prior to their training in the delivery room. With the aim of helping students to make correct decisions when dealing with real cases, it is necessary to engage them in authentic problem-solving contexts Traditional education system faces several issues, including overcrowded classrooms, high student teacher ratio, lack of personalized attention for students, varying learning paces and styles. Consequently, the lack of individualized student support leads to low satisfaction with learning and subsequent weak learning efficiency. Based on Egypt vision 2030 one of Challenges in the Health science Sector is mismatch of skills between higher education graduates and the needs of the health sector. Given that the priority agenda of the Egyptian government is reducing the high Maternal\& neonatal Mortality Rate (MMR) \& (NMR), a specific set of KPI has been selected to be used to monitor progress until 2030 as decrease in MMR and increase in life expectancy at birth to reach 31% \& 75% respectively. As the educational landscape continues to evolve, the rise of AI-powered chatbots emerges as a promising solution to enable students to learn and think deeply in the contexts of handling obstetrics. According to Egyptian National Council for Artificial Intelligence strategy, applying AI to areas such as education or healthcare can facilitate access, and reduce risks and costs. In this context, it is essential to assess the effects of Chatbot, which have the potential to be used in educational institutions that have the mission of shaping society and guiding the future of all stakeholders of education. Therefore, developing \& using Chatbot as educational method and evaluating its effects in the field of nursing education are needed, so this study will be conducted. * An approval will be obtained from the Faculty of Nursing Research Ethics Committee to carry out the study. * An official permission will be obtained by submission of an official letter to head of woman's' health and midwifery nursing department Faculty of Nursing, Mansoura University, and the Dean of the selected setting to conduct the study after explaining the aim, importance and benefits of the research of the study to gain their cooperation and support during data collection. * Students from level three will be recruited in the study \& divided into two groups, the intervention group will use EFM Chatbot, and the control group will receive the traditional learning. * The researcher will measure the effectiveness of EFM Chatbot to assess how well the maternity students were remembering the material and applying the instructions compare the results with the control group.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
OTHER
Masking
NONE
Enrollment
84
effect of using designed artificial intelligence Chatbot about electronic fetal monitoring on maternity nursing students' performance
the traditional learning method (online meeting).
Faculty of Nursing, Mansoura University
Al Mansurah, Dakahlia Governorate, Egypt
Maternity nursing students who received EFM Chatbot education will have better theoretical knowledge regarding EFM within 3 months.
Maternity Students' Knowledge regard EFM will be assessed using a test made by the researcher consist of 33 questions with a varying degree of difficulty about the core knowledge regrading EFM. Calculated scores will be assigned to the students' knowledge-related answers. Each correct response received a score of "one" \& every incorrect response received a score of "zero." The scores of the items for each area of knowledge will be added up, and the total was divided by the number of items, yielding a mean score for each area. Classification system for the knowledge level will be: * Good knowledge (80% or higher) * Average knowledge (60% to 79%) * poor knowledge (40% to 59%) * very poor knowledge (less than 40%).
Time frame: 3 months
Maternity nursing students who received EFM Chatbot education will have satisfactory practical interpretation skills regarding EFM within 3 months.
Maternity Students' Interpretation Competency regard EFM will be assessed by a test contain number of traces charts; each trace will contain questions intended to assess the respondent's understanding of it \& the ability to accurately interpret and analyze electronic signals generated by fetal cardiotocography machine (total 40 questions). Each accurate response received a score of one, while each wrong response received a score of zero. The scores of the items will be added up for each area of fetal trace interpretation, and the total will be divided by the number of items, yielding a mean score for each region. A percentage score will be created from these scores. A successful interpretation of the fetal trace will be considered satisfactory if the percent score was greater than 60%, as opposed to an unsatisfactory interpretation scoring
Time frame: 3 months.
Maternity nursing students who received EFM Chatbot education will have better clinical reasoning confidence regarding EFM within 3 months.
Maternity students' clinical reasoning confidence in fetal health assessment: will be measured with series of questions as ability to collect patient history, apply proper assessment skills \& identify abnormalities from collected patient information…. etc. Using a 5-point Likert scale with a response of "strongly confident" and "not confident at all" accounts for 5 and 1 points, respectively. The scores of the questions will be added up, and the total will be divided by the number of items, yielding a mean score. The score will be stratified as: 20% to less than 35% indicates beginning 35%-60% indicates developing 61%-85% indicates achieving above 86% indicates exemplary.
Time frame: 3 months.
Maternity nursing students who received EFM Chatbot education will have more interest in education than the control group within 3 months.
this outcome will be assessed by the academic motivation scale (AMS) which contains questions as (students experience pleasure while learning, students' satisfaction of accomplishment of difficult academic activities, students obtain more interesting information in Chatbot, independent learning, self-management…etc.). Questions scored on a 5-point Likert scale from 1 (does not correspond at all) to 5 (corresponds totally). The scores of the questions will be added up, and the total will be divided by the number of items. Score range from 14% to less than 35% indicates low motivation Score from 35% to 57% indicates average motivation Score above 57% indicates good motivation.
Time frame: 3 months
Maternity nursing students who received EFM Chatbot education will have higher feedback satisfaction.
Students' Feedback Satisfaction regard using EFM Chatbot: students will be asked questions related to acceptability and learnability of Chatbot \& the degree to which users feel fulfilled with the responses and assistance provided by the Chatbot in response to their queries as (Chatbot is an acceptable way to receive information, improves access to EFM resources \& covers information needs…. etc.). Questions will be scored on a 3-point Likert scale, the possible ratings for answers ranged from 1 (not satisfied) to 3 (satisfied). The scores of the items will be added up, and the total will be divided by the number of items. The score range 20% to 49% indicates low satisfaction Score from 50% to 69% indicates average satisfaction Score 70% or above indicates high satisfaction.
Time frame: 3 months
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