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临床试验/NCT07051343
NCT07051343进行中(未招募)不适用

Effect of Using Artificial Intelligence Chatbot About Electronic Fetal Monitoring on Maternity Nursing Students' Performance

Mansoura University1 个研究点 分布在 1 个国家目标入组 84 人开始时间: 2025年6月1日最近更新:
适应症
干预措施

试验速览

阶段
不适用
状态
进行中(未招募)
入组人数
84
试验地点
1
主要终点
Maternity nursing students who received EFM Chatbot education will have better theoretical knowledge regarding EFM within 3 months.

研究概览

简要总结

  • 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

详细描述

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.

研究设计

研究类型
Interventional
分配方式
Non Randomized
干预模型
Parallel
主要目的
Other
盲法
None

入排标准

性别
All
接受健康志愿者
是

入选标准

  • •third level students at faculty of nursing Mansoura university who will register midwifery course of academic year 2024/2025

排除标准

  • •students who refuse to participate in the study and those not registered in the course

研究组 & 干预措施

intervention group

Experimental

this group will receive the designed electronic fetal monitoring AI Chatbot education

干预措施: designed artificial intelligence Chatbot education about electronic fetal monitoring (Other)

control group

Other

this group includes students who will receive the traditional learning method (online meeting).

干预措施: traditional teaching method (Other)

结局指标

主要结局

Maternity nursing students who received EFM Chatbot education will have better theoretical knowledge regarding EFM within 3 months.

时间窗: 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%).

Maternity nursing students who received EFM Chatbot education will have satisfactory practical interpretation skills regarding EFM within 3 months.

时间窗: 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

Maternity nursing students who received EFM Chatbot education will have better clinical reasoning confidence regarding EFM within 3 months.

时间窗: 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.

次要结局

  • Maternity nursing students who received EFM Chatbot education will have higher feedback satisfaction.(3 months)
  • Maternity nursing students who received EFM Chatbot education will have more interest in education than the control group within 3 months.(3 months)

研究者

申办方类型
Other
责任方
Principal Investigator
主要研究者

Amal Mohamed Talaat AbdElwahab Hassan Ahmed Aboaish

assistant lecturer of woman's health and midwifery nursing department

Mansoura University

研究点 (1)

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