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AI-SUPPORTED FLIPPED LEARNING IN NURSING EDUCATION

2026年7月12日 更新者:Neslihan Lok、Selcuk University

THE EFFECT OF ARTIFICIAL INTELLIGENCE-SUPPORTED FLIPPED LEARNING-BASED PROFESSIONAL COMMUNICATION SKILLS TRAINING ON NURSING STUDENTS' LEARNING MOTIVATION AND SELF-DIRECTED LEARNING: A RANDOMIZED CONTROLLED TRIAL

The increasing complexity of healthcare services and the diversification of patient needs require nurses to be equipped not only with clinical knowledge and technical skills, but also with effective communication, critical thinking, and self-directed learning competencies. Nurses continuously interact with multidisciplinary teams throughout the processes of planning, implementing, and evaluating patient care. Therefore, communication skills are among the fundamental determinants of patient safety and quality of care. However, traditional educational methods are largely based on passive learning and may be insufficient in developing students' professional communication and self-directed learning skills. This limitation can reduce students' learning motivation and negatively affect their ability to make independent decisions and communicate effectively in clinical practice.

Digital technologies and artificial intelligence (AI)-supported applications provide opportunities to strengthen student-centered approaches in education. AI-supported systems offer personalized feedback, enabling targeted support according to students' individual learning needs. The flipped learning approach, on the other hand, is based on acquiring theoretical knowledge before class, while class time is devoted to practice, discussion, and problem-solving activities. This approach enhances students' active participation and supports the development of critical thinking and communication skills.

AI-supported flipped learning combines technological opportunities with pedagogical strategies to create a more interactive and personalized learning experience. This method encourages students to take responsibility for their own learning and strengthens their self-directed learning skills. Nevertheless, studies examining the effects of this approach on learning motivation and self-directed learning in nursing education remain limited. Therefore, this study aims to evaluate the effects of professional communication skills training based on an AI-supported flipped learning approach on nursing students' learning motivation and self-directed learning levels.

研究概览

详细说明

The growing complexity of healthcare systems and the increasing diversity of patient needs require nurses to possess a broad range of competencies beyond clinical knowledge and technical expertise. Effective communication, critical thinking, and self-directed learning have become essential skills for nursing professionals in contemporary healthcare settings. Throughout the planning, implementation, and evaluation of patient care, nurses collaborate continuously with multidisciplinary healthcare teams. In this context, communication competence plays a pivotal role in ensuring patient safety, facilitating teamwork, and improving the quality of care. Effective communication extends beyond the exchange of information; it also involves understanding patients' emotional concerns, engaging family members in care processes, and maintaining professional collaboration among healthcare providers. Despite the importance of these competencies, traditional educational approaches often rely on instructor-centered teaching methods that encourage passive learning. Such approaches may provide limited opportunities for students to actively develop communication abilities, problem-solving skills, and independent learning behaviors. Consequently, nursing students may experience lower learning motivation and encounter challenges in making autonomous clinical decisions and communicating effectively in professional practice. Furthermore, rapid technological advancements and evolving healthcare expectations have increased the importance of lifelong learning and self-management skills among future nurses. Students are expected not only to acquire theoretical knowledge but also to apply that knowledge in clinical environments, regulate their own learning processes, and continuously improve through reflection and feedback. Therefore, communication competence and self-directed learning are recognized as fundamental components of nursing education and professional practice.

Recent developments in digital technology have transformed educational environments by supporting more learner-centered approaches. These innovations offer significant opportunities to enhance learning experiences, particularly in health professions education where knowledge and practice evolve rapidly. Artificial intelligence (AI)-based educational tools have emerged as promising resources for delivering personalized learning experiences. By analyzing individual learning needs, AI-supported systems can provide tailored feedback and targeted guidance, enabling students to identify areas requiring improvement and optimize their learning outcomes. At the same time, the flipped learning model has gained increasing attention as an alternative to conventional classroom instruction. Within this model, students review theoretical content before attending class, while face-to-face sessions are devoted to interactive activities such as discussion, application, and problem-solving exercises. This instructional strategy encourages active participation and allows learners to engage more deeply with educational content. Through collaborative activities and scenario-based discussions, students have greater opportunities to strengthen both critical thinking and communication skills.

The integration of artificial intelligence with flipped learning creates an innovative educational framework that combines technological capabilities with active learning principles. This approach enables students to study educational materials at their own pace before class and participate in experiential learning activities during classroom sessions. Personalized feedback generated through AI technologies further supports students in monitoring their progress and refining their learning strategies. As a result, learners are encouraged to assume greater responsibility for their educational development, fostering stronger self-directed learning behaviors and promoting meaningful, long-term knowledge retention. Therefore, AI-supported flipped learning has the potential to enhance both students' motivation to learn and their ability to plan, monitor, and evaluate their own learning processes.

Although AI-supported flipped learning has attracted growing interest in educational research, evidence regarding its effectiveness within nursing education remains limited. Existing studies have predominantly focused on conventional teaching methods, while the influence of AI-enhanced pedagogical strategies on student learning outcomes has received comparatively less attention. This gap in the literature is particularly relevant in the context of professional communication skills training, which is a critical component of nursing practice. Communication competence directly influences nurses' ability to make clinical decisions, establish therapeutic relationships with patients, and collaborate effectively within multidisciplinary teams. Educational approaches that promote active student engagement can facilitate the integration of theoretical knowledge into clinical practice and support the development of independent thinking, communication, and problem-solving skills. In contrast, educational methods centered solely on information transmission may be insufficient for cultivating these competencies. Innovative teaching strategies may better prepare nursing students to become proactive, reflective, and critically minded healthcare professionals.

Given these considerations, a systematic evaluation of AI-supported flipped learning in professional communication skills education is warranted. Such research may contribute to the advancement of innovative pedagogical practices in nursing education while supporting the preparation of future nurses who are capable of delivering effective, patient-centered, and collaborative care. Moreover, evidence generated from this area of inquiry may guide educational policy development and inform the integration of artificial intelligence technologies into health professions education. Therefore, the aim of this study is to examine the effects of AI-supported flipped learning-based professional communication skills training on nursing students' learning motivation and self-directed learning.

研究类型

介入性

注册 (估计的)

52

阶段

  • 不适用

联系人和位置

本节提供了进行研究的人员的详细联系信息,以及有关进行该研究的地点的信息。

学习地点

参与标准

研究人员寻找符合特定描述的人,称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 孩子
  • 成人
  • 年长者

接受健康志愿者

是的

描述

Inclusion Criteria:

  • Students' ability to speak and understand Turkish
  • Their ability to use a smartphone, computer, or tablet at a level sufficient to access AI-supported materials.

Exclusion Criteria:

  • Participation in a program similar to the intervention to be implemented
  • having a diagnosis of any chronic psychiatric disorder.

学习计划

本节提供研究计划的详细信息,包括研究的设计方式和研究的衡量标准。

研究是如何设计的?

设计细节

  • 主要用途:支持治疗
  • 分配:不适用
  • 介入模型:单组作业
  • 屏蔽:无(打开标签)

武器和干预

参与者组/臂
干预/治疗
实验性的:AI-Supported Flipped Learning Group

Students who met the eligibility criteria were recruited into the study and randomly allocated to either the intervention or control group. Participants assigned to the intervention group were enrolled in the elective Professional Communication course included in the nursing curriculum. In addition to receiving the standard nursing education program, students in the intervention group participated in an Artificial Intelligence (AI)-Supported Flipped Learning-Based Professional Communication Skills Training program. The intervention consisted of six weekly sessions, each lasting approximately 45 minutes. Participants in the control group continued to receive only the standard nursing education curriculum.

The educational program was developed around patient situations frequently encountered by nursing students during clinical practice, including newly admitted patients, individuals displaying crying behavior, patients refusing treatment, those exhibiting anger, individuals making freq

This intervention is distinguished from other educational approaches by integrating artificial intelligence (AI)-supported tools with the flipped learning model to provide a personalized, interactive, and student-centered learning experience. Unlike traditional nursing education methods, this approach enables students to access learning materials before class, analyze clinical scenarios, and receive AI-generated feedback according to their individual learning needs. The intervention combines pre-class preparation, in-class case-based discussions, role-playing, and simulation activities to enhance professional communication skills. AI-assisted activities and feedback mechanisms support students' self-directed learning processes by encouraging reflection, continuous assessment, and individualized improvement. The training specifically focuses on professional communication scenarios frequently encountered in nursing practice, including interactions with patients experiencing anxiety, ange

研究衡量的是什么?

主要结果指标

结果测量
措施说明
大体时间
Learning Motivation
大体时间:Baseline (before the intervention) and immediately after the completion of the 6-week intervention
The change in nursing students' learning motivation levels will be assessed before and after the intervention using a validated learning motivation scale.Students' online learning motivation levels were assessed using the Online Learning Motivation Scale (OLMS), originally developed by Chen and Jang (2010) and adapted into Turkish by Özbaşı et al. (2018). The OLMS consists of 28 items and seven subscales: intrinsic motivation toward knowledge, intrinsic motivation toward accomplishment, intrinsic motivation toward stimulation, identified regulation, integrated regulation, external regulation, and amotivation. The scale uses a 7-point Likert response format, and, consistent with the original version, items 5, 12, 19, and 26 are reverse scored. The total score ranges from 28 to 196, with higher scores in The primary outcome measure is the difference in post-intervention learning motivation scores between students in the AI-supported flipped learning group and those in the control group.
Baseline (before the intervention) and immediately after the completion of the 6-week intervention

次要结果测量

结果测量
措施说明
大体时间
Self-Directed Learning Level
大体时间:Baseline (before the intervention) and immediately after the completion of the 6-week intervention
The change in nursing students' self-directed learning levels will be assessed before and after the intervention using a validated self-directed learning scale.The Self-Directed Learning Skills Scale, developed by Tekkol and Demirel (2018), was used to assess university students' self-directed learning skills. The scale consists of 21 items and comprises four subscales: self-monitoring, motivation, self-control, and self-confidence. Each item is rated on a five-point Likert scale ranging from 1 (Never), 2 (Rarely), 3 (Sometimes), 4 (Usually), to 5 (Always).The total score ranges from 21 to 105, with higher scores indicating a higher level of self-directed learning skills. The Cronbach's alpha reliability coefficients reported for the original scale were 0.76 for the self-monitoring subscale, 0.82 for the The secondary outcome measure is the difference in post-intervention self-directed learning scores between students in the AI-supported flipped learning group and the control group.
Baseline (before the intervention) and immediately after the completion of the 6-week intervention

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查,以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (实际的)

2026年3月15日

初级完成 (估计的)

2026年8月15日

研究完成 (估计的)

2026年9月15日

研究注册日期

首次提交

2026年7月5日

首先提交符合 QC 标准的

2026年7月12日

首次发布 (实际的)

2026年7月15日

研究记录更新

最后更新发布 (实际的)

2026年7月15日

上次提交的符合 QC 标准的更新

2026年7月12日

最后验证

2026年7月1日

更多信息

与本研究相关的术语

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)?

不

IPD 计划说明

This study does not involve individual patient data. The research data consist of educational outcomes and scale assessment results obtained from nursing students. Individual-level identifiable data will not be shared to protect participant confidentiality and data privacy.

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索,没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求,请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改,我们的网站上也会自动更新.

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