AI-SUPPORTED FLIPPED LEARNING IN NURSING EDUCATION | NCT07705152

Description Module

Brief Summary

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.

Detailed Description

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.

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
AI-Supported Flipped Learning Group	Experimental	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	Other: Artificial Intelligence-Supported Flipped Learning Approach

Interventions

Name	Type	Description	Arm Group Labels	Other Names
Artificial Intelligence-Supported Flipped Learning Approach	Other	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

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Learning Motivation	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

Secondary Outcomes

Measure	Description	Time Frame
Self-Directed Learning Level	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.