AI-Driven Smart Learning Platform for University Students

Revolutionizing Clinical Education for University Students: The Impact of AI-Driven Smart Learning Platforms on Reflective Thinking, Emotional Competence, and Clinical Embeddedness: An RCT Study

The rapid advancement and integration of Artificial Intelligence (AI) into various facets of modern life have ushered in a new era of technological possibilities, particularly within the realm of education. AI-driven smart learning platforms (SLPs) are emerging as powerful tools with the potential to revolutionize how individuals learn and develop crucial skills. These platforms, characterized by adaptive learning algorithms, personalized feedback mechanisms, and intelligent tutoring systems, offer a dynamic and interactive learning experience that traditional methods often struggle to replicate. This exploration delves into the multifaceted impact of AI-driven SLPs on three key dimensions of professional development: reflective thinking, emotional competence, and clinical embeddedness. Understanding the complex interplay between these elements and the influence of AI is crucial for shaping the future of education and professional training (Ali et al., 2023).

Study Overview

• Status: Completed
• Conditions: Artificial Intelligent
• Intervention / Treatment: Behavioral: AI- driven simulation platform

Detailed Description

Reflective thinking, the capacity to critically analyze experiences, identify patterns, and derive meaningful insights for future action, is a cornerstone of continuous learning and professional growth. It involves a deliberate process of introspection and evaluation, enabling individuals to learn from both successes and failures. In professional fields, particularly those involving complex decision-making, reflective thinking is essential for adapting to changing circumstances, improving performance, and fostering innovation. AI-driven SLPs, with their ability to provide personalized feedback and track learning progress, may offer unique opportunities to cultivate reflective thinking skills. However, the extent to which these platforms truly promote deep reflection versus surface-level learning requires careful examination (Cohen et al., 2023).

Emotional competence, encompassing a range of skills related to self-awareness, self-regulation, motivation, empathy, and social skills, is increasingly recognized as a critical factor in personal and professional success. In today's interconnected world, individuals must possess the ability to manage their own emotions, understand and respond effectively to the emotions of others, and build strong interpersonal relationships. Emotional competence is particularly vital in fields that involve direct interaction with people, such as healthcare, education, and social work. The role of AI-driven SLPs in fostering emotional competence is a complex issue. While these platforms can provide personalized learning experiences, they may also lack the human element crucial for developing empathy and social skills (Vistorte et al., 2024).

Clinical embeddedness, the degree to which an individual is integrated within their professional context, plays a significant role in their commitment, performance, and overall contribution. It encompasses a sense of belonging, connection to colleagues, and understanding of organizational culture. In clinical settings, embeddedness is crucial for ensuring effective teamwork, promoting knowledge sharing, and fostering a culture of patient safety. The impact of AI-driven SLPs on clinical embeddedness is an area that warrants further investigation. While these platforms can facilitate access to information and training, their influence on social interaction and professional integration needs to be carefully considered (Klimova & Pikhart. 2025).

• Study Type: Interventional
• Enrollment (Actual): 140
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Egypt
  • Sidigaber
    • Alexandria, Sidigaber, Egypt, 52312
      • Faculty of Nursing, Alexandria University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

students enrolled in psychiatric mental health nursing department and Currently participating in clinical rotations.

Willingness to provide informed consent.

Exclusion Criteria:

Students with significant cognitive impairments that may affect their ability to participate in the study.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Supportive Care
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: interventional group; This investigation will draw upon existing literature exploring the intersection of AI in education, reflective practice, emotional intelligence, and professional integration. By synthesizing these perspectives, we aim to provide a comprehensive overview of the transformative potential of AI-driven SLPs in shaping future professionals. Furthermore, this analysis will consider the ethical implications of using AI in education, including issues related to data privacy, algorithmic bias, and the potential displacement of human interaction. Ultimately, understanding the impact of AI-driven SLPs on reflective thinking, emotional competence, and clinical embeddedness is crucial for effectively designing and deploying these technologies in a way that promotes holistic professional development. By carefully considering the human element in the age of AI, we can ensure that these powerful tools are used to enhance, rather than diminish, the essential skills and attributes that make professiona	Behavioral: AI- driven simulation platform; This investigation will draw upon existing literature exploring the intersection of AI in education, reflective practice, emotional intelligence, and professional integration. By synthesizing these perspectives, we aim to provide a comprehensive overview of the transformative potential of AI-driven SLPs in shaping future professionals. Furthermore, this analysis will consider the ethical implications of using AI in education, including issues related to data privacy, algorithmic bias, and the potential displacement of human interaction.
Placebo Comparator: control group; traditional learning methods such as lectures and group discussion	Behavioral: AI- driven simulation platform; This investigation will draw upon existing literature exploring the intersection of AI in education, reflective practice, emotional intelligence, and professional integration. By synthesizing these perspectives, we aim to provide a comprehensive overview of the transformative potential of AI-driven SLPs in shaping future professionals. Furthermore, this analysis will consider the ethical implications of using AI in education, including issues related to data privacy, algorithmic bias, and the potential displacement of human interaction.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Tool I: Reflective Thinking Scale	It was developed by (Kember et al., 2000) and consist of 17 questions to measure level of reflective thinking among university students and include four items habitual actions, understanding, reflection and critical reflection. the demographic data was attached to this tool in order to assess characteristics of participated students as age, gender, and residence.	1 month

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The Situational Emotional Response Scale (ERES)	It was developed by (Mayor-Silva et al., 2024) to assess the level of emotional skills in university students. It consists of 34 questions that measure four factors as Communication and positive emotional influence, Awareness of others, empathy, and listening, Emotional self-regulation and outcome-oriented thinking and Appropriate self-assessment and personal development.	1 month
Clinical Adjustment scale	It was developed by (Labrague et al., 2024) to assess clinical adjustment among student nurses during their clinical placements. It consists of 15 questions and include 3 factors actors as following: Professional Growth and interpersonal Engagement, Clinical Competence and Confidence and Coping and Support Strategies.	1 month

Collaborators and Investigators

• Sponsor: Alexandria University
• Investigators: Study Chair: halla Ali, lecturer, hallaaly42@gmail.com

Study record dates

Study Major Dates

• Study Start (Actual): February 13, 2025
• Primary Completion (Actual): May 15, 2025
• Study Completion (Actual): May 30, 2025

Study Registration Dates

• First Submitted: February 22, 2025
• First Submitted That Met QC Criteria: February 22, 2025
• First Posted (Actual): February 27, 2025

Study Record Updates

• Last Update Posted (Estimated): September 18, 2025
• Last Update Submitted That Met QC Criteria: September 12, 2025
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Keywords: Artificial Intelligence, Reflective Thinking, Emotional Competence, and Clinical Embeddedness
• Other Study ID Numbers: 22/2/2025

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No