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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

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06848803
Enrollment
140
Registered
2025-02-27
Start date
2025-02-13
Completion date
2025-05-30
Last updated
2025-09-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligent

Keywords

Artificial Intelligence, Reflective Thinking, Emotional Competence, and Clinical Embeddedness

Brief summary

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).

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).

Interventions

BEHAVIORALAI- 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.

Sponsors

Alexandria University
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SUPPORTIVE_CARE
Masking
SINGLE (Subject)

Eligibility

Sex/Gender
ALL
Age
19 Years to 23 Years
Healthy volunteers
No

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.

Design outcomes

Primary

MeasureTime frameDescription
Tool I: Reflective Thinking Scale1 monthIt 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.

Secondary

MeasureTime frameDescription
The Situational Emotional Response Scale (ERES)1 monthIt 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.
Clinical Adjustment scale1 monthIt 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.

Countries

Egypt

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026