Artificial IntelligentNCT06848803
AI-Driven Smart Learning Platform for University Students
A study testing AI- driven simulation platform in people with Artificial Intelligent, run by Alexandria University at 1 site in Egypt.
Trial 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. Read more about this study
- Who is it for?
- Ages 19 to 23 years
- Conditions the study is for
- Artificial Intelligent
- What is being tested?
- AI- driven simulation platform
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 140 participants
- Where
- 1 siteEgypt
- Study dates
- 13 Feb 2025to 30 May 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 19 to 23 years
- All sexes
- No healthy volunteers
- Adult
Who may be able to join
- students enrolled in psychiatric mental health nursing department and Currently participating in clinical rotations.
- Willingness to provide informed consent.
Who cannot join
- Students with significant cognitive impairments that may affect their ability to participate in the study.
Trial locations and contacts
1 site in 1 country.
Egypt1 site
Faculty of Nursing, Alexandria university
Alexandria, Sidigaber, 52312
About this study
Official title: 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).
Read the 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).
- Conditions
- Artificial Intelligent
- Being tested
- AI- driven simulation platform
- Keywords
- Artificial Intelligence, Reflective Thinking, Emotional Competence, and Clinical Embeddedness
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI- driven simulation platformBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| interventional group | Experimental | 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 Receives: Behavioral: AI- driven simulation platform |
| control group | Placebo comparator | traditional learning methods such as lectures and group discussion Receives: Behavioral: AI- driven simulation platform |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1 month
Secondary outcomes (2)
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.
Measured: 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.
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Feb 2025
- Study start13 Feb 2025
- Primary completion15 May 2025
- Study completion30 May 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligentStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 140 participants (actual)
Sponsor and investigators
- Lead sponsor
- Alexandria University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Rasha salah elsayed eweida, Alexandria University
- Investigators
- halla Ali, lecturer, Study chair, hallaaly42@gmail.com
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06848803
- Sponsor's protocol ID
- 22/2/2025
- Status last verified
- Sep 2025
- First submitted
- 22 Feb 2025
- Last updated
- 18 Sep 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT06848803
- Acronym / sponsor ID
- 22/2/2025
- Conditions
- Artificial Intelligent
- Keywords
- Artificial Intelligence, Reflective Thinking, Emotional Competence, and Clinical Embeddedness
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 140 participants (actual)
- Lead sponsor
- Alexandria UniversityUniversity, hospital or other
- Study start
- 13 Feb 2025 (actual)
- Primary completion
- 15 May 2025 (actual)
- Study completion
- 30 May 2025 (actual)
- First posted
- 27 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Study design
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| interventional group | Experimental | Behavioral: AI- driven simulation platformThis 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 |
| control group | Placebo comparator | Behavioral: AI- driven simulation platformtraditional learning methods such as lectures and group discussion |
Who may qualify
- Ages
- 19 to 23 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
Key inclusion criteria (2 listed)
- students enrolled in psychiatric mental health nursing department and Currently participating in clinical rotations.
- Willingness to provide informed consent.
Key exclusion criteria (1 listed)
- Students with significant cognitive impairments that may affect their ability to participate in the study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI- driven simulation platformThis 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. | Behavioural | control group, interventional group |
Primary outcome
Tool I: Reflective Thinking Scale
Time frame: 1 month
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.
2 secondary outcomes
The Situational Emotional Response Scale (ERES)
Time frame: 1 month
Clinical Adjustment scale
Time frame: 1 month
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Sep 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
halla Ali, lecturer study chair
hallaaly42@gmail.com
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI- driven simulation platform in people with Artificial Intelligent, run by Alexandria University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 18 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Tool I: Reflective Thinking Scale (time frame: 1 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Sep 2025.