Nursing EducationNCT07872969
Kolb-Based AI-Powered Neonatal Simulation Training
A study testing Kolb-Based Artificial Intelligence-Supported Neonatal Simulation Training in people with Nursing Education, run by Maltepe University at 1 site in Turkey (Türkiye).
Trial summary
This quasi-experimental study aims to evaluate the multidimensional effects of an artificial intelligence-supported newborn simulation training program based on Kolb's Experiential Learning Theory on nursing students' self-efficacy, knowledge, health literacy, satisfaction, and confidence in learning. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Nursing Education
- What is being tested?
- Kolb-Based Artificial Intelligence-Supported Neonatal Simulation Training
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 34 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 10 Nov 2026to 30 Jan 2027
- Phase
- Interventional
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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Being a third-year nursing student
- Taking the neonatal nursing theory course
- Agreeing to participate in volunteer work
- Filling out the developed forms completely
Who cannot join
- Not being enrolled in the third year of the nursing licensure program
- Not having taken the neonatal nursing theory course or not having completed the course yet
- Not giving consent to participate voluntarily in the study or withdrawing from the study at any stage
- Filling out data collection tools incompletely, incorrectly, or invalidly
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Multidimensional Effects of an Artificial Intelligence-Assisted Neonatal Simulation Training Based on Kolb's Experiential Learning Theory on Nursing Students: A Quasi-Experimental Study
This quasi-experimental study aims to evaluate the multidimensional effects of an artificial intelligence-supported newborn simulation training program based on Kolb's Experiential Learning Theory on nursing students' self-efficacy, knowledge, health literacy, satisfaction, and confidence in learning. The study will be conducted with third-year nursing students at Maltepe University during the Fall semester. The design incorporates a single-group, three-time measurement model: pre-test (baseline before training), mid-test (after simulation observation/demonstration), and post-test (after individual application and reflection). The intervention spans a total of 12 weeks. Data will be collected using an introductory information form, the Multidimensional Assessment Scale for Assessment and Care of Newborns, and the Student Satisfaction and Self-Confidence in Learning Scale. Statistical analyses will include descriptive statistics, repeated measures ANOVA or Friedman tests, and correlation analyses.
Read the detailed description
The neonatal period requires rapid physiological adaptation and precise clinical evaluation, making neonatal care a critical component of nursing education. Traditional teaching methods often fall short in fostering critical thinking and active clinical decision-making. To bridge this gap, this study integrates artificial intelligence (AI) into neonatal simulation training based on Kolb's Experiential Learning Theory, which defines learning as a four-stage cycle: concrete experience, reflective observation, abstract conceptualization, and active experimentation.
The intervention program lasts a total of 12 weeks, with each session structured carefully according to Kolb's cycle. Participants first receive theoretical background and educational brochures/videos, followed by AI-supported simulation scenarios, structured debriefing and reflection sessions, and personalized feedback generated by AI based on performance checklists and simulation recordings.
Measurements are taken at three key time points for the same participant group (T0: pre-test, T1: mid-test, T2: post-test) to assess changes in knowledge, self-efficacy, health literacy, skills, satisfaction, and learning confidence. Data analyses will be performed using SPSS software with appropriate parametric or non-parametric tests based on distribution normality."
- Conditions
- Nursing Education
- Being tested
- Kolb-Based Artificial Intelligence-Supported Neonatal Simulation Training
- Keywords
- "Al-Supported Simulation Training", "Nursing Students", "Newborn Assessment", "Newborn Care", "Computer-Assisted Instruction"
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Kolb-Based Artificial Intelligence-Supported Neonatal Simulation TrainingBehavioural
Participants will receive AI-supported neonatal care simulation training based on Kolb's Experiential Learning Cycle (concrete experience-reflective observation-abstract conceptualization-active experience). The training consists of (1) brief theoretical background information, (2) simulation application with neonatal clinical scenarios, (3) structured debriefing/reflection session, and (4) AI-supported feedback and personalized learning recommendations. The AI component provides real-time/post-session feedback, error/deficiency analysis, and individualized study recommendations for the next session based on performance checklist inputs and/or simulation recordings. The intervention lasts a total of 12 weeks, with each session structured according to Kolb's Experiential Learning Cycle. All participants receive the same content and scenarios; there is no control arm. Measurements are taken at time points T0 (pre-test), T1 (mid-test), and T2 (post-test).
| Study group | Role | What they receive |
|---|---|---|
| AI-Supported Neonatal Simulation Training | Experimental | This is a single-arm intervention group. Participants will receive AI-supported neonatal simulation training based on Kolb's Experiential Learning Theory. Receives: Behavioral: Kolb-Based Artificial Intelligence-Supported Neonatal Simulation Training |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in nursing students' self-efficacy regarding initial neonatal assessment and care from baseline (pre-test) to post-intervention.
Change in nursing students' self-efficacy regarding initial neonatal assessment and care from baseline to post-intervention, as assessed by the Self-Assessment Scale for Initial Neonatal Assessment and Care (first section of the Multidimensional Assessment Scale for Assessment and Care of Newborns). The scale consists of 26 items evaluated on a 4-point Likert scale, with total scores ranging from 26 to 104. Higher scores indicate greater self-efficacy regarding initial neonatal assessment and care.
Measured: Baseline (pre-test, before the intervention), mid-test (during/after demonstration), and post-test (at the end of the 12-week intervention period)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Oct 2026
- Study start10 Nov 2026 (estimated)
- Primary completion18 Jan 2027 (estimated)
- Study completion30 Jan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Nursing EducationStudies 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
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 34 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Maltepe University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Tuğba Bozdemir, Maltepe University
- Investigators
- Tuğba Bozdemir, Assistant Professor, Principal investigator, Maltepe UniversitySelay Deniz Konur, Research Assistant, Study director, Maltepe UniversityAdife Ahsen Çetin, Research Assistant, Study director, Maltepe University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07872969
- Sponsor's protocol ID
- 2025/16-02
- Status last verified
- Oct 2026
- First submitted
- 10 Aug 2026
- Last updated
- 14 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Akgerman A, Özdemir Yavuz ED, Kavaslar İ, Güngör S. Yapay zekâ ve hemşirelik. Journal of Artificial Intelligence in Health Sciences. 2022;2(1):21-27. doi:10.52309/jaihs.v2i1.36. Aykanat Girgin B, Gözen D. Dünya'da ve ülkemizde yenidoğan sağlığının mevcut durumu. In: Arslan S, editor. Yenidoğan Hemşireliği-1. 1. bs. [Basım yeri belirtilmemiş]: Eğiten Matbaacılık; 2024. p.21-39. Bresolin P, Martini JG, Maffissoni AL, Sanes MdS, Riegel F, Unicovsky MAR. Debriefing in clinical nursing simulation: an analysis based on the theory of experiential learning. Rev Gaucha Enferm. 2022 Aug 26;43:e20210050. doi:10.1590/1983-1447.2022.20210050.en. PubMed Buchanan C, Howitt ML, Wilson R, Booth RG, Risling T, Bamford M. Predicted influences of artificial intelligence on nursing education: scoping review. JMIR Nurs. 2021;4(1):e23933. doi:10.2196/23933. Cheng J, Wu Y, Huang L, Wu Y, Guan Y. Integrating Kolb's experiential learning theory into nursing education: a four-stage intervention with case analysis, mind maps, reflective journals, and peer simulations for advanced health assessment. Front Med (Lausanne). 2025;12:1616392. doi:10.3389/fmed.2025.1616392. Frontiers Chen L, Chen P, Lin Z. Artificial intelligence in education: a review. IEEE Access. 2020;8:75264-75278. doi:10.1109/ACCESS.2020.2988510. Choshi M. Addressing challenges in undergraduate community health nursing clinical: Kolb's experiential learning theory. J Nurs Educ. 2025;64(6):e31-e34. doi:10.3928/01484834-20240513-02. Healio Journals Copeland BJ. Artificial intelligence [Internet]. In: Encyclopaedia Britannica. [cited 2025 Dec 30]. Available from: https://www.britannica.com/technology/artificial-intelligence Dikmen Y. Kolb'un öğrenme stilleri modeline göre hemşirelik öğrencilerinin öğrenme stillerinin incelenmesi. Journal of Human Rhythm. 2015;1(3):100-106.