Kolb-Based AI-Powered Neonatal Simulation Training

NCT07872969 | Not Yet Recruiting

• 1 other identifier: 2025/16-02
• Study Type: interventional
• Target: 34
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Nursing Education

Keywords

"Al-Supported Simulation Training"; "Nursing Students"; "Newborn Assessment"; "Newborn Care"; "Computer-Assisted Instruction"

Outcome Measures

Primary Outcomes (1)

• 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.

  Baseline (pre-test, before the intervention), mid-test (during/after demonstration), and post-test (at the end of the 12-week intervention period)

Study Arms (1)

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.

Behavioral: Kolb-Based Artificial Intelligence-Supported Neonatal Simulation Training

Interventions

Kolb-Based Artificial Intelligence-Supported Neonatal Simulation Training BEHAVIORAL

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).

AI-Supported Neonatal Simulation Training

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Being a third-year nursing student
• Taking the neonatal nursing theory course
• Agreeing to participate in volunteer work
• Filling out the developed forms completely

You may not qualify if:

• 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

Sponsors & Collaborators

• Maltepe University: lead

Study Sites (1)

Maltepe University

Istanbul, Maltepe, Turkey (Türkiye)

Location

Related Publications (1)

• 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.

  BACKGROUND

Study Officials

• Tuğba Bozdemir, Assistant Professor

  Maltepe University

  PRINCIPAL INVESTIGATOR

• Selay Deniz Konur, Research Assistant

  Maltepe University

  STUDY DIRECTOR

• Adife Ahsen Çetin, Research Assistant

  Maltepe University

  STUDY DIRECTOR

Central Study Contacts

Tuğba Bozdemir, Assistant Professor

CONTACT

+90 552 097 88 60; tugbabozdemir@maltepe.edu.tr

Selay Deniz Konur, Research Assistant

CONTACT

+90 538 453 48 05; selaydeniz@maltepe.edu.tr

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: August 10, 2026
• First Posted: October 14, 2026
• Study Start (Estimated): November 10, 2026
• Primary Completion (Estimated): January 18, 2027
• Study Completion (Estimated): January 30, 2027
• Last Updated: October 14, 2026

Record last verified: 2026-10

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)