Development of a Serious Game to Simulate Neonatal Intensive Care Unit Experiences: Collaborative Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Opportunities for neonatal intensive care unit (NICU) training are limited for medical and nursing students due to patient safety concerns and the complexities of neonatal care. In addition, the COVID-19 pandemic significantly disrupted clinical training opportunities, further underscoring the need for alternative educational tools that can provide immersive and practical learning experiences. Serious games have garnered attention as potential tools for medical education; however, few are designed to simulate the complete NICU environment and its unique challenges. OBJECTIVE: To address the educational gaps in neonatal care training, we aimed to develop and evaluate a serious game that provides a comprehensive NICU simulation experience for students and the general public. METHODS: The game was developed over 14 months by a collaborative team that included a neonatologist, 4 medical students, and 1 art student, with a total cost of US $10,000. Initially created in TyranoBuilder (STRIKEWORKS), the game was later redeveloped in Unity with Naninovel to support multilingual functionality. Structured as a 6-chapter visual novel, the game follows a high school student observing the NICU during a hospital internship. Scenario-based decision-making and interactive dialogues guide the player through both the clinical and emotional aspects of neonatal care. After completing the game, players were invited to participate in an optional web-based survey that assessed demographic information, gameplay quality, and educational value using Likert scales. Descriptive and inferential statistics were used for data analysis. RESULTS: The game, titled First Steps in the NICU, was released for iOS, Android, and Steam. As of May 2025, it has been downloaded 2799 times (2260 on iOS and 539 on Android). A total of 160 survey responses were collected, with 46.3% of respondents identifying as health care professionals or students. The majority of participants were female (114/160, 71.3%) and aged 20-29 years (59/160, 36.9%). Mean scores for length, difficulty, and gameplay were 3.05 (SD 0.62), 2.49 (SD 0.76), and 3.65 (SD 0.77), respectively, indicating a well-balanced design. The educational usefulness of the game received high ratings: empathy with the story (4.24), usefulness for knowledge acquisition (4.16), and effectiveness of serious games as a learning tool (4.37). No significant differences in evaluations were found between health care professionals and students and the general public, suggesting broad accessibility and appeal. CONCLUSIONS: We developed a low-cost serious game that simulates NICU experiences through collaboration between a neonatologist and students. The game received positive feedback and demonstrated educational value for a diverse audience. Positioned as formative research, this study highlights the potential of serious games to supplement neonatal care education. Future updates will incorporate user feedback, leading to improvements in gameplay and expanded content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".