Are Serious Games an Effective Teaching Tool in Anatomy Education? A Systematic Review
Bibliographic record
Abstract
Background: Human anatomy is a crucial component of medical curricula, requiring innovative methods to enhance students’ learning outcomes. Recently, various technology-based methods have emerged to address the limitations of traditional anatomy teaching methods. Among these, serious games have emerged as a promising tool demonstrating effectiveness in achieving various learning outcomes. This systematic review aims to evaluate the effectiveness of serious games in anatomy education and identify gaps in literature. Methods: Following PRISMA guidelines, a comprehensive search of databases including PubMed, Scopus, and Google Scholar was performed. Of 900 records identified, 24 records were eligible for the full text review. Of these, 14 studies were included eventually for detailed analysis. Study quality was assessed using the Newcastle–Ottawa Scale. Results: The results showed that the key learning domains assessed were knowledge acquisition, engagement, perception, and skills development. Most studies reported positive outcomes in terms of students’ performance and satisfaction. Despite these findings, variations in study design, sample size, and assessment methods were noted, limiting the generalizability of results. Conclusions: Serious games represent a novel supplement to anatomy education, fostering improved learning outcomes and engagement. However, future work should focus on well-crafted randomized controlled trials to effectively evaluate the impact of using serious games in anatomy teaching with combined qualitative and quantitative evaluation approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".