Development and Evaluation of a Case-Based Serious Game for Diagnosis and Treatment Planning in Orthodontic Education: Quasi-Experimental Study
Bibliographic record
Abstract
Background: Orthodontic education requires effective training in diagnosis and treatment planning, but traditional teaching methods may lack engagement and opportunities in offering a safe learning environment. Serious games are gaining momentum in dental education due to their positive educational impact in enhancing learner knowledge and motivation. However, their application in orthodontic diagnosis and treatment planning training remains unexplored. Objective: The aim of this study was to develop and evaluate a simulation-based serious game for training orthodontic diagnosis and treatment planning in virtual patients (OrthoVirt), examining its impact on student knowledge and satisfaction. This study also explored whether prior gaming experience influenced learning outcomes. Methods: A quasi-experimental study was conducted with 32 fourth-year dental students, who were requested to complete a preknowledge assessment, 3 simulated patients within OrthoVirt, a postknowledge assessment, and a satisfaction survey. Participants were categorized as gamers (n=16) or nongamers (n=16) based on self-reported weekly gaming time. The primary outcome was knowledge improvement, analyzed using 2-tailed paired t tests (Cohen dz). Group comparisons were conducted using 2-tailed independent t tests (Cohen d). User satisfaction was measured using a validated questionnaire based on the technology acceptance model. A stricter significance threshold (P<.01) and effect size metrics (Cohen d and Cohen dz) were used to account for the small sample size, multiple comparisons, and exploratory nature of the study. Results: Both gamer and nongamer groups showed significant knowledge improvement after using OrthoVirt (mean score increased from 10.75 (SD 2.75) to 14.75 (SD 1.81) out of 20; P<.001). The mean scores of the gamer group increased from 10.31 (SD 3.07) to 15.19 (SD 1.83) while those of the nongamer group rose from 11.19 (SD 2.40) to 14.31 (SD 1.74). No statistically significant differences were found between groups in pre- and postknowledge assessments as well as improvement scores (P>.01), suggesting that the educational benefit was consistent regardless of gaming background. Participants from both groups rated OrthoVirt positively, particularly for "perceived ease of use." However, "perceived enjoyment" was rated slightly lower than other aspects, with nongamers scoring it 3.60 (SD 0.81) and gamers 3.45 (SD 0.73) out of 5, indicating a potential area for design enhancement. Overall satisfaction ratings were similar between the 2 groups (P>.01). Conclusions: OrthoVirt demonstrated potential as a supplementary tool for diagnosis and treatment planning in orthodontic education, with statistically significant improvements observed in learner knowledge. While feedback was generally positive, these findings should be interpreted with caution due to the quasi-experimental design and small sample size. Future development should focus on improving user enjoyment and engagement through entertaining design elements. Further research should explore how OrthoVirt can be integrated as a case discussion tool alongside lectures, with the potential to enhance learning not only in orthodontic education but also across other areas of dental training.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".