Applying Augmented Reality to Convey Medical Knowledge on Osteoclasts to Users of a Serious Game: Vignette Experiment
Bibliographic record
Abstract
BACKGROUND: Visualization technology is enhancing interactive learning by merging digital content with real-world environments, offering immersive experiences through Augmented Reality (AR) in fields like medical education. AR is being increasingly used in medicine and dental education to improve student learning, particularly in understanding complex concepts such as bone remodeling. Active learning strategies, supported by AR, boost student autonomy, reduce cognitive load, and improve learning outcomes across various disciplines. AR is gaining popularity in higher education as it enhances active learning, reduces cognitive load, and improves cognitive, meta-cognitive, and affective outcomes, particularly in medical and nursing education. The effectiveness of immersive AR in enhancing understanding of complex physiological processes is still unclear, with a lack of rigorous studies on its impact and how to effectively convert academic content into AR. OBJECTIVE: We assess the capacity of AR-enhanced content for learning medical knowledge with a state-of-the-art AR game, published along with a modern cell atlas of the oral cavity. To assess AR-enhanced content for learning, we formulated hypotheses on the general impact on learning (H1), specific improvements in learning (H2) and the long-term retention (H3). METHODS: An AR serious game was developed to represent current knowledge on osteoclasts for classroom use. The game was evaluated in an unblinded face-to-face vignette experiment (39 participants): Learning outcomes on "Osteoclasts" were compared between the AR game (17 participants) and a textbook-only option (20 participants) conveying the same content. Participants were randomly assigned and learning success was measured at three time-points, immediately after the experiment session, one week later, and one month later, via web-based surveys. RESULTS: The AR serious game elicited strong interest in the topic (perceived relevance in ARCS, W= 10417; P < .001) and motivated students by increasing self-efficacy (confidence in ARCS, W = 11882.5; P = .023) and satisfaction (in ARCS, W = 4561; P < .001). The learning outcomes were comparable to text-based self-learning (T = 2.0103; PBonferroni = .095). Furthermore, curious students benefited more from interactive learning methods compared to text-only methods and had higher learning success (T = -2.518; P = .019). CONCLUSIONS: Introducing new technology such as AR into teaching requires technological investment, updated curricula, and careful application of learning paradigms. We found support for improved motivation (H1) and some evidence of AR's baseline effectiveness (H2a). While we couldn't confirm AR's impact on visual tasks overall (H2b), we noted an interesting interaction between curiosity and visual task outcomes (H2c), as well as how game design influences student perception of the material (H2d). Due to attrition, long-term learning outcomes (H3) could not be assessed. AR-based learning may particularly benefit curious students, who often struggle with text-heavy methods. As students are increasingly accustomed to brief, engaging content, teaching approaches must adapt.
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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 | 0.001 |
| 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) | 0.001 | 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 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".