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Record W4408212769 · doi:10.2196/64751

Applying Augmented Reality to Convey Medical Knowledge on Osteoclasts to Users of a Serious Game: Vignette Experiment

2025· article· en· W4408212769 on OpenAlexvenueno aff
Jascha Grübel, Julia Chatain, Claudio Schmid, Violaine Fayolle, Fabio Zünd, Reinhard Gruber, Bernd Stadlinger

Bibliographic record

VenueJMIR Serious Games · 2025
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
FundersEidgenössische Technische Hochschule Zürich
KeywordsPreprintComputer scienceHuman–computer interactionMultimediaWorld Wide Web

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.025
GPT teacher head0.396
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

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Citations2
Published2025
Admission routes1
Has abstractyes

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