The impacts of augmented reality teaching tools in health professional education.
Bibliographic record
Abstract
Objective: Augmented reality (AR) has successfully facilitated clinical training in health professional education. This technology can also accelerate non-clinical classroom education by improving students' spatial understanding and mental rotation skills, essential for many health professional education programs, including dental hygiene. However, this use has been relatively less explored and evaluated. This review investigates the effectiveness of AR-based tools in non-clinical didactic teaching. Methods: A literature search was conducted in 3 databases using the search terms "augmented reality," "classroom teaching," and "health professional education." Articles were screened first by the title and then by full-text review to identify reports that met the inclusion criteria and were relevant to the research questions. Results: Nineteen articles were included in the narrative review. AR Magic Mirror and ARBOOK were found to be the 2 most-used AR tools in didactic teaching. AR-based teaching tools can reduce cognitive loads and improve knowledge acquisition, spatial understanding, mental rotation skills, attention, motivation, confidence, and satisfaction. Discussion: AR tools can significantly improve students' learning experiences compared to traditional teaching methods in health professional education. As most AR-based teaching tools are focused on teaching anatomy, many health professional education programs can benefit from these tools. However, qualitative exploration of student and faculty perspectives and development costs are absent from the literature. Conclusion: Didactic learning of basic science concepts such as anatomy is essential to many health professional education programs, including dental hygiene. Dental hygiene can largely benefit from incorporating AR-based teaching tools into classroom education.
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 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".