Implementing Augmented Reality to Facilitate the Learning of Oral Histology
Bibliographic record
Abstract
OBJECTIVES: The study of biological materials under a microscope is known as histology, which is one of the most challenging subjects for students. Our objective was to develop a learning tool that can reduce the extrinsic load of studying histology and make learning enjoyable and flexible. We used augmented reality (AR) to create a cellphone application called Dental AR. With Dental AR, students can use their cellphones as dynamic flashcards to hide or reveal the annotations of a histology slide. Our application enables students to study, practice, and self-test oral histology knowledge at their own pace. METHODS: We used Unity3D with Vuforia to develop Dental AR. To generate a set of target images, oral histology glass slides were scanned and converted to digital images. Annotated versions of the slides were used as output for the corresponding target images. To understand user experiences and satisfaction with Dental AR, first-year dentistry students were invited to complete an online survey. RESULTS: Dental AR was successfully developed and released on both the Apple and Google Play online app stores. The survey of dentistry students indicated overall satisfaction with Dental AR and willingness to use similar applications in other subjects. CONCLUSIONS: Dental AR can be used for in-class activities, gamification, and providing students with practice questions to study and self-test outside the classroom. This application can be expanded in the future to incorporate more target images, videos, and interactive components to make learning histology less challenging and more enjoyable.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".