Immersive Virtual Learning Environments for Healthcare Education: State-of-Art and Open Problems
Bibliographic record
Abstract
Immersive virtual learning environments (iVLEs), serious games and virtual simulations in particular, allow trainees to learn from virtual simulated experiences in an interactive, engaging, and ethically safe manner. Although the use of iVLEs in healthcare education was steadily growing over the last 10-15 years, their use was accelerated during the coronavirus disease 2019 (COVID-19) pandemic as they played a significant role in facilitating remote learning during the pandemic-related lock-downs. Within healthcare education, iVLEs are not so much technologies of the future but rather, of the present. Educators are finding innovative ways to leverage the coupling of iVLEs and emerging technologies such as extended reality (augmented, mixed, and virtual realities), and artificial intelligence (AI), to transform healthcare education. Despite the growing popularity and benefits of iVLEs, there are several issues/limitations in their current form including the following three that particularly limit their use. More specifically, iVLEs typically i) contain fixed/static scenarios leading to predictability, disengagement, and repetition in learning experiences, ii) emphasize cognitive skills in contrast to psychomotor skills given the difficulties and cost associated in simulating the sense of touch which is inherent in psychomotor-based tasks, and iii) are not inclusive and therefore, do not properly address user diversity and accessibility. With a focus on healthcare education, this article begins with an overview of iVLEs, followed by a discussion regarding these limitations and potential solutions to overcome these limitations.
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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 | 0.001 | 0.001 |
| 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".