Factors Influencing Educators’ Perspectives on Accepting Extended Reality in Health Care Education: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Palestinian higher education institutions face limitations in providing interactive practical training for medical education. Extended reality (XR), which encompasses virtual reality and augmented reality, is increasingly recognized for addressing these challenges by offering immersive learning experiences. OBJECTIVE: This study investigates the factors influencing the acceptance and adoption of XR in health care education within Palestinian universities, exploring its potential to transform traditional teaching methods. METHODS: A qualitative approach was used in this study to collect data through semistructured interviews and artifacts from the participants. The participants of the study were 25 faculty members from 2 large Palestinian universities who teach in the field of medical sciences. RESULTS: Three primary categories-external, internal, and design-related factors-emerged as pivotal in influencing XR adoption. Professional development, technical support, and infrastructure were key external enablers. Internally, prior experience with digital tools and positive attitudes had a significant impact on the adoption of XR. Design factors, including ease of use and interactivity, played a crucial role but also posed challenges for less tech-savvy educators. Despite barriers such as cost and technical issues, XR demonstrated notable benefits, including enhanced learning outcomes, improved knowledge retention, and the ability to simulate complex medical scenarios. CONCLUSIONS: XR technologies offer transformative potential for health care education in Palestine. By addressing challenges and leveraging XR's strengths, educational institutions can foster innovation and improve student engagement and skill acquisition. The study contributes to the theoretical understanding of technology acceptance in education by identifying the interplay of external, internal, and design factors. Practically, it emphasizes strategic investments in infrastructure, professional training, and institutional policies to optimize XR integration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".