Technology Integration in Syrian Medical Education From the Perspective of Students and Faculty: A Cross-Sectional Evaluation
Bibliographic record
Abstract
Background: Technology-enhanced learning (TEL) has become increasingly vital in global medical education, offering significant advantages in knowledge acquisition, communication, motivation, and student engagement. In Syria, a country facing prolonged crises, there is an urgent need to evaluate the integration of technology within medical education to address institutional limitations and support student learning. Objective: The aim of this study is to evaluate the awareness, perceived challenges, and needs regarding the integration of technology in medical education from the perspectives of students and faculty at Syrian medical colleges. Methods: A cross-sectional survey was conducted during the 2023-2024 academic year across Syrian universities. Stratified random sampling was used to recruit 500 medical students and 200 faculty members. Two tailored, self-administered questionnaires were used, covering motivation, perceived benefits, challenges, and suggestions for technology integration. Validity was assessed through expert review and pilot testing (n=30), and internal consistency was confirmed (Cronbach α=0.6-0.7). Quantitative data were analyzed using descriptive statistics, t tests, ANOVA, and Kruskal-Wallis tests. Results: Among medical students, 94% (470\500) agreed that integrating technology into medical education is essential, with similar agreement from 93.5% (187\200) of faculty. No significant differences were found in student responses based on specialization (P=.32) or university type (P=.11). Likewise, faculty perspectives did not significantly differ by academic qualification or years of experience (P>.05). There were several perceived benefits; for instance, 93.2% (n=466) of students reported that technology kept them up to date with new developments, 88% stated it enhanced research skills, and 86.8% found TEL more enjoyable than traditional learning methods. Most respondents (95% n=475) said TEL created a flexible, interactive environment. Among faculty, 77% (n=154) agreed TEL improves clinical skill development. Respondents noted there were some challenges; specifically, 57% (n=285) of students cited poor internet service, 33% (n=165) noted the financial burden, and 82.2% (n=411) called for behavioral guidelines. Among faculty, 85.5% (n=171) cited lack of institutional support and 90% (n=180) emphasized the need for training. Both groups supported the development of communication platforms, curriculum revisions, and faculty development programs. Conclusions: There is a strong consensus among Syrian medical students and faculty on the value and necessity of integrating technology in medical education. Despite infrastructure and administrative challenges, both groups recognize TEL as a powerful tool for improving clinical competencies, student motivation, and academic engagement. Institutional commitment, curricular reform, and tailored training are essential to achieving sustainable, effective technology integration.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".