Assessing Familiarity, Usage Patterns, and Attitudes of Medical Students Toward ChatGPT and Other Chat-Based AI Apps in Medical Education: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
Background: There has been a rise in the popularity of ChatGPT and other chat-based artificial intelligence (AI) apps in medical education. Despite data being available from other parts of the world, there is a significant lack of information on this topic in medical education and research, particularly in Saudi Arabia. Objective: The primary objective of the study was to examine the familiarity, usage patterns, and attitudes of Alfaisal University medical students toward ChatGPT and other chat-based AI apps in medical education. Methods: This was a cross-sectional study conducted from October 8, 2023, through November 22, 2023. A questionnaire was distributed through social media channels to medical students at Alfaisal University who were 18 years or older. Current Alfaisal University medical students in years 1 through 6, of both genders, were exclusively targeted by the questionnaire. The study was approved by Alfaisal University Institutional Review Board. A χ2 test was conducted to assess the relationships between gender, year of study, familiarity, and reasons for usage. Results: A total of 293 responses were received, of which 95 (32.4%) were from men and 198 (67.6%) were from women. There were 236 (80.5%) responses from preclinical students and 57 (19.5%) from clinical students, respectively. Overall, males (n=93, 97.9%) showed more familiarity with ChatGPT compared to females (n=180, 90.09%; P=.03). Additionally, males also used Google Bard and Microsoft Bing ChatGPT more than females (P<.001). Clinical-year students used ChatGPT significantly more for general writing purposes compared to preclinical students (P=.005). Additionally, 136 (46.4%) students believed that using ChatGPT and other chat-based AI apps for coursework was ethical, 86 (29.4%) were neutral, and 71 (24.2%) considered it unethical (all Ps>.05). Conclusions: Familiarity with and usage of ChatGPT and other chat-based AI apps were common among the students of Alfaisal University. The usage patterns of these apps differ between males and females and between preclinical and clinical-year students.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".