Status and perceptions of ChatGPT utilization among medical students: a survey-based study
Bibliographic record
Abstract
BACKGROUND: The integration of ChatGPT with educational settings is happening at an unprecedented rate, and there is a growing trend for students to use ChatGPT for various academic work. Although numerous studies have evaluated the knowledge, attitudes, and practices related to ChatGPT among students in diverse medical fields, there remains a notable absence of such research within the Chinese context. METHODS: The questionnaire survey was conducted to a sample of 1,133 medical students from various medical colleges across Sichuan Province, China, between May 2024 and November 2024 to explore the awareness and attitudes of medical students towards ChatGPT. Descriptive statistics were used to tabulate the frequency of each variable. A chi-square test and multiple regression analysis were employed to investigate the factors influencing participants' positive attitudes toward the prospective use of ChatGPT. RESULTS: The findings revealed that 62.9% of participants had employed ChatGPT in their medical studies, with 16.5% having utilized the tool in a published article. Participants primarily used ChatGPT for searching information (84.4%) and completing academic assignments (60.4%). However, concerns were expressed regarding the potential for ChatGPT to disseminate misinformation (76.9%) and facilitate plagiarism or complicate its detection (65.4%). Despite these concerns, 64.4% of respondents indicated a willingness to use ChatGPT to seek assistance with learning problems. Overall, a majority of participants (60.7%) maintained a positive attitude on the future use of ChatGPT in the medical field. CONCLUSION: Our research showed that while most medical students perceived ChatGPT as a valuable tool for academic study and research, they remained cautious about its potential risks, particularly regarding misinformation and plagiarism concerns. Despite these reservations, a majority participants indicated a willingness to incorporate ChatGPT into their academic workflow, specifically for problem-solving tasks, and maintained optimistic perspectives regarding its potential integration into medical education and clinical practice. It is therefore essential to improve student literacy about AI, develop clear guidelines for its acceptable use, and implement support systems to ensure that medical students are fully prepared for the upcoming integration of AI into medical education. TRIAL REGISTRATION: Not applicable.
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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 | 0.002 | 0.013 |
| 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 | 0.000 | 0.000 |
| 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".