Status and perceptions of ChatGPT utilization among medical students: a survey-based study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».