Integration of ChatGPT in medical learning: An analysis of interaction and contradictions.
Background: Current research on generative AI in medical education focuses on AI's performance or risks, such as unreliability. We argue these issues are not isolated flaws but are symptoms of systemic contradictions that emerge when a technology is introduced into a learning environment. To move be...
| Publicado en: | Medical Teacher Vol. 48; no. 6; pp. 1004 - 1012 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jun2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193923653&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193923653 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0142159X MCH jtl: Medical Teacher issn: 0142159X maglogo: Y pubinfo: dt: Jun2026 vid: 48 iid: 6 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 193923653 190461441 193923653 193923653 10.1080/0142159X.2025.2604245 193923653 ppf: 1004 ppct: 8 formats: tig: atl: Integration of ChatGPT in medical learning: An analysis of interaction and contradictions. aug: au: Tai, Shih-Hsuan Yeh, Chi-Chuan Wang, Jann-Yuan Hu, Rey-Heng Lee, Po-Huang Ho, Cheng-Maw affil: School of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan sug: subj: Artificial Intelligence, Generative Utilization Education, Medical Educational Technology Students, Medical Self-Directed Learning Student Experiences Evaluation User-Computer Interface Peer Group Interpersonal Relations Faculty, Medical Faculty-Student Relations Computer-Assisted Instruction Human Taiwan Qualitative Studies Schools, Medical Feedback Liver Failure, Acute Education Thematic Analysis Student Knowledge Rules and Regulations Confusion Educational Theory Conceptual Framework Students, Undergraduate Comparative Studies Descriptive Statistics Questionnaires Curriculum Cross Sectional Studies Data Analysis Software Clinical Competence Models, Educational Data Quality Natural Language Processing Learning Methods Misinformation Surgery, Operative Education ab: Background: Current research on generative AI in medical education focuses on AI's performance or risks, such as unreliability. We argue these issues are not isolated flaws but are symptoms of systemic contradictions that emerge when a technology is introduced into a learning environment. To move beyond descriptive reports, a theoretical framework is necessary to analyze the systemic tensions that arise during generative AI integration. Methods: A total of 141 first-year clerkship medical students used ChatGPT and provided qualitative data, including conversations with ChatGPT, evaluations of the generative AI's responses, and free-text feedback after watching concept videos of 'Acute Liver Failure'. We employed inductive thematic analysis to identify initial patterns, followed by a deductive analysis using Cultural-Historical Activity Theory to identify and interpret systemic contradictions. Results: The analysis revealed four contradictions within the activity system: 1) a conflict between the Tool's (ChatGPT's) unreliability and the Object of achieving accurate knowledge; 2) a skills gap between the Subject's (students') initial questioning abilities and the Tool's operational demand; 3) an unstable Division of Labor (student-AI) that conflicted with professional Rules, creating a demand for the need for expert validation; and 4) ambiguous Rules that created confusion and conflicted with professional norms. Conclusions: Challenges like AI unreliability and skill gaps are contradictions that function as catalysts for expansive learning. Resolving these tensions requires systemic transformation, including formalizing prompt engineering training and redefining the educator's role from an information provider to an essential expert validator within a new collaborative practice. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|