Academic Journal
Problem-based learning in the age of generative AI: A structured blueprint for medical curricula.
| Τίτλος: | Problem-based learning in the age of generative AI: A structured blueprint for medical curricula. |
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| Συγγραφείς: | Safarov R; University Hospital Magdeburg, University Clinic for Cardiac and Thoracic Surgery, Magdeburg, Germany. Electronic address: rauf.safarov@med.ovgu.de., Wippermann J; University Hospital Magdeburg, University Clinic for Cardiac and Thoracic Surgery, Magdeburg, Germany., Meyer F; University Hospital Magdeburg, University Hospital for General, Abdominal, Vascular and Transplant Surgery, Magdeburg, Germany., Wacker M; University Hospital Magdeburg, University Clinic for Cardiac and Thoracic Surgery, Magdeburg, Germany. |
| Πηγή: | Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen [Z Evid Fortbild Qual Gesundhwes] 2026 Jun; Vol. 203, pp. 87-96. Date of Electronic Publication: 2026 Jun 01. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Urban & Fischer Country of Publication: Netherlands NLM ID: 101477604 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2212-0289 (Electronic) Linking ISSN: 18659217 NLM ISO Abbreviation: Z Evid Fortbild Qual Gesundhwes Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam ; New York : Elsevier Urban & Fischer |
| Ιατρικοί όροι (MeSH): | Problem-Based Learning*/methods , Curriculum*/trends , Education, Medical*/trends, Humans ; Generative Artificial Intelligence ; Clinical Competence ; Self-Directed Learning as Topic |
| Περίληψη: | Background: Problem-based learning (PBL) is a well-established approach in medical education. In small groups, students work on real clinical cases with a high level of self-responsibility. Rooted in constructivist learning theory and the Socratic method, PBL promotes active knowledge construction, clinical reasoning, self-directed learning, and motivation. Instructors act as facilitators, guiding the group without dominating, which fosters independent learning and peer collaboration. Method: Narrative review including novel conceptual ideas on PBL using the options of AI plus representative references from the scientific literature and initial personal experiences from teaching practice in human medicine. Results (corner Points): Extensive evidence shows that PBL is particularly effective in enhancing clinical skills, critical thinking, and student satisfaction. However, its impact on theoretical knowledge remains mixed. Research suggests that PBL is more effective for advanced students who already possess a foundational knowledge base and are capable of self-directed learning. This highlights the importance of aligning PBL with the learner's developmental stage through thoughtful curriculum design. The rapid advancement of artificial intelligence (AI) offers new opportunities to enhance the PBL model. AI-driven tools have the potential to support personalized learning, provide real-time feedback, and simulate complex clinical scenarios, bringing PBL to a new level of effectiveness and adaptability in modern medical education. Another significant potential of generative AI, particularly when combined with effective prompt engineering, lies in its ability to efficiently generate realistic clinical scenarios. This can greatly support tutors by reducing the time and effort required to design complex case studies. AI-generated scenarios can be reviewed and refined by educators, ultimately leading to a diverse and adaptable series of clinical cases. Moreover, generative AI offers the possibility not only to create the textual content of clinical cases but also to enhance them through multimedia elements. By incorporating dynamic visuals, audio, and video materials, AI can help create immersive and engaging learning environments. This makes clinical case discussions more realistic, interactive, and stimulating for students, further enriching the PBL experience. Conclusion: The combination of established teaching methods such as PBL with the innovations of generative AI opens up new possibilities for a modern era of medical education. While human mentoring and tutoring remain essential, the integration of AI can enhance the PBL approach and make it more engaging and dynamic for students. Further medical education research, especially prospective studies, is necessary to test this hypothesis and explore how AI-supported PBL can be developed and implemented effectively. Such research could pave the way for taking the PBL concept to the next level. (Copyright © 2026. Published by Elsevier GmbH.) |
| Contributed Indexing: | Keywords: Artificial intelligence (AI); Human mentoring and tutoring; Humanes Mentoring und Tutorenschaft; Künstliche Intelligenz (KI); Large Language Models (LLMs); Large language models (LLMs); Medical education; Medizinische Bildung; Problem-based learning (PBL); Problembasiertes Lernen (PBL) |
| Entry Date(s): | Date Created: 20260601 Date Completed: 20260620 Latest Revision: 20260625 |
| Update Code: | 20260626 |
| DOI: | 10.1016/j.zefq.2026.03.012 |
| PMID: | 42225450 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2212-0289 |
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| DOI: | 10.1016/j.zefq.2026.03.012 |