Team-based learning and its impact on clinical reasoning in medical education: A review and the unexplored role of artificial intelligence.
- 1Department of ENT, All India Institute of Medical Sciences, Bibinagar, Telangana, India.
- 2Department of Anatomy, All India Institute of Medical Sciences, Bibinagar, Telangana, India.
Abstract
Team-based learning (TBL) is widely adopted in medical education to promote active learning, collaboration, and improved clinical reasoning. Although digital platforms are increasingly integrated into TBL, the use of artificial intelligence (AI) in this context remains largely theoretical. We systematically reviewed empirical studies evaluating the effectiveness of TBL on clinical reasoning skills among medical students. Searches were conducted using PubMed with Boolean operators targeting terms such as "team-based learning," "clinical reasoning," "AI," and "medical education." After screening 21 studies, 16 met the inclusion criteria. Data were extracted on study design, participants, TBL interventions, assessment methods, and presence of digital or AI tools. Of the 16 studies included, none employed true AI-based interventions. Although three studies incorporated digital platforms such as Tronclass, InteDashboard, or the Learning Activity Management System, these lacked adaptive, intelligent functionalities characteristic of AI. Most studies reported that TBL significantly improved clinical reasoning outcomes, assessed through validated tools such as the Script Concordance Test, readiness assurance tests, or final examinations. However, variability in study design and outcome measures limited cross-comparability. TBL appears effective in enhancing clinical reasoning among medical students, yet no current study has explored its integration with AI. This highlights a critical research gap and the need for future studies evaluating AI-driven, personalized learning tools within TBL frameworks.
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BibTeX
@article{shrikrishna2026teambased,
title = {{Team-based learning and its impact on clinical reasoning in medical education: A review and the unexplored role of artificial intelligence.}},
author = {B H Shrikrishna and G Deepa},
journal = {Journal of education and health promotion},
year = {2026},
month = sep,
doi = {10.4103/jehp.jehp_1218_25},
url = {https://europepmc.org/article/MED/42924330},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Team-based learning and its impact on clinical reasoning in medical education: A review and the unexplored role of artificial intelligence.
AU - B H Shrikrishna
AU - G Deepa
PY - 2026
DA - 2026/09/30
JO - Journal of education and health promotion
DO - 10.4103/jehp.jehp_1218_25
UR - https://europepmc.org/article/MED/42924330
AB - Team-based learning (TBL) is widely adopted in medical education to promote active learning, collaboration, and improved clinical reasoning. Although digital platforms are increasingly integrated into TBL, the use of artificial intelligence (AI) in this context remains largely theoretical. We systematically reviewed empirical studies evaluating the effectiveness of TBL on clinical reasoning skills among medical students. Searches were conducted using PubMed with Boolean operators targeting terms such as "team-based learning," "clinical reasoning," "AI," and "medical education." After screening 21 studies, 16 met the inclusion criteria. Data were extracted on study design, participants, TBL interventions, assessment methods, and presence of digital or AI tools. Of the 16 studies included, none employed true AI-based interventions. Although three studies incorporated digital platforms such as Tronclass, InteDashboard, or the Learning Activity Management System, these lacked adaptive, intelligent functionalities characteristic of AI. Most studies reported that TBL significantly improved clinical reasoning outcomes, assessed through validated tools such as the Script Concordance Test, readiness assurance tests, or final examinations. However, variability in study design and outcome measures limited cross-comparability. TBL appears effective in enhancing clinical reasoning among medical students, yet no current study has explored its integration with AI. This highlights a critical research gap and the need for future studies evaluating AI-driven, personalized learning tools within TBL frameworks.
SN - 2277-9531
SN - 2319-6440
ER -