AI-supported PBL cohorts produced sixteen student-authored peer-reviewed publications and consistently high peer evaluations¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? During 2021–2023, student teams produced sixteen peer-reviewed publications, and peer evaluations ranged from 88.80% to 94.60% across courses. → Micky C. Nnamdi 2026

Evidence¶

Micky C. Nnamdi 2026¶

Micky C. Nnamdi, J. Ben Tamo, Benoit Marteau, Wenqi Shi, May D. Wang. (2026). Advancing Problem-Based Learning in Biomedical Engineering in the Era of Generative AI. arXiv. https://arxiv.org/abs/2503.16558 doi:10.48550/arxiv.2503.16558

q2 · i? · design · r2

Three-year case study (2021–2023) across three courses with 248 students in 62 interdisciplinary teams. Table III reports per-year publications and peer-evaluation percentages; text states BHI evaluations "ranged from 89.80% to 94.60%" and MIP "between 88.80% and 90.60%".

"the PBL implementation also yielded sixteen peer-reviewed publications in credible technical conferences by student teams addressing biomedical-AI problems"

Discussion¶

Related Claims¶

• More years of coding experience were associated with better performance in AI-supported PBL courses (exploratory) — related
• MLME student grades improved across the three course years, shifting from a 60–85 range with a median around 75 in Fall 2021 to concentrations in the high 80s and low 90s by Fall 2023 — related
• The overall effect of PBL in the tutor-background meta-analysis is modest (g = 0.27) with large, statistically significant heterogeneity — related