From Device Design to Systems Modeling: Evaluating Student Development in Diabetes Engineering Analysis

Engineering curricula often emphasize theoretical content while providing limited opportunities for students to engage with authentic engineering challenges, creating a gap between academic preparation and professional practice. Studio-based learning has emerged as a promising pedagogical response, yet its capacity to support systematic engineering thinking remains underexplored in Biomedical Engineering (BME) contexts. This study examines how the PRIME studio model— P hysiological system identification, R anked constraints, I nteractive block diagrams, M athematical modeling, and E vidence-based solutions—supports the application of engineering principles in diabetes-focused design challenges and how student work differs across intentionally scaffolded sophomore and junior implementations. We analyzed final design presentations from diabetes-focused studio challenges across sophomore ( n = 6 teams) and junior ( n = 12 teams) BME courses. The assignments were intentionally differentiated according to students’ curricular preparation: sophomore work emphasized conceptual and qualitative systems analysis, whereas junior work explicitly incorporated mathematical and computational modeling. Student work was evaluated across three dimensions using systematic coding with high inter-rater reliability (AC1= 0.733–1.000): constraint development, block-diagram complexity, and solution-analysis alignment. Mann–Whitney U tests assessed course-level differences in ordinal rubric scores, with Bonferroni-adjusted p values reported for the block diagram and solution-analysis alignment comparison families. All teams demonstrated application of the PRIME studio model, with selective course-level differences across the assessed dimensions. Junior teams demonstrated higher constraint specificity than sophomore teams (Mdn=3 vs. 1; p < 0.001, r = 0.70) and stronger block-diagram performance in Variables and Constants (Mdn=3.5 vs. 0; Bonferroni-adjusted p = 0.003, r = 0.82), Component Interactions (Mdn=3 vs. 2; Bonferroni-adjusted p = 0.002, r = 0.85), and Systems Thinking (Mdn=3 vs. 1.5; Bonferroni-adjusted p = 0.005, r = 0.79). Consistent with the intentionally differentiated assignment expectations, Quantitative Support was higher among junior teams, who were required to provide quantitative modeling and computational evidence, than among sophomore teams, whose assignment emphasized conceptual and qualitative analysis (Mdn=3 vs. 0; Bonferroni-adjusted p < 0.001, r = 0.94). The findings illustrate how the PRIME studio model can be adapted across curricular levels to scaffold a shift from conceptual reasoning toward systematic quantitative analysis, offering BME education a scalable instructional approach for bridging clinical inspiration with analytical rigor.