Mentored Project Extensions Linked to Stronger Career Readiness in Biomedical Engineering

Biomedical engineering departments have achieved uneven results in preparing students for immediate entry into the health technology workforce, with graduates from these programs securing engineering-related positions at lower rates than their peers in other engineering disciplines. A nine-year program evaluation now describes a countermeasure that requires no redesign of the core curriculum. Instructors created a co-curricular program called Biodesign NEXT that provides funding and mentorship so small teams of students can continue working on their course-based projects after the semester ends.

A retrospective survey of program alumni, published in Biomedical Engineering Education, reports that 85 percent of respondents said the program helped prepare them for the workforce, and 90 percent said it increased their interest in a career in health technology innovation. The program earned a net promoter score of 83, a loyalty metric the authors note is in the range typically achieved by top-rated offerings. The scale of the underlying effort is easy to underestimate.

Over nine academic years, 219 students participated in the program after completing project-based capstone and interdisciplinary courses. The evaluation team compiled contact information for all of them, tracking alumni down on LinkedIn when email addresses went stale, and managed to send the 31-question Qualtrics survey to 210 of the 219 participants. Forty complete responses came back, a 22.4 percent response rate that the authors themselves flag as a limitation.

Half of the respondents were continuing their formal education at the time of the survey; the other half were in the workforce, and of those, 15 of 20 reported being in engineering-related careers. Respondents spanned all nine program years and every degree level, from six who had not yet completed a degree to one MD and three PhDs, with the largest subgroup being 22 undergraduates pursuing a bachelor's in bioengineering. Because every participant rated all skills on a five-point Likert scale, the authors treated the responses as paired ordinal data and ran Wilcoxon signed-rank tests, comparing each skill against every other within its domain and testing each participant's composite technical score against their composite professional score.

To account for the large number of pairwise comparisons, they controlled the false discovery rate at q = 0.05 using the Benjamini–Hochberg procedure, applied separately to the technical and soft skill families, with all tests two-tailed at a nominal alpha of 0.05. The free-text comments were processed through a GPT-4.1 workflow on a protected Azure OpenAI endpoint governed by a Business Associate Agreement, generating sentiment, tone, summaries, and theme tags against a predefined schema, with low-confidence classifications routed to human reviewers for consensus resolution. The university's IRB determined the project did not qualify as human subjects research.

The pattern that emerged from those tests was consistent and specific. Respondents rated their gains in need finding and validation significantly higher than in regulation, payment and reimbursement, business model development, networking, and project budgeting, with a mean difference of 0.93 against regulation (p = 0.0006). Prototyping beat regulation, reimbursement, business model development, and intellectual property, while project planning outscored regulation, reimbursement, and IP.

Regulation came out at the bottom, scoring significantly lower than project budgeting and networking. In plain terms, the program's perceived strengths lay in early-stage innovation competencies, such as finding and validating clinical needs and building prototypes, while commercial and implementation-related areas like reimbursement strategy, business model development, and regulatory execution were rated comparatively lower. On the professional side, teamwork scored significantly higher than collaboration with stakeholders, leadership, and problem solving, all at p = 0.0124, marking team-based collaboration as the program's relative strength.

One of the most consequential comparisons in the paper is between two composite scores. Respondents' mean soft skill rating was 4.596 with a standard deviation of 0.455, against 4.181 with a standard deviation of 0.488 for hard skills, a gap the Wilcoxon test put at p < 0.0001. The authors are careful with the wording: these are self-reported perceptions from a retrospective survey without a comparison group, so the data cannot establish that the program caused the gains.

But the direction of the effect is consistent with the program's design hypothesis, which held that a semi-structured, mentored continuation of real projects would deepen skills that assignment-driven coursework often leaves underdeveloped. Two secondary analyses add texture, and a note of caution, to the headline figures. First, promoters were not evenly distributed: 76.2 percent of respondents currently in the workforce qualified as promoters, compared with 89.5 percent of those still in school, a gap the authors suggest may reflect recency bias among students closer to their program days.

The temporal split tells a similar story, with 2015–2019 participants assigning more middling 7s and 8s than their 2020–2024 counterparts, which the authors attribute to possible early-stage growing pains combined with later program improvements. Second, 65.0 percent of respondents reported at least one tangible project outcome, such as prototype advancement, patent filings, follow-on funding, or company formation, and those with a tangible outcome were more likely to be promoters, 96.2 percent versus 57.1 percent among those without one. The authors explicitly label this association correlational and decline to claim that participation caused either the outcomes or the enthusiasm.

The qualitative feedback, coded separately for promoters and passive respondents, detailed what participants saw as the program's strengths and weaknesses. Both groups singled out mentorship as a key strength, praising dedicated guidance that shaped their learning and project development, and both valued the intellectual community of interdisciplinary peers and the freedom to steer their own projects. The constructive criticism clustered around resource access, particularly help reaching specific patient populations for user validation and better information about follow-on funding, and around structure, with requests for regular check-ins, defined deadlines, and clearer team accountability.

Promoters described an environment that fostered passion for healthcare innovation; passive respondents pointed to uneven engagement among teammates and difficulties validating their projects. Notably, the authors observed that students arrived attracted by the monetary award but tended to credit the ongoing mentorship as the most beneficial aspect afterward, raising the possibility that the funding element might be less essential than the mentoring, though they have sustained the awards while funds allow. Behind the results lies a program that iterated on itself using the kind of risk-retirement logic it teaches.

The instructors found that most teams never spent their full awards, so the second-quarter budget was cut from $2,500 to $1,000, with teams free to make a case for more; very few did. For technically advanced projects, the program added volunteer engineering coaches with industry experience, a change that appears to have encouraged more ambitious prototyping and experimentation among some teams. Team dynamics emerged as a variable largely outside the program's direct control, prompting a new requirement that at least two team members commit before an award is granted.

The authors are candid about fragility elsewhere: instructors receive no compensation or teaching offsets for the weekly or bi-weekly mentor meetings, and the entire program runs on philanthropic support that has become increasingly difficult to sustain. The authors plan a more rigorous evaluation, including a longitudinal design administered before and after participation and the possible involvement of industry hiring managers in its design and implementation, which could test whether student self-perceptions align with employer observations. They also intend to gather structured feedback from mentors about what differentiates the most productive teams and to explore whether different types of mentor support are associated with different project outcomes.

For a field where graduates secure engineering-related positions at lower rates than their peers, the prospect of an additive co-curricular intervention requiring no curriculum overhaul is the practical significance of this work. The 85 percent figure is self-reported, the sample of 40 respondents is small, and causation remains unproven, but the survey signal is directionally consistent across quantitative ratings, qualitative themes, and nine years of operational experience, and the authors commit to using these results to keep refining the program.

Subject of Research: A co-curricular biomedical engineering project extension program and its association with student-reported career readiness

Article Title: BME Project Extensions: Promoting Learning Beyond the Classroom and Improving Workforce Readiness

Article References: Lucian, L., Nerurkar, M., Bui, K., Fan, R., Venook, R., de Haaff, M., & Denend, L. (2026). BME Project Extensions: Promoting Learning Beyond the Classroom and Improving Workforce Readiness. Biomedical Engineering Education. https://doi.org/10.1007/s43683-026-00255-4

Image Credits: AI Generated

DOI: 10.1007/s43683-026-00255-4

Keywords: biomedical engineering education, career readiness, co-curricular programs, mentorship, capstone projects, health technology innovation, soft skills, net promoter score, Wilcoxon signed-rank test, survey research, workforce development, Biodesign NEXT