Zero-Cost Digital Automation for Scalable Assessment in a National Mechanical Ventilation Curriculum

Background As medical education transitions toward competency-based models, longitudinal programs emphasizing deliberate practice and repeated assessment are essential for mastery of complex concepts. Frequent formative assessment requirements, limited expert pools, and growing cohorts that outpace traditional infrastructure create substantial scalability constraints. Preceptorial programs, in which learners demonstrate mastery to expert educators, are particularly vulnerable. As enrollment expands, administrative demands for portfolio tracking, examination logistics, and case development fall disproportionately on the volunteer expert educators who sustain them. Objective We sought to implement a unified, zero-cost digital infrastructure to support scalable formative and summative assessment of a multi-center year-long mechanical ventilation preceptorial program while minimizing administrative burden for expert educators. Methods We implemented two complementary digital systems built on the Google ecosystem, requiring no institutional licensing or IT support. For formative tracking, we first deployed a standardized WP with defined milestone deadlines and a zero-cost automated tracking and reminder system. Completion of the fall milestone was compared between Fall 2024 (manual educator-dependent tracking and reminders) and Fall 2025 (standardized requirements plus automated tracking). For summative assessment, we also deployed an automatically graded multiple-choice case-based waveform examination (CBWE), operationalizing an expert-defined partial-credit scoring rubric. Results There was a 7.7-fold increase in the total number of waveforms submitted (194 images in Fall 2024 to 1498 images in Fall 2025). Portfolio submission rate rose from 45.6% (31/68 learners) in Fall 2024 to 68.5% (63/92 learners) in Fall 2025. Among learners who submitted their portfolios, the proportion submitting all required waveforms increased from 29.0% to 55.6%. Proof-of-concept automated CBWE grading confirmed scoring accuracy, eliminated 8.5 hours of manual grading per grader per cycle, generated item-level analytics, and demonstrated capacity for immediate individualized learner feedback. Conclusion A unified, zero-cost digital infrastructure addressed parallel administrative and content scarcity constraints limiting scalability of a national MV preceptorial program. By reallocating administrative burden away from expert faculty, these tools preserve the high-fidelity teaching relationship at the core of the preceptorial model while enabling it to operate at scale, ultimately providing a replicable model for competency-based assessment in diverse training environments.