Using AI to Bridge Technology Gaps in Higher Education

Universities rely on enterprise technology platforms to deliver essential services, yet vendor-provided systems often impose rigid constraints that limit institutional responsiveness to pedagogical needs. This paper presents a learning engineering approach to bridging technology gaps through AI-augmented local solutions. Following the learning engineering (LE) process (Goodell et al., 2023), we addressed a structural limitation in our survey platform that prevented longitudinal analysis of student self-assessments. Through human-centered stakeholder analysis (Schatz et al., 2022; Thai et al., 2022), we identified requirements for visualizing individual student growth on Program Learning Objectives (PLOs) over time. We then leveraged large language models to develop a FERPA-compliant, locally-run pipeline that transforms fragmented course-level survey data into longitudinal student trajectories. Our contribution is twofold. First, we demonstrate how institutions can create adaptive layers within existing ecosystems that address context-specific needs and function as tech augmenters instead of passive consumers. Second, we provide practitioners with a framework guiding them through friction identification, feasibility assessment, process decomposition, and implementation criteria including data governance, maintainability, and scalability. Implementation revealed three key affordances: (1) faculty gained visibility into individual learning trajectories previously obscured by course-level aggregates, (2) curricular gaps became evident through cross-course PLO progression mapping, and (3) accreditation documentation improved through evidence-based growth narratives. This work extends learning engineering practice by demonstrating how readily available AI tools can enable local innovation within complex institutional technology environments, offering a replicable approach for institutions facing similar constraints.