An Offline Architecture for Assessment-Driven Remediation Videos

We present an offline generative AI architecture for assessment-driven remediation videos. The pipeline maps course materials to learning objectives, authors assessments and reusable instructional components, then composes personalised videos from learners’ assessment results. By separating course-level preparation from learner-specific composition, the design aims to reuse instructional content and reduce repeated generation. Runtime measurements from individual stages project a 46% reduction in serial production time under the proposed reuse and precomputation strategy; this is a projection rather than an end-to-end production benchmark. The manuscript is in preparation, and learning effectiveness has not yet been evaluated.

Manuscript in preparation