Real-Time, Context-Specific Educational Metadata Enrichment of Learning Resources on the Web

Abstract

Web resources rarely carry the educational metadata that teachers use to judge whether a resource fits their lesson, and dedicated educational repositories that do carry it cover only a fraction of what teachers find through web search. Closing this gap in real time, at the point of search, requires classifiers that are fast enough to run per query and that target a vocabulary specific to the teacher’s curriculum and grade structure, for which labelled training data is scarce. We investigate whether Large Language Model (LLM)-generated labels and paraphrases can train small, fast classifiers for this task. Across nine LRMI-aligned educational metadata properties for STEM education in Lower Saxony, Germany, matching model complexity to property difficulty reaches micro F1 > 0.8 for every property at inference latencies under 15ms, with models compact enough for client-side, browser-based deployment. We report this as preliminary, ongoing work: evaluation for six properties currently relies on LLM-generated labels validated against a 50-page human-annotated sample, and train/test splits do not yet separate resources by website, which we flag as open validity questions for the digital libraries community to weigh in on.