Current resources for data literacy education, such as visualization galleries and datasets, provide useful examples but lack mechanisms for learners to query, compare, and navigate the visualization design space efficiently. This position paper advocates for visualization retrieval as essential infrastructure for data literacy, transforming static collections into dynamic, inquiry-based learning environments. We analyze the role of retrieval across the data lifecycle, demonstrating how it facilitates design space exploration and vocabulary expansion, supports data consumption through visualization comparison and critique, and aids data management via resource curation. We outline key opportunities for future research and system design, including integrated retrieval-authoring environments, pedagogical relevance modeling, and collaborative educational corpora. Ultimately, we argue that visualization retrieval systems empower learners to articulate intent, bridge technical barriers, and proactively reason with data.
Nguyen et al. (Thu,) studied this question.