NLDATA: Supporting Human-Centric Data Exploration Through Semantics and Natural Language Interaction (Tutorial)

Data science increasingly drives decision-making across domains, yet the quality of these decisions depends not only on advanced computational methods but also on how effectively systems support human interpretation, exploration, and communication of data. This tutorial provides a structured, interactive introduction to designing human-centric data exploration tools that integrate semantics, natural language processing (NLP), and human-computer interaction (HCI) to enhance accessibility, trust, and transparency in intelligent interfaces. Drawing from research across the HCI, NLP, and visualization communities, participants will learn about research concerning the generation of meaningful visual encodings of data, applying NLP techniques for query interpretation and ambiguity resolution, and designing conversational and multimodal interfaces to support data exploration. Through guided case studies and research examples, this 1.5-hour session will demonstrate how human-centered design principles can be integrated into the data exploration interfaces, supporting adaptive defaults, mixed-initiative interaction, and intelligent query handling. The tutorial will also highlight emerging challenges and opportunities, including AI-augmented data workflows, semantic inferencing for unstructured data, retrieval-augmented generation (RAG), and the ethics of fairness, explainability, and user agency.