Over the past decade, the field of artificial intelligence (AI) has witnessed significant advancements across domains such as natural language processing and computer vision. These advances have accelerated discoveries, innovations, and breakthroughs across interdisciplinary fields, including the Science of Science, which relies on large-scale data to uncover reproducible patterns governing the evolution of research topics, collaboration networks, knowledge diffusion, and systemic factors that drive scientific progress.

Science of Science is increasingly incorporating AI techniques into the analysis of large-scale data, yielding insights into longstanding and emerging problems. Building on the first and second AI4SciSci workshops, held with ICDM 2023 and JCDL 2025, this workshop will further engage the AI and Science of Science communities in discussion and research on frontier topics in this area.

The 2026 edition of the Workshop on Web Archiving and Digital Libraries (WADL) will explore the integration of web archiving and digital libraries. The workshop addresses the full life cycle of digital resources, including creation and authoring, uploading and publishing, crawling, indexing, exploration, and archiving.

It will also examine processes and tools for preserving non-traditional resources, including social media, scholarly and government datasets, 3D objects, and digital online art.

Artificial intelligence is reshaping libraries from traditional resource repositories into intelligent, adaptive, and user-centered knowledge service hubs. Across public, academic, and special libraries, AI is increasingly embedded in information discovery, reference services, knowledge organization, user interaction, decision support, and operational workflows.

These developments create new opportunities for digital library innovation, while raising questions about trust, accountability, data privacy, algorithmic fairness, professional ethics, and inclusive access. This workshop examines future libraries through the dual lenses of service transformation and governance challenges, bringing together researchers, practitioners, AI researchers, and policy-oriented stakeholders to develop sustainable, equitable, accountable, and human-centered approaches.

Scholarly metadata is essential to the scholarly ecosystem, supporting discovery, interoperability, and provenance. However, the substantial growth and diversification of research artifacts—including publications, datasets, source code, and machine-learning models—makes the collection of heterogeneous metadata features across repositories increasingly complex.

Large language models and agents are being widely adopted for tasks ranging from information extraction to knowledge-graph construction. Important questions remain about their accuracy, robustness, bias, and societal impact in research metadata, scholarly information extraction, and open science. This workshop explores these questions with a focus on metadata features from research artifacts, large language models, and knowledge graphs.

Web archive collections are increasingly large and heterogeneous, making it challenging for practitioners to choose efficient representations and workflows for common analytical tasks. This half-day hybrid tutorial provides a hands-on introduction to web-archiving formats, including WARC, CDX/CDXJ, Parquet, and derived formats such as WAT and WET, demonstrating how each format supports different kinds of analysis.

Using the End of Term Archive Datasets, comprising more than 2.5 PB, as a real-world testbed, the tutorial presents a three-tier model of analytical queries. Participants will access data on AWS S3, convert and query CDX(J) data in Parquet, use SQL through DuckDB and Python workflows, and perform link-structure and basic NLP tasks with WAT/WET. The tutorial emphasizes practical trade-offs among data formats, costs, and computational requirements.

AI for Organizing Digital Collections is a half-day tutorial introducing practical approaches for applying AI to organize textual, visual, and multimodal digital collections. It examines how large language models can support classification, subject analysis, and vocabulary mapping, and how vision-language models can support archival image description and visual subject-term assignment.

Through accessible explanations, demonstrations, and hands-on exercises, participants will learn how to train LLMs for digital collection organization, evaluate model outputs, and design AI-assisted workflows. The tutorial is intended for digital library researchers, metadata professionals, catalogers, LIS educators, students, and cultural heritage practitioners.