As AI increasingly becomes part of the research discovery experience, the quality and depth of the underlying scholarly content matter more than ever. While many AI systems rely heavily on metadata, snippets, and abstracts, researchers often need the richer context found only in full text to properly understand methods, limitations, evidence, and nuance, as well as the ability to understand why that content is used by the AI and how to get to the Full Text Article.

This session explores why attribution to full-text scholarly content is becoming increasingly important for AI-assisted research and discovery. We will examine how context, attribution, provenance, and publisher visibility influence the quality and reliability of AI-generated outputs, and why preserving connections between researchers and scholarly sources matters in an AI-supported environment.