{"ID":2868928,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16128","arxiv_id":"2509.16128","title":"AnchoredAI: Contextual Anchoring of AI Comments Improves Writer Agency and Ownership","abstract":"Generative AI is increasingly integrated into writing support, yet current chat-based interfaces often obscure referential context and risk amplifying automation bias and overreliance. We introduce AnchoredAI, a novel system that anchors AI feedback directly to relevant text spans. AnchoredAI implements two key mechanisms: (1) an Anchoring Context Window (ACW) that maintains unique, context-rich references, and (2) an update-aware context retrieval method that preserves the intent of prior comments after document edits. In a controlled user study, we compared AnchoredAI to a chat-based LLM interface. Results show that AnchoredAI led to more targeted revisions while fostering a stronger agency metrics (e.g., control and ownership) among writers. These findings highlight how interface design shapes AI-assisted writing, suggesting that anchoring can mitigate overreliance and enable more precise, user-driven revision practices.","short_abstract":"Generative AI is increasingly integrated into writing support, yet current chat-based interfaces often obscure referential context and risk amplifying automation bias and overreliance. We introduce AnchoredAI, a novel system that anchors AI feedback directly to relevant text spans. AnchoredAI implements two key mechani...","url_abs":"https://arxiv.org/abs/2509.16128","url_pdf":"https://arxiv.org/pdf/2509.16128v1","authors":"[\"Martin Lou\",\"Jackie Crowley\",\"Samuel Dodson\",\"Dongwook Yoon\"]","published":"2025-09-19T16:25:30Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Large Language Model\"]","has_code":false}