A qualitative study with 22 creative writers finds that the reflective value of AI refusals depends on alignment with users' situational thinking phases, cognitive beliefs, and views of AI roles.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
years
2026 5verdicts
UNVERDICTED 5representative citing papers
SynDocDis generates synthetic physician-to-physician dialogues from metadata using LLMs and achieves high physician-rated quality in oncology and hepatology scenarios.
13 participants became convinced AI understands human values after chatbot interactions evaluated with the VAPT toolkit.
The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.
Binary groundedness judgments in AI evaluations should be replaced by a reader-centered taxonomy of support relations that distinguishes syntactic and interpretive moves between generated statements and source documents.
citing papers explorer
-
Beyond Compliance: How AI Could Help Creative Writers by Refusing Them
A qualitative study with 22 creative writers finds that the reflective value of AI refusals depends on alignment with users' situational thinking phases, cognitive beliefs, and views of AI roles.
-
SynDocDis: A Metadata-Driven Framework for Generating Synthetic Physician Discussions Using Large Language Models
SynDocDis generates synthetic physician-to-physician dialogues from metadata using LLMs and achieves high physician-rated quality in oncology and hepatology scenarios.
-
AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations
13 participants became convinced AI understands human values after chatbot interactions evaluated with the VAPT toolkit.
-
Addressing the Synergy Gap: The Six Elements of the Design Space
The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.
-
From Binary Groundedness to Support Relations: Towards a Reader-Centred Taxonomy for Comprehension of AI Output
Binary groundedness judgments in AI evaluations should be replaced by a reader-centered taxonomy of support relations that distinguishes syntactic and interpretive moves between generated statements and source documents.