aiqbench analysis
This research proposes using Flower and Hayes' cognitive process theory to guide Large Language Models in proactively inferring user needs during creative writing, reducing the burden of explicit prompting. By mapping observable interaction behaviors to six specific cognitive processes, the AToM CoWriter system demonstrates improved expressiveness and idea exploration without requiring constant user intervention. The study offers a promising framework for making AI writing assistants more context-aware and less intrusive in ambiguous creative tasks.
Towards Cognitive Process-Aware Proactive Writing Support
arXiv:2608.30424v1 Announce Type: cross Abstract: Large language models can support writing, but existing tools require users to explicitly articulate prompts-particularly burdensome in creative writing, where intentions are often ambiguous. Proactive support that infers users' needs from writing interactions could alleviate this burden, but raises two challenges: determining what support to provide and when to intervene. This work focuses on the former. We hypothesize that Flower and Hayes' cognitive process theory of writing-which characterizes writing through six cognitive processes-offers an interpretable bridge between observable writing behavior and appropriate support types. Through a formative study and literature review, we identify 14 writing support types associated with these cognitive processes, along with characteristic interaction behaviors linked to each process. We then instantiate this framework in AToM CoWriter, which infers support needs from writing interactions and document context. Two within-subjects studies (N = 21) provide initial evidence that this approach improves expressiveness and idea exploration, and that cognitive process inference increases engagement with proactive suggestions. These findings suggest that cognitive processes can provide a promising basis for support selection in proactive writing systems.
Abstract:Large language models can support writing, but existing tools require users to explicitly articulate prompts-particularly burdensome in creative writing, where intentions are often ambiguous. Proactive support that infers users' needs from writing interactions could alleviate this burden, but raises two challenges: determining what support to provide and when to intervene. This work focuses on the former. We hypothesize that Flower and Hayes' cognitive process theory of writing-which characterizes writing through six cognitive processes-offers an interpretable bridge between observable writing behavior and appropriate support types. Through a formative study and literature review, we identify 14 writing support types associated with these cognitive processes, along with characteristic interaction behaviors linked to each process. We then instantiate this framework in AToM CoWriter, which infers support needs from writing interactions and document context. Two within-subjects studies (N = 21) provide initial evidence that this approach improves expressiveness and idea exploration, and that cognitive process inference increases engagement with proactive suggestions. These findings suggest that cognitive processes can provide a promising basis for support selection in proactive writing systems.
Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
arXiv:2608.30424 [cs.HC]
(or arXiv:2608.30424v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2608.30424 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 271 words
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