The paper proposes a proactive writing support system that infers users’ needs from their writing interactions, using Flower and Hayes’ cognitive process theory as a framework. By mapping six cognitive processes to 14 specific support types and associated interaction behaviors, the authors develop AToM CoWriter, which offers context-aware suggestions without requiring explicit prompts. Two within-subjects studies with 21 participants show that this approach improves expressiveness, idea exploration, and engagement with proactive suggestions.
Writing involves diverse cognitive activities, from ideation to revision, and writers' needs vary across individuals and moments. Proactive AI promises to provide the right support at the right time,...
arXiv:2609.01588v1 Announce Type: cross Abstract: Writing involves diverse cognitive activities, from ideation to revision, and writers' needs vary across individuals and moments. Proactive AI promis...
Pragmatic language use requires reasoning about alternatives: the alternative expressions a speaker might have chosen, or the alternative interpretations a listener might entertain. Formal and computational models of pragmatics must therefore specify the sets of alternatives that interlocutors reason over, which is often done through manual specification.
arXiv:2605. 05598v2 Announce Type: replace Abstract: The proliferation of large language models (LLMs) in educational settings has paradoxically undermined the cognitive processes they purport to support.
arXiv:2607. 00009v1 Announce Type: cross Abstract: Despite the remarkable proficiency of large language models (LLMs) in basic writing assistance, their utility in creative writing is fundamentally hindered by a persistent binary failure.
arXiv:2601. 07121v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used not only for problem solving but also for creative ideation; however, generating ideas that are both novel and coherent remains challenging.
arXiv:2606. 11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research.
The House with a Million Windows (HWAMW) is an LLM-based interactive fiction system that lets users narrate a story and then view it through a series of AI-generated "windows" that reframe the narrative in various literary styles. The system is grounded in the psychological restorying intervention, aiming to deepen users' exploration of meaning in their personal stories. Empirical results indicate that HWAMW enhances users' sense of narrative identity, and expert reviews suggest it achieves this by facilitating restorying rather than simply generating new content.
arXiv:2604. 22027v2 Announce Type: replace-cross Abstract: One of the most common complaints about large language models (LLMs) is their prompt sensitivity -- that is, the fact that their ability to perform a task or provide a correct answer to a question can depend unpredictably on the way the question is posed.
arXiv:2606. 12350v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) raises critical questions about human creativity and individual expression in an era of AI-assisted creation.
The paper introduces SWIM, a task that frames student writing simulation as proficiency‑conditioned essay generation. It evaluates prompting, supervised fine‑tuning, and reinforcement learning for aligning generated essays with student proficiency profiles, using automated essay scoring as a metric. Results show that prompting alone offers limited control, while supervised fine‑tuning and reinforcement learning significantly improve alignment across content, lexical, grammatical, and organizational traits, though low‑proficiency writing remains difficult to replicate.
arXiv:2602. 09907v2 Announce Type: replace-cross Abstract: College students increasingly use AI chatbots to support academic reading, yet we lack granular understanding of how these interactions shape their reading experience and cognitive engagement.