From Intention to Text: AI-Supported Goal Setting in Academic Writing
- URL: http://arxiv.org/abs/2604.15800v1
- Date: Fri, 17 Apr 2026 08:01:52 GMT
- Title: From Intention to Text: AI-Supported Goal Setting in Academic Writing
- Abstract summary: WriteFlow is an AI voice-based writing assistant designed to support reflective academic writing through goal-oriented interaction.<n>We discuss design implications for AI writing systems that prioritize reflective dialogue, flexible goal structures, and multi-perspective feedback to support intentional and agentic writing.
- Score: 3.589282565450899
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This study presents WriteFlow, an AI voice-based writing assistant designed to support reflective academic writing through goal-oriented interaction. Academic writing involves iterative reflection and evolving goal regulation, yet prior research and a formative study with 17 participants show that writers often struggle to articulate and manage changing goals. While commonly used AI writing tools emphasize efficiency, they offer limited support for metacognition and writer agency. WriteFlow frames AI interaction as a dialogic space for ongoing goal articulation, monitoring, and negotiation grounded in writers' intentions. Findings from a Wizard-of-Oz study with 12 expert users show that WriteFlow scaffolds metacognitive regulation and reflection-in-action by supporting iterative goal refinement, maintaining goal-text alignment during drafting, and prompting evaluation of goal fulfillment. We discuss design implications for AI writing systems that prioritize reflective dialogue, flexible goal structures, and multi-perspective feedback to support intentional and agentic writing.
Related papers
- Moving Beyond Review: Applying Language Models to Planning and Translation in Reflection [17.23739251695297]
We propose the first application of large language models (LLMs) to the planning and translation steps of reflective writing.<n>Pensée is a tool to explore the effects of explicit AI support during these stages by scaffolding structured reflection planning using a conversational agent.<n>Results show significantly greater reflection depth and structural quality when learners receive support during planning and translation stages of Cognitive Process Theory of writing.
arXiv Detail & Related papers (2026-03-30T15:42:38Z) - Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review [53.99984738447279]
Recent work frames this task as automatic text generation, underusing author expertise and intent.<n>We introduce REspGen, a generation framework that integrates explicit author input, multi-attribute control, and evaluation-guided refinement.<n>To support this formulation, we construct Re$3$Align, the first large-scale dataset of aligned review-response--revision triplets.
arXiv Detail & Related papers (2026-01-19T14:07:10Z) - Exposía: Academic Writing Assessment of Exposés and Peer Feedback [56.428320613219306]
We present Exposa, the first public dataset that connects writing and feedback assessment in higher education.<n>We use Exposa to benchmark state-of-the-art open-source large language models (LLMs) for two tasks: automated scoring of (1) the proposals and (2) the student reviews.
arXiv Detail & Related papers (2026-01-10T11:33:26Z) - Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process [17.275752224799497]
We identify four overarching design strategies for AI writing support.<n>Our findings reveal that writers' desired levels of AI intervention vary across the writing process.<n>Our findings offer actionable design guidance for developing human-centered writing tools for co-writing with AI.
arXiv Detail & Related papers (2025-04-16T21:05:46Z) - Voice Interaction With Conversational AI Could Facilitate Thoughtful Reflection and Substantive Revision in Writing [23.89410974646694]
Writing well requires not only expressing ideas but also refining them through revision, a process facilitated by reflection.<n>Recent advancements in multi-modal large language models (LLMs) now offer new possibilities for supporting interactive and expressive voice-based reflection in writing.<n>We argue that voice-based interaction can naturally facilitate this conversational exchange, encouraging writers' engagement with higher-order concerns.
arXiv Detail & Related papers (2025-04-11T16:54:12Z) - "It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models [97.22914355737676]
We examine whether and how writers want to preserve their authentic voice when co-writing with AI tools.
Our findings illuminate conceptions of authenticity in human-AI co-creation.
Readers' responses showed less concern about human-AI co-writing.
arXiv Detail & Related papers (2024-11-20T04:42:32Z) - Creativity Support in the Age of Large Language Models: An Empirical
Study Involving Emerging Writers [33.3564201174124]
We investigate the utility of modern large language models in assisting professional writers via an empirical user study.
We find that while writers seek LLM's help across all three types of cognitive activities, they find LLMs more helpful in translation and reviewing.
arXiv Detail & Related papers (2023-09-22T01:49:36Z) - VISAR: A Human-AI Argumentative Writing Assistant with Visual
Programming and Rapid Draft Prototyping [13.023911633052482]
VISAR is an AI-enabled writing assistant system designed to help writers brainstorm and revise hierarchical goals within their writing context.
It organizes argument structures through synchronized text editing and visual programming, and enhances persuasiveness with argumentation spark recommendations.
A controlled lab study confirmed the usability and effectiveness of VISAR in facilitating the argumentative writing planning process.
arXiv Detail & Related papers (2023-04-16T15:29:03Z) - Few-Shot Stance Detection via Target-Aware Prompt Distillation [48.40269795901453]
This paper is inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners.
PLMs can provide essential contextual information for the targets and enable few-shot learning via prompts.
Considering the crucial role of the target in stance detection task, we design target-aware prompts and propose a novel verbalizer.
arXiv Detail & Related papers (2022-06-27T12:04:14Z) - Revise and Resubmit: An Intertextual Model of Text-based Collaboration
in Peer Review [52.359007622096684]
Peer review is a key component of the publishing process in most fields of science.
Existing NLP studies focus on the analysis of individual texts.
editorial assistance often requires modeling interactions between pairs of texts.
arXiv Detail & Related papers (2022-04-22T16:39:38Z) - Structural Pre-training for Dialogue Comprehension [51.215629336320305]
We present SPIDER, Structural Pre-traIned DialoguE Reader, to capture dialogue exclusive features.
To simulate the dialogue-like features, we propose two training objectives in addition to the original LM objectives.
Experimental results on widely used dialogue benchmarks verify the effectiveness of the newly introduced self-supervised tasks.
arXiv Detail & Related papers (2021-05-23T15:16:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.