Voice Interaction With Conversational AI Could Facilitate Thoughtful Reflection and Substantive Revision in Writing (2025)

Abstract: Writing well requires not only expressing ideas but also refining them through revision, a process facilitated by reflection. Prior research suggests that feedback delivered through dialogues, such as those in writing center tutoring sessions, can help writers reflect more thoughtfully on their work compared to static feedback. Recent advancements in multi-modal large language models (LLMs) now offer new possibilities for supporting interactive and expressive voice-based reflection in writing. In particular, we propose that LLM-generated static feedback can be repurposed as conversation starters, allowing writers to seek clarification, request examples, and ask follow-up questions, thereby fostering deeper reflection on their writing. We argue that voice-based interaction can naturally facilitate this conversational exchange, encouraging writers’ engagement with higher-order concerns, facilitating iterative refinement of their reflections, and reduce cognitive load compared to text-based interactions. To investigate these effects, we propose a formative study exploring how text vs. voice input influence writers’ reflection and subsequent revisions. Findings from this study will inform the design of intelligent and interactive writing tools, offering insights into how voice-based interactions with LLM-powered conversational agents can support reflection and revision.

## introduction writing for effective communication requires more than just expressing thoughts; it demands transforming those thoughts to meet the expectations of an audience @xcite . this transformation is achieved through revision, which we define as any changes made to written content at any time, including both meaning-preserving edits and meaning-changing adjustments @xcite . writing research suggests that substantive revision is facilitated through reflection @xcite , in which writers critically examine their work from an external perspective to evaluate its effectiveness in addressing their rhetorical situation and fulfilling their communication goals @xcite . however, writers often suffer from the curse of knowledge bias, which prevents them from reading their text as their audience would @xcite . consequently, feedback from others is a common way to facilitate reflection @xcite . for example, many higher education institutions operate writing centers where tutors provide nonprescriptive and non-corrective feedback through conversational exchanges, encouraging writers to prioritize addressing higher-order concerns, such as thesis (or focus), audience engagement, organization, and content development, rather than lower-order concerns, such as grammar and syntax (purdue online writing lab, n.d.; @xcite @xcite . this dialogue with the tutors helps writers gain critical distance from their work and make independent and substantive revisions. previous work has demonstrated user interface (ui) affordances that enable writers to use large language models (llms) to generate personalized and contextually adaptive feedback, questions, and advice, to facilitate reflection @xcite . however, these systems primarily support the generation of static feedback, questions, and advice, and lack the ui support for conversational exchanges characteristic of human tutoring. yet, as highlighted by gero et al. (2023) (in the second paragraph of section 4.2.3), useful feedback comes from a back-and-forth conversational exchange, allowing the writers to clarify, ask follow-up questions, and refine their work based on the discussion. this discussion not only deepens the writer's understanding of feedback but also enables co-construction of meaning, which has been shown to improve feedback uptake and lead to sub-stantive revisions @xcite . furthermore, the input modality of interaction may significantly influence how people reflect on their writing through dialogue with llm-powered conversational agents. a study by @xcite showed that when co-authors gave feedback to each other on a collaborative writing task using spoken annotation, it imposed a lower cognitive load than written annotations, giving them more mental capacity to focus on higher-order concerns. however, there is limited evidence on how the modality of the writer's input, particularly spoken vs. written, impacts the quality of reflection when interacting with llm-powered conversational agents. recent advancements in multi-modal llms highlight their ability to understand and interpret multi-modal instructions and generate outputs across different modalities, including text and speech @xcite . commercially available multi-modal llms, such as gpt-4o (openai, 2024a) and gemini 2.0 flash @xcite , demonstrate high accuracy and low latency in understanding and generating outputs across different modalities. this allows interaction designers to build both highly interactive (i.e., responding quickly and appropriately) and expressive (i.e., conveying emotions, social context, and nuanced meaning through non-verbal cues such as voice inflection, pitch, and tone) @xcite conversational interfaces in ways that were not possible before. these advancements make our exploration of voice interactions with llm-powered conversational agents timely. we argue that the input modality, whether writers respond to feedback through voice or text, can influence the quality of their reflection. specifically, we hypothesize that responding to feedback from an llm-powered conversational agent using voice can (1) promote greater engagement with higher-order concerns, (2) facilitate more iterative refinement of one's reflections, and (3) reduce the cognitive load associated with reflection, compared to written input. to investigate this, we propose a formative study exploring how spoken versus written input affects writers' reflection and subsequent revision. ## formative study consider a writer who has completed a rough draft of an argumentative essay and wants to revise it to better meet audience expectations. before revising, they need to reflect on their work to set specific revision goals. they turn to reflection support systems, such as textfocals @xcite or impressona @xcite , seeking feedback, questions, and advice to help them reflect on their work and make decisions about what to revise. however, writers prefer feedback containing specific examples rather than vague comments. for example, one participant using impressona noted that the system "just tells me i have to do this and it doesn't come up with the specific examples" (as said by p11 in @xcite ). this illustrates a limitation in the aforementioned reflection support systems. as zhao et al. (2024) and gero et al. (2023) highlight, feedback is more useful when it is part of an interactive conversational exchange, allowing writers to seek clarification, request examples, and ask follow-up questions. this suggests an interaction design opportunity to repurpose static feedback, questions, and advice as conversation starters. writers can use these starters to initiate reflective dialogues with llm-powered conversational agents, to seek clarification, request examples, and ask follow-up questions, thus fostering thoughtful reflection. we argue that the speech modality can naturally facilitate this conversational exchange. prior research has shown that in collaborative writing, when co-authors provided feedback to each other using spoken annotations, the expressivity of spoken annotations encouraged a greater focus on higher-order concerns and promoted self-correction of the provided feedback compared to written annotations @xcite . this motivates the following research questions: rq1: how might speaking to llm-powered conversational agents, compared to typing, influence the depth and kinds of concerns writers reflect on in their work? rq2: how does engaging in spoken conversations with llm-powered conversational agents, as opposed to text-based interactions, shape the way writers refine and revisit their own reflections? furthermore, @xcite suggested that the benefits of the speech modality might stem from spoken language production being less cognitively demanding than written language production @xcite . this relative ease could allow writers to allocate more cognitive resources to their reflections. however, the cognitive demands of reflection itself, regardless of the modality, may also be significant. this raises the question: rq3: how do writers perceive the cognitive demands of speaking to llm-powered conversational agents, compared to typing, and what factors influence these perceptions? finally, given the association between reflection and revision in writing @xcite , it is important to examine whether reflective dialogues with llm-powered conversational agents lead to actionable changes in writing. this raises the question: rq4: how does reflecting with llm-powered conversational agents influence the extent and depth of revisions in written content? ## expected contributions through our formative study, we aim to inform the design of intelligent and interactive writing tools that support reflection through conversational exchanges with llm-powered conversational agents. specifically, we expect to make the following contributions: (1) providing evidence on the impact of speech modality in facilitating thoughtful reflection and (2) exploring the opportunity to transform static llm-generated feedback, questions, and advice into dynamic conversational exchanges that encourage reflection and subsequent revision.