Understanding Writing Assistants for Scientific Figure Captions: A Thematic Analysis (2025)

Abstract: Scientific figure captions are essential for communicating complex data but are often overlooked, leading to unclear or redundant descriptions. While many studies focus on generating captions as an ‘output’, little attention has been given to the writer’s process of crafting captions for scientific figures. This study examines how researchers use AI-generated captions to support caption writing. Through thematic analysis of interviews and video recordings with 18 participants from diverse disciplines, we identified four key themes: (1) integrating captions with figures and text, (2) bridging gaps between language proficiency and domain expertise, (3) leveraging multiple AI-generated suggestions, and (4) adapting to diverse writing norms. These findings provide actionable design insights for developing AI writing assistants that better support researchers in creating effective scientific figure captions.

## introduction and backgrounds scientific figures communicate complex data and concepts to readers in research papers @xcite . these figures are accompanied by captions, providing essential context and explanations to enhance the reader's understanding of the presented information @xcite . writing figure captions may seem straightforward, but many researchers overlook them, resulting in unclear explanations that confuse readers @xcite . crafting a good caption demands clarity, brevity, and alignment with the figure's purpose, making it more challenging than it appears. it requires specialized language and detailed explanations to effectively communicate abstract and complex scientific concepts @xcite . the difficulty of this task has contributed to the prevalence of low-quality captions in scientific literature @xcite , highlighting the need for improved approaches to caption writing. meanwhile, artificial intelligence (ai), especially large language models (llms), offers seemingly promising solutions for producing reasonable quality captions @xcite @xcite . for example, the scicap project @xcite compiled a large dataset of scientific figures and captions from arxiv papers to develop models for generating high-quality captions for scientific figures. many captiongeneration models have been proposed for scientific figures @xcite @xcite . despite these advancements, there remains a limited understanding of how ai-generated captions benefit writers of scholarly papers. while prior research has demonstrated that ai-generated captions are effective from a reader's perspective, as shown through human evaluation methods @xcite @xcite , their utility for writers has been underexplored from a human-computer interaction (hci) perspective. prior studies often only focused on readers' perspectives-having people evaluate ai-generated captions by providing ratings or feedback-rather than examining the writing process itself from the writers' perspective. recent efforts have started to address this gap. for instance, scicapenter showed that ai-generated captions can reduce cognitive load for writers @xcite , and another study investigated how different configurations and inputs improve caption generation to assist writers @xcite . however, these efforts emphasize quantitative measures, such as cognitive load or usability of ai outputs, and fall short of capturing qualitative, higher-level insights from practitioners engaged in the caption-writing process, which can guide the design of future writing assistants. this paper seeks to address this gap by examining how scholarly paper writers interact with 1 ai-generated captions during the writing process through a qualitative lens. we analyzed video recordings and transcripts from a think-aloud study @xcite in which participants rewrote figure captions for their previously published papers, as well as their post-study interview responses. using thematic analysis @xcite , guided by a design space for writing assistants proposed by @xcite , we tailored the framework to the unique context of scientific figure captions. we identified four main themes in the study data: (i) the multimodal and complex context inherent in figure caption writing, (ii) the gaps between domain-specific knowledge and linguistic expression, especially in describing complex scientific concepts in english, (iii) the diverse ways participants utilized ai-generated suggestions, and (iv) the variations in norms and conventions for figure captions across different academic disciplines. by identifying these challenges and insights, this paper seeks to bridge the gap between current ai capabilities and the specific needs of scientific writers, contributing to the advancement of more effective and intuitive writing assistance technologies. ## findings our analysis identified four key themes regarding researchers' experiences with ai-generated captions for scientific figure caption writing. these themes corresponded to the task, user, in-teraction, and ecosystem aspects of the guiding design space @xcite , while the technology aspect was less prominent in participants' concerns. below, we detail the four themes that emerged from our thematic analysis, noting their alignment with the relevant design space aspects. in the following, we used the participant labels (p1, p2, p3, etc.) from the original interview study. keeping these labels maintains consistency between our analysis and the prior work. ## discussion our analysis identifies four key themes that can guide the development of more effective writing assistants for scientific figure captions: (1) integrating captions with figures and text, (2) addressing gaps between language proficiency and domain expertise, (3) utilizing multiple ai-generated suggestions, and (4) accommodating diverse writing norms. in this section, we propose practical design recommendations for future captionwriting tools, using two illustrative examples to highlight strengths and limitations: scicapen-ter @xcite , which generates captions with quality ratings and contextual information to aid refinement (see appendix b, fig. 3 ), and figura11y @xcite , which focuses on accessibility by creating alt text drafts and offering interactive revision tools (see appendix c, fig. 4 ). by analyzing these systems, we identify gaps in current approaches and offer insights to guide the development of more versatile and user-centered ai writing assistants. ## conclusion and future work this study explored how researchers interact with ai-generated captions to improve scientific figure caption writing. by conducting thematic analysis of interviews and video recordings, we identified four key themes: (1) integrating captions with figures and text, (2) bridging gaps between language proficiency and domain expertise, (3) leveraging multiple ai-generated suggestions, and (4) adapting to diverse writing norms. these themes highlight the unique challenges of caption writing and provide actionable insights for designing ai writing assistants. by focusing on the writer's process rather than just the output, this research contributes to a deeper understanding of how ai can assist researchers in crafting effective figure captions. these insights lay the groundwork for developing more effective and intuitive ai tools that enhance scientific communication. building on these insights, future research should focus on developing and testing ai tools for scientific caption writing in real-world scenarios. such evaluations will reveal their effectiveness and usability while providing deeper insights into researchers' needs and challenges. observations of authentic writing practices will guide refinements, ensuring that ai systems address the complexities of caption writing across disciplines. this work will lead to more adaptable, user-centered ai solutions that enhance both the writing process and the quality of scientific communication.