arXiv subjects

Kynnedy Simone Smith

Publications and source records attributed to Kynnedy Simone Smith.

4 recordsLinked to original sources

arXiv2026

Identifying, Explaining, and Correcting Ableist Language with AI

Ableist language perpetuates harmful stereotypes and exclusion, yet its nuanced nature makes it difficult to recognize and address. Artificial intelligence could serve as a powerful ally in the fight against ableist language, offering tools that detect and suggest alternatives to biased terms. This two-part study investigates the potential of large language models (LLMs), specifically ChatGPT, to rectify ableist language and educate users about inclusive communication. We compared GPT-4o generations with crowdsourced annotations from trained disability community members, then invited disabled participants to evaluate both. Participants reported equal agreement with human and AI annotations but significantly preferred the AI, citing its narrative consistency and accessible style. At the same time, they valued the emotional depth and cultural grounding of human annotations. These findings highlight the promise and limits of LLMs in handling culturally sensitive content. Our contributions include a dataset of nuanced ableism annotations and design considerations for inclusive writing tools.

cs.HC↗

arXiv2025

"I never would have thought to say this": Example-Based Exploration to Balance Scientists' Writing Preferences with Public Science Communication Strategies

Public-facing science communication is important in garnering interest, engagement, and trust in science. Social media platforms provide scientists with opportunities to reach broader audiences, yet many resist adopting social media writing strategies because the strategies conflict with traditional science writing norms and personal preferences. To address this gap, we first evaluate readers' preferences for strategies such as examples, walkthroughs, and personal language. While many readers enjoyed science narratives that used these strategies, their effectiveness was nuanced and context-dependent, varying by topic and individual preference. Building on these findings, we design a system that uses contrastive examples to help scientists adopt and integrate these social media science writing strategies. In a user study with scientists, we found that presenting contrastive examples helped writers critically evaluate different narrative options, balance competing goals, and gain confidence in adapting social media writing strategies to fit both their topic and audience.

cs.HC↗

arXiv2025

Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review

Large language models (LLMs) have been positioned to revolutionize HCI, by reshaping not only the interfaces, design patterns, and sociotechnical systems that we study, but also the research practices we use. To-date, however, there has been little understanding of LLMs' uptake in HCI. We address this gap via a systematic literature review of 153 CHI papers from 2020-24 that engage with LLMs. We taxonomize: (1) domains where LLMs are applied; (2) roles of LLMs in HCI projects; (3) contribution types; and (4) acknowledged limitations and risks. We find LLM work in 10 diverse domains, primarily via empirical and artifact contributions. Authors use LLMs in five distinct roles, including as research tools or simulated users. Still, authors often raise validity and reproducibility concerns, and overwhelmingly study closed models. We outline opportunities to improve HCI research with and on LLMs, and provide guiding questions for researchers to consider the validity and appropriateness of LLM-related work.

cs.HC↗

arXiv2023

Tweetorial Hooks: Generative AI Tools to Motivate Science on Social Media

Communicating science and technology is essential for the public to understand and engage in a rapidly changing world. Tweetorials are an emerging phenomenon where experts explain STEM topics on social media in creative and engaging ways. However, STEM experts struggle to write an engaging "hook" in the first tweet that captures the reader's attention. We propose methods to use large language models (LLMs) to help users scaffold their process of writing a relatable hook for complex scientific topics. We demonstrate that LLMs can help writers find everyday experiences that are relatable and interesting to the public, avoid jargon, and spark curiosity. Our evaluation shows that the system reduces cognitive load and helps people write better hooks. Lastly, we discuss the importance of interactivity with LLMs to preserve the correctness, effectiveness, and authenticity of the writing.

cs.HC↗