Tootfinder

Organization Matters: A Qualitative Study of Organizational Dynamics in Red Teaming Practices For Generative AI
Bixuan Ren, EunJeong Cheon, Jianghui Li
https://arxiv.org/abs/2508.12504

Organization Matters: A Qualitative Study of Organizational Dynamics in Red Teaming Practices For Generative AI
The rapid integration of generative artificial intelligence (GenAI) across diverse fields underscores the critical need for red teaming efforts to proactively identify and mitigate associated risks. While previous research primarily addresses technical aspects, this paper highlights organizational factors that hinder the effectiveness of red teaming in real-world settings. Through qualitative analysis of 15 semi-structured interviews with red teamers from various organizations, we uncover chall…

Investigating VR Accessibility Reviews for Users with Disabilities: A Qualitative Analysis
Yi Wang, Chetan Arora, Xiao Liu, Thuong Hoang, ZHengxin Zhang, Henry Been Lirn Duh, John Grundy
https://arxiv.org/abs/2508.13051

Investigating VR Accessibility Reviews for Users with Disabilities: A Qualitative Analysis
Accessibility reviews provide valuable insights into both the limitations and benefits experienced by users with disabilities when using virtual reality (VR) applications. However, a comprehensive investigation into VR accessibility for users with disabilities is still lacking. To fill this gap, this study analyzes user reviews from the Meta and Steam stores of VR apps, focusing on the reported issues affecting users with disabilities. We applied selection criteria to 1,367,419 reviews from the…

Qualitative Research in an Era of AI: A Pragmatic Approach to Data Analysis, Workflow, and Computation
Corey M. Abramson, Zhuofan Li, Tara Prendergast, Daniel Dohan
https://arxiv.org/abs/2509.12503

Qualitative Research in an Era of AI: A Pragmatic Approach to Data Analysis, Workflow, and Computation
Rapid computational developments - particularly the proliferation of artificial intelligence (AI) - increasingly shape social scientific research while raising new questions about in-depth qualitative methods such as ethnography and interviewing. Building on classic debates about using computers to analyze qualitative data, we revisit longstanding concerns and assess possibilities and dangers in an era of automation, AI chatbots, and 'big data.' We first historicize developments by revisiting c…

System-driven Interactive Design Support for Cloud Architecture: A Qualitative User Experience Study with Novice Engineers
Ryosuke Kohita, Akira Kasuga
https://arxiv.org/abs/2508.12385

System-driven Interactive Design Support for Cloud Architecture: A Qualitative User Experience Study with Novice Engineers
Cloud architecture design presents significant challenges due to the necessity of clarifying ambiguous requirements and systematically addressing complex trade-offs, especially for novice engineers with limited cloud experience. While recent advances in the use of AI tools have broadened available options, system-driven approaches that offer explicit guidance and step-by-step information management may be especially effective in supporting novices during the design process. This study qualitati…

Optimizers Qualitatively Alter Solutions And We Should Leverage This
Razvan Pascanu, Clare Lyle, Ionut-Vlad Modoranu, Naima Elosegui Borras, Dan Alistarh, Petar Velickovic, Sarath Chandar, Soham De, James Martens
https://arxiv.org/abs/2507.12224

Optimizers Qualitatively Alter Solutions And We Should Leverage This
Due to the nonlinear nature of Deep Neural Networks (DNNs), one can not guarantee convergence to a unique global minimum of the loss when using optimizers relying only on local information, such as SGD. Indeed, this was a primary source of skepticism regarding the feasibility of DNNs in the early days of the field. The past decades of progress in deep learning have revealed this skepticism to be misplaced, and a large body of empirical evidence shows that sufficiently large DNNs following stand…

Prompting the Professoriate: A Qualitative Study of Instructor Perspectives on LLMs in Data Science Education
Ana Elisa Lopez-Miranda, Tiffany Timbers, Rohan Alexander
https://arxiv.org/abs/2509.12283 …

Prompting the Professoriate: A Qualitative Study of Instructor Perspectives on LLMs in Data Science Education
Large Language Models (LLMs) have shifted in just a few years from novelty to ubiquity, raising fundamental questions for data science education. Tasks once used to teach coding, writing, and problem-solving can now be completed by LLMs, forcing educators to reconsider both pedagogy and assessment. To understand how instructors are adapting, we conducted semi-structured interviews with 42 instructors from 33 institutions in 10 countries in June and July 2025. Our qualitative analysis reveals a …

Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding
Veronica Pimenova, Sarah Fakhoury, Christian Bird, Margaret-Anne Storey, Madeline Endres
https://arxiv.org/abs/2509.12491

Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding
Vibe coding, a term coined by Andrej Karpathy in February 2025, has quickly become a compelling and controversial natural language programming paradigm in AI-assisted software development. Centered on iterative co-design with an AI assistant, vibe coding emphasizes flow and experimentation over strict upfront specification. While initial studies have begun to explore this paradigm, most focus on analyzing code artifacts or proposing theories with limited empirical backing. There remains a need …

Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews
He Zhang, Yueyan Liu, Xin Guan, Jie Cai, John M. Carroll
https://arxiv.org/abs/2509.12709

Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews
Semi-structured interviews highly rely on the quality of follow-up questions, yet interviewers' knowledge and skills may limit their depth and potentially affect outcomes. While many studies have shown the usefulness of large language models (LLMs) for qualitative analysis, their possibility in the data collection process remains underexplored. We adopt an AI-driven "Wizard-of-Oz" setup to investigate how real-time LLM support in generating follow-up questions shapes semi-structured interviews.…

Generative AI in Game Development: A Qualitative Research Synthesis
Alexandru Ternar, Alena Denisova, Jo\~ao M. Cunha, Annakaisa Kultima, Christian Guckelsberger
https://arxiv.org/abs/2509.11898

Generative AI in Game Development: A Qualitative Research Synthesis
Generative Artificial Intelligence (GenAI) has had a tremendous impact on game production and promises lasting transformations. In the last five years since GenAI's inception, several studies, typically via qualitative methods, have explored its impact on game production from different settings and demographic angles. However, these studies often contextualise and consolidate their findings weakly with related work, and a big picture view is still missing. Here, we aim to provide such a view of…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Qualitative Study for LLM-assisted Design Study Process: Strategies, Challenges, and Roles
Shaolun Ruan, Rui Sheng, Xiaolin Wen, Jiachen Wang, Tianyi Zhang, Yong Wang, Tim Dwyer, Jiannan Li