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arxivcs.HC2026-07-13

Thought Experiments for Conceptual Work: A New Application of a (Very) Old Method

Leah Hope Ajmani, Mo Houtti, Eric P. S. Baumer, Stevie Chancellor

In this paper, we propose thought experiments (TEs) as a crucial method for Human-Computer Interaction (HCI) researchers to engage in conceptual work. As an interdisciplinary field, HCI often uses concepts as the fundamental building blocks for larger theories. However, the conceptual commitments we make in this process carry normative consequences. TEs are a well-established philosophical method, whereby a hypothetical but tractable scenario logically progresses to a conclusion. We outline TEs as an interrogative method that brings conceptualizations to their normative implications through logical moves. We illustrate the value of thought experiments through two examples: (1) original thought experiments to critique stakeholders in Value-Sensitive Design and (2) Helen Nissenbaum's use of thought experiments to generate contextual integrity. We discuss how TEs precisely anticipate the potential harms of technologies, allowing HCI to operationalize current calls for increased scrutiny of research ethics and broader implications.

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arxivcs.AIcs.HC2026-07-16

Project Kaleidoscope: Contextual, Human-Aligned Evaluation for Real-World AI Applications

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Evaluations (Evals) are a deployment bottleneck for real-world AI applications: public benchmarks rarely match a team's users, context, or policies, and human review is often tedious to scale. Motivated by our work with AI applications in the public sector, this project addresses…

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arxivcs.HC2026-07-22

TargetFinder: Detecting Widgets from Pixels on Desktop Interfaces

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''Target-aware'' pointing techniques, like Bubble Cursor or Semantic Pointing, outperform traditional pointing by leveraging knowledge of target locations. Yet the lack of application-agnostic widget geometry information limits their adoption across the desktop. We present Target…

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Doom Researching: A Conceptual Framework for Repetitive AI-Assisted Information Seeking, Cognitive Offloading, and the Illusion of Knowing

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Generative artificial intelligence (GenAI) systems such as ChatGPT, Claude, and Gemini have made information seeking faster, more conversational, and more cognitively comfortable. These affordances can support learning and productivity, but they can also encourage a repetitive pa…

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