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20242026
most citedHuman learning is an understudied but promising lever for boosting human--AI synergy

1 citations · 2 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.HC2025

Prompt-Hacking: The New p-Hacking?

Thomas Kosch, Sebastian Feger

As Large Language Models (LLMs) become increasingly embedded in empirical research workflows, their use as analytical tools for quantitative or qualitative data raises pressing con…

cs.HC2025

The Impostor is Among Us: Can Large Language Models Capture the Complexity of Human Personas?

Christopher Lazik, Christopher Katins, Charlotte Kauter +4

Large Language Models (LLMs) created new opportunities for generating personas, expected to streamline and accelerate the human-centered design process. Yet, AI-generated personas…

cs.HC2025

If You Had to Pitch Your Ideal Software -- Evaluating Large Language Models to Support User Scenario Writing for User Experience Experts and Laypersons

Patrick Stadler, Christopher Lazik, Christopher Katins +1

The process of requirements analysis requires an understanding of the end users of a system. Thus, expert stakeholders, such as User Experience (UX) designers, usually create vario…

cs.HC2025

HappyRouting: Learning Emotion-Aware Route Trajectories for Scalable In-The-Wild Navigation

David Bethge, Daniel Bulanda, Adam Kozlowski +3

Routes represent an integral part of triggering emotions in drivers. Navigation systems allow users to choose a navigation strategy, such as the fastest or shortest route. However,…

cs.HC2025

Evaluating Eye Tracking and Electroencephalography as Indicator for Selective Exposure During Online News Reading

Thomas Krämer, Francesco Chiossi, Thomas Kosch

Selective exposure to online news consumption reinforces filter bubbles, restricting access to diverse viewpoints. Interactive systems can counteract this bias by suggesting altern…

cs.HC2025

Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction

Daniela Fernandes, Steeven Villa, Salla Nicholls +6

Optimizing human-AI interaction requires users to reflect on their own performance critically. Our paper examines whether people using AI to complete tasks can accurately monitor h…