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Researcher profile

Andreas Butz

Andreas Butz contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Human-Computer InteractionComputation and Language

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Published work

5 published item(s)

preprint2026arXiv

Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas

Conversational interfaces powered by large language models (LLMs) are widely used for ideation and analysis, yet their linear structure limits exploration of alternatives and management of long-running interactions. We present CanvasConvo, a conversational interface concept that transforms linear chat into a branching conversation tree embedded in a spatial canvas. CanvasConvo enables users to explore what-if scenarios by branching directly from conversational content, supporting parallel development of alternative directions. These branches are visualized on a canvas while remaining integrated with a familiar chat interface, allowing users to switch between linear and non-linear interaction. Features such as timeline-based navigation, automatic tagging and summarization, and context-aware controls (e.g., goals, reusable prompts) support structured interaction and continuity. We evaluated CanvasConvo in a 5-7 day field study with 24 participants. Our findings highlight how non-linear conversational structures support exploratory workflows and different interactions in LLM-based work.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

How Should Voice Assistants Deal With Users' Emotions?

There is a growing body of research in HCI on detecting the users' emotions. Once it is possible to detect users' emotions reliably, the next question is how an emotion-aware interface should react to the detected emotion. In a first step, we tried to find out how humans deal with the negative emotions of an avatar. The hope behind this approach was to identify human strategies, which we can then mimic in an emotion-aware voice assistant. We present a user study in which participants were confronted with an angry, sad, or frightened avatar. Their task was to make the avatar happy by talking to it. We recorded the voice signal and analyzed it. The results show that users predominantly reacted with neutral emotion. However, we also found gender differences, which opens a range of questions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Human in the Infinite Loop: A Case Study on Revealing and Explaining Human-AI Interaction Loop Failures

Interactive AI systems increasingly employ a human-in-the-loop strategy. This creates new challenges for the HCI community when designing such systems. We reveal and investigate some of these challenges in a case study with an industry partner, and developed a prototype human-in-the-loop system for preference-guided 3D model processing. Two 3D artists used it in their daily work for 3 months. We found that the human-AI loop often did not converge towards a satisfactory result and designed a lab study (N=20) to investigate this further. We analyze interaction data and user feedback through the lens of theories of human judgment to explain the observed human-in-the-loop failures with two key insights: 1) optimization using preferential choices lacks mechanisms to deal with inconsistent and contradictory human judgments; 2) machine outcomes, in turn, influence future user inputs via heuristic biases and loss aversion. To mitigate these problems, we propose descriptive UI design guidelines. Our case study draws attention to challenging and practically relevant imperfections in human-AI loops that need to be considered when designing human-in-the-loop systems.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Modeling Web Browsing Behavior across Tabs and Websites with Tracking and Prediction on the Client Side

Clickstreams on individual websites have been studied for decades to gain insights into user interests and to improve website experiences. This paper proposes and examines a novel sequence modeling approach for web clickstreams, that also considers multi-tab branching and backtracking actions across websites to capture the full action sequence of a user while browsing. All of this is done using machine learning on the client side to obtain a more comprehensive view and at the same time preserve privacy. We evaluate our formalism with a model trained on data collected in a user study with three different browsing tasks based on different human information seeking strategies from psychological literature. Our results show that the model can successfully distinguish between browsing behaviors and correctly predict future actions. A subsequent qualitative analysis identified five common web browsing patterns from our collected behavior data, which help to interpret the model. More generally, this illustrates the power of overparameterization in ML and offers a new way of modeling, reasoning with, and prediction of observable sequential human interaction behaviors.

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preprint2020arXiv

What If Your Car Would Care? Exploring Use Cases For Affective Automotive User Interfaces

In this paper we present use cases for affective user interfaces (UIs) in cars and how they are perceived by potential users in China and Germany. Emotion-aware interaction is enabled by the improvement of ubiquitous sensing methods and provides potential benefits for both traffic safety and personal well-being. To promote the adoption of affective interaction at an international scale, we developed 20 mobile in-car use cases through an inter-cultural design approach and evaluated them with 65 drivers in Germany and China. Our data shows perceived benefits in specific areas of pragmatic quality as well as cultural differences, especially for socially interactive use cases. We also discuss general implications for future affective automotive UI. Our results provide a perspective on cultural peculiarities and a concrete starting point for practitioners and researchers working on emotion-aware interfaces.

0 citations0 reviewsNo code linkedOpen access