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Faculty of Mathematics, Physics and Computer Sciences

Research Group HCI + AI

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AI Tools - Junior Research Group Project

We are funded by the Bavarian State Ministry of Science and the Arts, coordinated by the Bavarian Research Institute for Digital Transformation (bidt).


Many “intelligent” systems today lack interaction: They run “AI as a service” in the background to serve ready-made meals, often presented as suggestions or recommendations. As users, we can decide to accept these or not but as such our role seems limited to passive consumers. Even experts often just receive final output of black-box AI systems: If it is uninformative, they change code and start over. Among humans, we would judge this lack of exchange during tasks as ineffective – neither should we accept it for AI.

How can we enable fluent human-AI interaction? 

We address this with the perspective of “AI as a tool”: We envision that humans apply and steer AI with continuous impact and feedback, and combine AI modules from a toolbox. Among the key factors we see continuous interaction, flexible control, appropriation & explainability, as well as ergonomic considerations. We are motivated to develop conceptual and technical foundations and explore application domains that empower millions of people (e.g. data analysis, mobile communication, design, health).

AI Tools

What is an AI tool? These key elements guide our work on such tools:

  • Interactive: An AI tool is interactively used by humans, without replacing them.

  • Shaping interaction: An AI tool influences how we actively engage with the overall system and task at hand (e.g. not exclusively a “background service”).

  • Non-antropomorphic: An AI tool is not (always) presented as an agent, person, or “being”.

  • Empowering: An AI tool is useful if it helps humans in a task, i.e. if it improves a meaningful metric, such as efficiency, effectiveness, or expressiveness.

Consider this analogy with classic tools: With a hand drill, people provide both a control signal (positioning the drill at the target) and power (turning the handle). In contrast, the invention of the power drill provided an external power source (electric power) while keeping humans in control (user still positions the drill). In analogy, we envision that future digital AI tools empower humans with AI as a “power source” for task-specific intelligence.


Here we list selected recent publications that are part of this project. Our group's general publication list is available at our publications page here.

What is "Intelligent" in Intelligent User Interfaces? A Meta-Analysis of 25 Years of IUI (IUI'20)

Paper (ACM DL) · Preprint (arXiv) · Overview article (Medium)

AI is a hyped term but what does it actually mean for technology to be "intelligent"? We followed a bottom-up approach to analyse the emergent meaning of intelligence in the research community of Intelligent User Interfaces (IUI). We processed all IUI papers from 25 years to extract how researchers use the term "intelligent". Our results show a growing variety over the years of what is referred to as intelligent and how it is characterised as such. At the core, we found the key aspects of automation, adaptation, and interaction.

Heartbeats in the Wild: A Field Study Exploring ECG Biometrics in Everyday Life (CHI'20)

 Paper (ACM DL) · Preprint (arXiv) · Presentation (YouTube)

Interactive AI systems process user data. More and more, this includes physiological data. This paper reports on the first in-depth study of one such source - electrocardiogram (ECG) data - in everyday life. Our study contributes to the bigger picture that new biometric modalities are now viable to be measured in daily life. A broader implication is that it becomes increasingly important not to adopt a simplified view on “biometrics” as a single concept. Working with everyday data, as in our study here, thus highlights the need for responsible application design for real world biometrics applications.

Awarded with a "Best Paper Honourable Mention Award" at CHI'20.

Developing a Personality Model for Speech-based Conversational Agents Using the Psycholexical Approach (CHI'20)

Paper (ACM DL) · Preprint (arXiv) · Presentation (YouTube)

The "AI Tools" project contrasts presenting AI as a tool with the currently common presentation as an agent. Such agents might convey having a certain personality. In this interdisciplinary work across HCI, AI, and Psychology, we ask how such AI personality might be designed in an informed way: We present the first systematic analysis of personality dimensions to describe speech-based conversational AI agents, following the psycholexical approach from Psychology. A factor analysis reveals that the commonly used Big Five model for human personality does not adequately describe AI personality. We propose alternative dimensions as a step towards developing an AI agent personality model.

Awarded with a "Best Paper Honourable Mention Award" at CHI'20.

LanguageLogger: A Mobile Keyboard Application for Studying Language Use in Everyday Text Communication in the Wild (Proc. ACM Hum.-Comput. Interact., June 2020)

Paper (ACM DL) · Project website

We present a new keyboard app as a research tool to collect data on language use in everyday mobile text communication (e.g. chats). Our approach enables researchers to collect such data during unconstrained natural typing without logging readable messages to preserve privacy. We achieve this with a combination of three customisable text abstraction methods that run directly on participants' phones. Our research tool thus facilitates diverse interdisciplinary projects interested in language data, e.g. in HCI, AI, Linguistics, and Psychology.

Get in touch with us if you would like to deploy the tool in your research project!

Paper2Wire - A Case Study of User-Centred Development of Machine Learning Tools for UX Designers (Proc. Mensch und Computer 2020)

▹Paper (to appear September 2020)

This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners' needs and practices.


Interested in discussing related ideas? We are present at the following workshops / events:


For various projects we're working with great people from other labs, including at: 

Webmaster: Dr. Daniel Buschek

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