Publications
Below is a searchable list of publications by the projects of the Priority Program.
1.
Pfützenreuter, Niklas; Liebers, Carina; Goedicke, David; Degraen, Donald; Gruenefeld, Uwe; Schneegass, Stefan
Eye Want It All! Investigating Eye Tracking as Implicit Support for Generative Inpainting Proceedings Article
In: Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, 2026, ISBN: 9798400722813.
Abstract | Links | BibTeX | Tags: eye tracking, Generative Artificial Intelligence, Image Generation
@inproceedings{10.1145/3772363.3799314,
title = {Eye Want It All! Investigating Eye Tracking as Implicit Support for Generative Inpainting},
author = {Niklas Pfützenreuter and Carina Liebers and David Goedicke and Donald Degraen and Uwe Gruenefeld and Stefan Schneegass},
url = {https://doi.org/10.1145/3772363.3799314},
doi = {10.1145/3772363.3799314},
isbn = {9798400722813},
year = {2026},
date = {2026-01-01},
booktitle = {Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {CHI EA '26},
abstract = {Users often struggle to use Generative Artificial Intelligence (GenAI) models to generate a desired image, as controlling them solely with prompts is difficult. Current solutions to this problem, such as adding conditional controls, require users to provide explicit input, which can be tedious. To avoid depending on additional explicit input, this paper explores what implicit gaze behavior tells about user intentions when viewing generated images. In our user study (N = 16), we evaluated the correlation between gaze behavior and user annotations, showing that users looked longer at areas they wanted to regenerate. While our research is the first step, we believe our work can pave the way for incorporating implicit user input into interactions with GenAI systems.},
keywords = {eye tracking, Generative Artificial Intelligence, Image Generation},
pubstate = {published},
tppubtype = {inproceedings}
}
Users often struggle to use Generative Artificial Intelligence (GenAI) models to generate a desired image, as controlling them solely with prompts is difficult. Current solutions to this problem, such as adding conditional controls, require users to provide explicit input, which can be tedious. To avoid depending on additional explicit input, this paper explores what implicit gaze behavior tells about user intentions when viewing generated images. In our user study (N = 16), we evaluated the correlation between gaze behavior and user annotations, showing that users looked longer at areas they wanted to regenerate. While our research is the first step, we believe our work can pave the way for incorporating implicit user input into interactions with GenAI systems.
2.
Sauter, Teresa Hirzle Tobias Wagner Marian; Huckauf, Anke
Behind the Screens: Exploring Eye Movement Visualization to Optimize Online Teaching and Learning Proceedings Article
In: Mensch und Computer 2023 (MuC ’23), Association for Computing Machinery, Rapperswil, Switzerland, 2023.
Abstract | Links | BibTeX | Tags: education, eye tracking, gaze visualizations, learning, online teaching, quantitative methods
@inproceedings{10.1145/3603555.3603560,
title = {Behind the Screens: Exploring Eye Movement Visualization to Optimize Online Teaching and Learning},
author = {Teresa Hirzle Tobias Wagner Marian Sauter and Anke Huckauf},
url = {https://doi.org/10.1145/3603555.3603560},
doi = {10.1145/3603555.3603560},
year = {2023},
date = {2023-01-01},
booktitle = {Mensch und Computer 2023 (MuC ’23)},
publisher = {Association for Computing Machinery},
address = {Rapperswil, Switzerland},
series = {MuC '23},
abstract = {The effective delivery of e-learning depends on the continuous monitoring and management of student attention. While instructors in traditional classroom settings can easily assess crowd attention
through gaze cues, these cues are largely unavailable in online learning environments. To address this challenge and highlight the significance of our study, we collected eye movement data from
twenty students and developed four visualization methods: (a) a heat map, (b) an ellipse map, (c) two moving bars, and (d) a vertical bar, which were overlaid on 13 instructional videos. Our results revealed unexpected preferences among the instructors. Contrary to expectations, they did not prefer the established heat map and vertical bar for live online instruction. Instead, they chose the less
intrusive ellipse visualization. Nevertheless, the heat map remained the preferred choice for retrospective analysis due to its more detailed information. Importantly, all visualizations were found to be useful and to help restore emotional connections in online learning. In conclusion, our innovative visualizations of crowd attention show considerable potential for a wide range of applications, extending beyond e-learning to all online presentations and retrospective analyses. The significant results of our study underscore the critical role these visualizations will play in enhancing both the effectiveness and emotional connectedness of future e-learning experiences, thereby facilitating the educational landscape.},
keywords = {education, eye tracking, gaze visualizations, learning, online teaching, quantitative methods},
pubstate = {published},
tppubtype = {inproceedings}
}
The effective delivery of e-learning depends on the continuous monitoring and management of student attention. While instructors in traditional classroom settings can easily assess crowd attention
through gaze cues, these cues are largely unavailable in online learning environments. To address this challenge and highlight the significance of our study, we collected eye movement data from
twenty students and developed four visualization methods: (a) a heat map, (b) an ellipse map, (c) two moving bars, and (d) a vertical bar, which were overlaid on 13 instructional videos. Our results revealed unexpected preferences among the instructors. Contrary to expectations, they did not prefer the established heat map and vertical bar for live online instruction. Instead, they chose the less
intrusive ellipse visualization. Nevertheless, the heat map remained the preferred choice for retrospective analysis due to its more detailed information. Importantly, all visualizations were found to be useful and to help restore emotional connections in online learning. In conclusion, our innovative visualizations of crowd attention show considerable potential for a wide range of applications, extending beyond e-learning to all online presentations and retrospective analyses. The significant results of our study underscore the critical role these visualizations will play in enhancing both the effectiveness and emotional connectedness of future e-learning experiences, thereby facilitating the educational landscape.
through gaze cues, these cues are largely unavailable in online learning environments. To address this challenge and highlight the significance of our study, we collected eye movement data from
twenty students and developed four visualization methods: (a) a heat map, (b) an ellipse map, (c) two moving bars, and (d) a vertical bar, which were overlaid on 13 instructional videos. Our results revealed unexpected preferences among the instructors. Contrary to expectations, they did not prefer the established heat map and vertical bar for live online instruction. Instead, they chose the less
intrusive ellipse visualization. Nevertheless, the heat map remained the preferred choice for retrospective analysis due to its more detailed information. Importantly, all visualizations were found to be useful and to help restore emotional connections in online learning. In conclusion, our innovative visualizations of crowd attention show considerable potential for a wide range of applications, extending beyond e-learning to all online presentations and retrospective analyses. The significant results of our study underscore the critical role these visualizations will play in enhancing both the effectiveness and emotional connectedness of future e-learning experiences, thereby facilitating the educational landscape.