Description
Data Analysis Workflows (DAWs) undergo a step-by-step development process that involves continuous testing and re-implementation. While this process allows for early error detection and optimization, it is resource-intensive. The goal of this project is to extend existing workflow definition languages by DAW interfaces, investigate user interaction preferences when DAWs prompt user interaction, and conduct empirical user studies to assess user performance when using DAW interfaces.
Scientists
- Christopher Katins
- Christopher Lazik
Publications
2026
Fernandes, Daniela; Villa, Steeven; Nicholls, Salla; Haavisto, Otso; Buschek, Daniel; Schmidt, Albrecht; Kosch, Thomas; Shen, Chenxinran; Welsch, Robin
AI makes you smarter but none the wiser: The disconnect between performance and metacognition Journal Article
In: Computers in Human Behavior, vol. 175, pp. 108779, 2026, ISSN: 0747-5632.
@article{fernandes2025ai,
title = {AI makes you smarter but none the wiser: The disconnect between performance and metacognition},
author = {Daniela Fernandes and Steeven Villa and Salla Nicholls and Otso Haavisto and Daniel Buschek and Albrecht Schmidt and Thomas Kosch and Chenxinran Shen and Robin Welsch},
url = {https://www.sciencedirect.com/science/article/pii/S0747563225002262},
doi = {https://doi.org/10.1016/j.chb.2025.108779},
issn = {0747-5632},
year = {2026},
date = {2026-10-27},
urldate = {2026-02-01},
journal = {Computers in Human Behavior},
volume = {175},
pages = {108779},
abstract = {Optimizing human–AI interaction requires users to reflect on their performance critically, yet little is known about generative AI systems’ effect on users’ metacognitive judgments. In two large-scale studies, we investigate how AI usage is associated with users’ metacognitive monitoring and performance in logical reasoning tasks. Specifically, our paper examines whether people using AI to complete tasks can accurately monitor how well they perform. In Study 1, participants (N = 246) used AI to solve 20 logical reasoning problems from the Law School Admission Test. While their task performance improved by three points compared to a norm population, participants overestimated their task performance by four points. Interestingly, higher AI literacy correlated with lower metacognitive accuracy, suggesting that those with more technical knowledge of AI were more confident but less precise in judging their own performance. Using a computational model, we explored individual differences in metacognitive accuracy and found that the Dunning–Kruger effect, usually observed in this task, ceased to exist with AI use. Study 2 (N = 452) replicates these findings. We discuss how AI levels cognitive and metacognitive performance in human–AI interaction and consider the consequences of performance overestimation for designing interactive AI systems that foster accurate self-monitoring, avoid overreliance, and enhance cognitive performance.},
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Optimizing human–AI interaction requires users to reflect on their performance critically, yet little is known about generative AI systems’ effect on users’ metacognitive judgments. In two large-scale studies, we investigate how AI usage is associated with users’ metacognitive monitoring and performance in logical reasoning tasks. Specifically, our paper examines whether people using AI to complete tasks can accurately monitor how well they perform. In Study 1, participants (N = 246) used AI to solve 20 logical reasoning problems from the Law School Admission Test. While their task performance improved by three points compared to a norm population, participants overestimated their task performance by four points. Interestingly, higher AI literacy correlated with lower metacognitive accuracy, suggesting that those with more technical knowledge of AI were more confident but less precise in judging their own performance. Using a computational model, we explored individual differences in metacognitive accuracy and found that the Dunning–Kruger effect, usually observed in this task, ceased to exist with AI use. Study 2 (N = 452) replicates these findings. We discuss how AI levels cognitive and metacognitive performance in human–AI interaction and consider the consequences of performance overestimation for designing interactive AI systems that foster accurate self-monitoring, avoid overreliance, and enhance cognitive performance.
Berger, Julian; Burton, Jason W.; Hertwig, Ralph; Kosch, Thomas; Kurvers, Ralf H. J. M.; Kurzenberger, Benito; Lazik, Christopher; Onnasch, Linda; Rieger, Tobias; Thoma, Anna I.; Wulff, Dirk U.; Herzog, Stefan M.
Human learning is an understudied but promising lever for boosting human–AI synergy Journal Article
In: Proceedings of the National Academy of Sciences, vol. 123, no. 39, pp. e2536100123, 2026.
@article{<LineBreak>doi:10.1073/pnas.2536100123,
title = {Human learning is an understudied but promising lever for boosting human–AI synergy},
author = {Julian Berger and Jason W. Burton and Ralph Hertwig and Thomas Kosch and Ralf H. J. M. Kurvers and Benito Kurzenberger and Christopher Lazik and Linda Onnasch and Tobias Rieger and Anna I. Thoma and Dirk U. Wulff and Stefan M. Herzog},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.2536100123},
doi = {10.1073/pnas.2536100123},
year = {2026},
date = {2026-01-01},
urldate = {2026-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {123},
number = {39},
pages = {e2536100123},
abstract = {Humans collaborating with AI hold the promise of achieving superior outcomes compared to either acting alone (i.e., human–AI synergy). However, the conditions that facilitate such synergy when humans are advised by AI are not well understood. A recent meta-analysis showed that, on average, human–AI combinations do not outperform the better individual agent. We argue that this pessimistic conclusion arises from insufficient attention to human learning in experimental designs. To substantiate this claim, we reanalyzed all 74 studies included in the original meta-analysis and found that most previous research overlooked design features that foster human learning (e.g., outcome feedback to participants). Our reanalysis further revealed that studies providing outcome feedback show tentatively higher synergy than those without outcome feedback. Crucially, feedback paired with AI explanations was associated with positive synergy, while explanations without feedback were associated with negative synergy—suggesting that explanations improve synergy mainly when humans can learn to verify the AI’s reliability through feedback. Our reanalysis suggests that the current literature underestimates the potential of human–AI collaboration because it predominantly relies on paradigms that do not facilitate human learning, thus hindering humans from effectively adapting their collaboration strategies. However, experiments directly varying learning opportunities are needed for stronger, causal conclusions. We advocate for a paradigm shift in human–AI interaction research that explicitly addresses human learning and thus enhances our understanding of and support for successful human–AI collaboration.},
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Humans collaborating with AI hold the promise of achieving superior outcomes compared to either acting alone (i.e., human–AI synergy). However, the conditions that facilitate such synergy when humans are advised by AI are not well understood. A recent meta-analysis showed that, on average, human–AI combinations do not outperform the better individual agent. We argue that this pessimistic conclusion arises from insufficient attention to human learning in experimental designs. To substantiate this claim, we reanalyzed all 74 studies included in the original meta-analysis and found that most previous research overlooked design features that foster human learning (e.g., outcome feedback to participants). Our reanalysis further revealed that studies providing outcome feedback show tentatively higher synergy than those without outcome feedback. Crucially, feedback paired with AI explanations was associated with positive synergy, while explanations without feedback were associated with negative synergy—suggesting that explanations improve synergy mainly when humans can learn to verify the AI’s reliability through feedback. Our reanalysis suggests that the current literature underestimates the potential of human–AI collaboration because it predominantly relies on paradigms that do not facilitate human learning, thus hindering humans from effectively adapting their collaboration strategies. However, experiments directly varying learning opportunities are needed for stronger, causal conclusions. We advocate for a paradigm shift in human–AI interaction research that explicitly addresses human learning and thus enhances our understanding of and support for successful human–AI collaboration.
Lazik, Christopher; Kauter, Charlotte; Graßl, Isabella; Pryma, Alina; Katins, Christopher; Grunske, Lars; Kosch, Thomas
Nobody Talks About the Prompt Club: Evaluating Diverse Persona Generation Through System Prompts and Guidelines Proceedings Article
In: 2026 IEEE 34th International Requirements Engineering Conference (RE), pp. 188-200, 2026.
@inproceedings{11676614,
title = {Nobody Talks About the Prompt Club: Evaluating Diverse Persona Generation Through System Prompts and Guidelines},
author = {Christopher Lazik and Charlotte Kauter and Isabella Graßl and Alina Pryma and Christopher Katins and Lars Grunske and Thomas Kosch},
doi = {10.1109/RE68928.2026.00022},
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Lazik, Christopher Klaus; Eberlein, Martin; Ziglowski, Aaron; Katins, Christopher; Kosch, Thomas
Does Great Power Come with Great Explainability? Comparing Explanation Strategies for Automated Program Diagnosis Proceedings Article
In: Proceedings of Mensch Und Computer 2026, pp. 638–642, Association for Computing Machinery, New York, NY, USA, 2026, ISBN: 9798400726118.
@inproceedings{10.1145/3820253.3831404,
title = {Does Great Power Come with Great Explainability? Comparing Explanation Strategies for Automated Program Diagnosis},
author = {Christopher Klaus Lazik and Martin Eberlein and Aaron Ziglowski and Christopher Katins and Thomas Kosch},
url = {https://doi.org/10.1145/3820253.3831404},
doi = {10.1145/3820253.3831404},
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year = {2026},
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booktitle = {Proceedings of Mensch Und Computer 2026},
pages = {638–642},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {MuC '26},
abstract = {Debugging is an important activity in software development, yet providing actionable and comprehensible explanations for program failures remains challenging. Automated tools such as ALHAZEN and AVICENNA address this by using distinct strategies: ALHAZEN employs binary decision trees to show failure-inducing conditions, while AVICENNA uses a specification language to model complex input dependencies. To assess their impact on usability and user efficiency, we conducted a controlled within-between-subjects user study with 18 participants tasked with resolving four software bugs using either tool or no support. Quantitative results showed that both tools improved debugging efficiency compared to manual methods, with AVICENNA offering more precise diagnostics but requiring higher cognitive effort. Qualitative feedback revealed a preference for AVICENNA’s expressiveness despite its complexity. Our findings show that effective debugging tools have tradeoffs between accuracy and interpretability to support developers’ decision-making in increasingly complex software environments.},
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Debugging is an important activity in software development, yet providing actionable and comprehensible explanations for program failures remains challenging. Automated tools such as ALHAZEN and AVICENNA address this by using distinct strategies: ALHAZEN employs binary decision trees to show failure-inducing conditions, while AVICENNA uses a specification language to model complex input dependencies. To assess their impact on usability and user efficiency, we conducted a controlled within-between-subjects user study with 18 participants tasked with resolving four software bugs using either tool or no support. Quantitative results showed that both tools improved debugging efficiency compared to manual methods, with AVICENNA offering more precise diagnostics but requiring higher cognitive effort. Qualitative feedback revealed a preference for AVICENNA’s expressiveness despite its complexity. Our findings show that effective debugging tools have tradeoffs between accuracy and interpretability to support developers’ decision-making in increasingly complex software environments.
Graßl, Isabella; Lazik, Christopher; Chakraborty, Shalini; Liebel, Grischa; Goulão, Miguel
Domain Diversity, Motivation, Inclusion, and Feedback in Software Modelling Education Proceedings Article
In: Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering, pp. 1051–1062, Association for Computing Machinery, Concordia University, Montreal, QC, Canada, 2026, ISBN: 9798400726361.
@inproceedings{10.1145/3803437.3805791,
title = {Domain Diversity, Motivation, Inclusion, and Feedback in Software Modelling Education},
author = {Isabella Graßl and Christopher Lazik and Shalini Chakraborty and Grischa Liebel and Miguel Goulão},
url = {https://doi.org/10.1145/3803437.3805791},
doi = {10.1145/3803437.3805791},
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address = {Concordia University, Montreal, QC, Canada},
series = {FSE Companion '26},
abstract = {Student engagement is critical for effective learning in software modelling, yet fostering motivation and inclusivity remains a challenge. While existing research has focused on modelling tools, notations, and assessment, little attention has been given to how the choice of problem domains and the diversity, relatability, and cultural perspectives they bring shape students' learning experiences. This study explores how problem domains and teaching methods influence motivation, engagement, inclusiveness, and feedback in modelling education. To investigate these dimensions, we conducted parallel surveys with 90 students and 22 educators. Our findings reveal disconnects between educator assumptions and student preferences: Students show greatest motivation for socially relevant domains and prefer choice in selection, while educators overestimate interest in study-related domains. The study identifies how minor design choices can exclude students. Students perceive feedback as meaningful when visibly acted upon. These findings suggest inclusive domain selection is central to student motivation; thus, we recommend student-centred domain selection.},
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Student engagement is critical for effective learning in software modelling, yet fostering motivation and inclusivity remains a challenge. While existing research has focused on modelling tools, notations, and assessment, little attention has been given to how the choice of problem domains and the diversity, relatability, and cultural perspectives they bring shape students' learning experiences. This study explores how problem domains and teaching methods influence motivation, engagement, inclusiveness, and feedback in modelling education. To investigate these dimensions, we conducted parallel surveys with 90 students and 22 educators. Our findings reveal disconnects between educator assumptions and student preferences: Students show greatest motivation for socially relevant domains and prefer choice in selection, while educators overestimate interest in study-related domains. The study identifies how minor design choices can exclude students. Students perceive feedback as meaningful when visibly acted upon. These findings suggest inclusive domain selection is central to student motivation; thus, we recommend student-centred domain selection.
2025
Cork, Alicia G.; Richardson, Mike; Lukosch, Heide K.; Khamis, Mohamed; Katins, Christopher; Krauβ, Veronika; Ruffin, Lauren; Papazoglakis, Sarah; Sanchez, Victoria; Pan, Xueni; Proulx, Michael J.; Fraser, Danaë Stanton
Shaping the future: principles for policy recommendations for responsible innovation in virtual worlds Journal Article
In: Frontiers in Virtual Reality, vol. Volume 6 - 2025, 2025, ISSN: 2673-4192.
@article{10.3389/frvir.2025.1645330,
title = {Shaping the future: principles for policy recommendations for responsible innovation in virtual worlds},
author = {Alicia G. Cork and Mike Richardson and Heide K. Lukosch and Mohamed Khamis and Christopher Katins and Veronika Krauβ and Lauren Ruffin and Sarah Papazoglakis and Victoria Sanchez and Xueni Pan and Michael J. Proulx and Danaë Stanton Fraser},
url = {https://www.frontiersin.org/journals/virtual-reality/articles/10.3389/frvir.2025.1645330},
doi = {10.3389/frvir.2025.1645330},
issn = {2673-4192},
year = {2025},
date = {2025-10-06},
urldate = {2025-01-01},
journal = {Frontiers in Virtual Reality},
volume = {Volume 6 - 2025},
abstract = {Extended Reality (XR) technologies are beginning to enter the mainstream and have the potential to change the way humans interact with computers on a global scale. As with all powerful tools, XR not only has the potential for enormous good, but also brings in a new set of challenges for policy to guide its innovation and development. This paper draws on a wide range of expertise from academia, research and development, and industry to collectively provide guiding principles for XR policy. The authors began discussions and developed a framework at a workshop at ACM SIGCHI 2024 and the ideas presented here are the result of debate, discussion, refinement, and offer next steps for the development of pervasive XR. We present three main principles for XR policy: Trust, Agency, and Inclusivity, along with a cross-cutting theme of Future-Proofing. Each principle is broken down and we offer example implementations. This paper aims to build upon previous work and efforts for fair and equitable XR for all, and further dialogue towards tangible changes in policy to help guide responsible innovation in virtual worlds.},
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Extended Reality (XR) technologies are beginning to enter the mainstream and have the potential to change the way humans interact with computers on a global scale. As with all powerful tools, XR not only has the potential for enormous good, but also brings in a new set of challenges for policy to guide its innovation and development. This paper draws on a wide range of expertise from academia, research and development, and industry to collectively provide guiding principles for XR policy. The authors began discussions and developed a framework at a workshop at ACM SIGCHI 2024 and the ideas presented here are the result of debate, discussion, refinement, and offer next steps for the development of pervasive XR. We present three main principles for XR policy: Trust, Agency, and Inclusivity, along with a cross-cutting theme of Future-Proofing. Each principle is broken down and we offer example implementations. This paper aims to build upon previous work and efforts for fair and equitable XR for all, and further dialogue towards tangible changes in policy to help guide responsible innovation in virtual worlds.
Rücker, Michael T.; Büchting, Carolin; Kosch, Thomas
Understanding the Effect of Risk Perception on the Acceptance and Use of Large Language Models Among University Students Journal Article
In: Proceedings of the ACM on Human-Computer Interaction, vol. 9, no. CSCW512, 2025.
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title = {Understanding the Effect of Risk Perception on the Acceptance and Use of Large Language Models Among University Students},
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Lazik, Christopher; Kauter, Charlotte; Nunes, Inês; Ziglowski, Aaron; Pryma, Alina; Katins, Christopher; Grunske, Lars; Kosch, Thomas
The Good, the Bad, and the Uncanny: Investigating Diversity Aspects of LLM-Generated Personas for Requirements Engineering Proceedings Article
In: 2025 IEEE 33rd International Requirements Engineering Conference (RE), pp. 244-256, 2025.
@inproceedings{11190356,
title = {The Good, the Bad, and the Uncanny: Investigating Diversity Aspects of LLM-Generated Personas for Requirements Engineering},
author = {Christopher Lazik and Charlotte Kauter and Inês Nunes and Aaron Ziglowski and Alina Pryma and Christopher Katins and Lars Grunske and Thomas Kosch},
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Schön, Dominik; Kosch, Thomas; Schmitz, Martin; Günther, Sebastian; Mühlhäuser, Max; Müller, Florian
From Pegs to Pixels: A Comparative Analysis of the Nine Hole Peg Test and a Digital Copy Drawing Test for Fine Motor Control Assessment Proceedings Article
In: Proceedings of the 27th International Conference on Human-Computer Interaction with Mobile Devices and Services, ACM, New York, NY, USA, 2025.
@inproceedings{schoen2025from,
title = {From Pegs to Pixels: A Comparative Analysis of the Nine Hole Peg Test and a Digital Copy Drawing Test for Fine Motor Control Assessment},
author = {Dominik Schön and Thomas Kosch and Martin Schmitz and Sebastian Günther and Max Mühlhäuser and Florian Müller},
url = {https://dl.acm.org/doi/10.1145/3743714},
doi = {10.1145/3743714},
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Lazik, Christopher; Katins, Christopher; Kauter, Charlotte; Jakob, Jonas; Jay, Caroline; Grunske, Lars; Kosch, Thomas
The Impostor is Among Us: Can Large Language Models Capture the Complexity of Human Personas? Proceedings Article
In: Proceedings of Mensch und Computer 2025, ACM, Chemnitz, Germany, 2025, ISBN: 979-8-4007-1582-2/25/08.
@inproceedings{lazik2025the,
title = {The Impostor is Among Us: Can Large Language Models Capture the Complexity of Human Personas?},
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url = {https://dl.acm.org/doi/10.1145/3743049.3743057},
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Katins, Christopher; Chen, Katja; Lazik, Christopher; Knierim, Pascal; Marky, Karola; Kosch, Thomas
Public Perceptions on Psychological and Social Impacts of Virtual and Augmented Reality Proceedings Article
In: Proceedings of Mensch und Computer 2025, ACM, Chemnitz, Germany, 2025, ISBN: 979-8-4007-1582-2/25/08.
@inproceedings{katins2025public,
title = {Public Perceptions on Psychological and Social Impacts of Virtual and Augmented Reality},
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Knierim, Pascal; Höck, Jacob; Kosch, Thomas
Wrist-Powered Touch: Evaluating Smartwatch-Based Touch Gesture Recognition for Interaction in Extended Reality Proceedings Article
In: Proceedings of Mensch und Computer 2025, ACM, Chemnitz, Germany, 2025, ISBN: 979-8-4007-1582-2/25/08.
@inproceedings{knierim2025wrist,
title = {Wrist-Powered Touch: Evaluating Smartwatch-Based Touch Gesture Recognition for Interaction in Extended Reality},
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url = {https://dl.acm.org/doi/full/10.1145/3743049.3743090},
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Hallgarten, Philipp; Kosch, Thomas; Grosse-Puppendahl, Tobias; Kasneci, Enkelejda
Route2Vec: Enabling Eficient Use of Driving Context through Contextualized Route Representations Proceedings Article
In: Proceedings of Mensch und Computer 2025, ACM, Chemnitz, Germany, 2025, ISBN: 979-8-4007-1582-2/25/08.
@inproceedings{hallgarten2025route2vec,
title = {Route2Vec: Enabling Eficient Use of Driving Context through Contextualized Route Representations},
author = {Philipp Hallgarten and Thomas Kosch and Tobias Grosse-Puppendahl and Enkelejda Kasneci},
url = {https://dl.acm.org/doi/full/10.1145/3743049.3743056},
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Kosch, Thomas; Feger, Sebastian
Prompt-Hacking: The New p-Hacking? Miscellaneous
2025.
@misc{kosch2025prompt,
title = {Prompt-Hacking: The New p-Hacking?},
author = {Thomas Kosch and Sebastian Feger},
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Villa, Steeven; Barth, Lisa L.; Chiossi, Francesco; Welsch, Robin; Kosch, Thomas
Whose Mind Is It Anyway? A Systematic Review and Exploration on Agency in Cognitive Augmentation Journal Article
In: Computers in Human Behavior: Artificial Humans, pp. 100158, 2025, ISSN: 2949-8821.
@article{villa2025whose,
title = {Whose Mind Is It Anyway? A Systematic Review and Exploration on Agency in Cognitive Augmentation},
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