Niklas Weller

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Weller

First name

Niklas

niklas.weller@unisg.ch

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HaessigDB: A Database of Irritable Speech with Intensity Grading
(2026-09-27)
Niklas Weller
;
Marc Christopher Grau
;
Ivo Blohm
Type:conference paper
Journal:Interspeech 2026
URI:https://alexandria.unisg.ch/handle/20.500.14171/127779
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Generating Synthetic Multi-Turn Conversations for Scalable Functional Testing of Conversational AI Systems
(2026)
Niklas Weller
;
Shijing Cai
;
Syang Zhou
Artificial intelligence (AI) systems with conversational interfaces are increasingly used to augment humans and even automate complex workflows. Yet systematic functional testing remains difficult because of open-ended multi-turn input. Existing evaluation benchmarks focus on isolated single-turn performance and provide limited insight into how systems behave in realistic interaction scenarios. This paper investigates how synthetic multi-turn conversations can be generated to support scalable functional testing of conversational AI systems. Using a design science research approach, we develop an artifact that employs LLM-based role play to generate synthetic conversations for testing an insurance claim decision system. The generation process combines software testing techniques such as equivalence partitioning and boundary value analysis with persona-based user simulation. We report preliminary results from the first design cycle and derive six design principles for generating diverse, faithful, and diagnostically useful conversational test data.
Type:conference paper
Journal:European Conference on Information Systems (ECIS)
URL:https://aisel.aisnet.org/ecis2026/datasc_isresearch/datasc_isresearch/10/
URI:https://alexandria.unisg.ch/handle/20.500.14171/132201
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Improving AI-Assisted Decision-Making: Insights into Example-Based Explanations and Cognitive Load in Sales Forecasting
(2025)
Niklas Weller
;
Anuschka Schmitt
;
Tobias Fahse
;
Ivo Blohm
AI-assisted decision-making often underperforms due to users' difficulties in effectively interacting with AI-based systems. This study investigates how example-based explanations—specifically factual and counterfactual explanations—impact users' decision-making performance and their tendency to overrule algorithmic advice in a sales forecasting task. We also examine the mediating role of cognitive load. By analyzing 1330 forecasts made in an online lab experiment, we find that factual explanations significantly enhance forecasting performance by enabling users to more effectively overrule algorithmic advice. While counterfactual explanations also result in performance gains, the increase is smaller and operates primarily through reduced deviation from AI advice due to cognitive overload. Our findings suggest that factual explanations align well with human cognitive processes, facilitating better decision outcomes, while counterfactuals may overwhelm users cognitively. This study contributes to a deeper understanding of explainable AI design in decision-making contexts, emphasizing the importance of aligning explanations with users' cognitive capacities.
Type:conference paper
URL:https://aisel.aisnet.org/ecis2025/ai_org/ai_org/15/
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/124138

Niklas Weller

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