44 citations · 50 across the 5 of their papers we have counts for
9 papers
Confident, Not Wiser: The Dunning-Kruger Effect in Human-AI Interaction
Daniela Fernandes, Michelle Rausch, Agnes Mercedes Kloft +2
AI assistance can improve performance without improving self-assessment. We report a study (N=366) comparing Human alone and Human+AI performance on reasoning tasks, for which the…
How Did Writing Change At CHI? Analyzing 44 Years of CHI Writing Before and After the Introduction of Large Language Models
Thomas Kosch, Robin Welsch, Michael Hedderich +1
The availability of Large Language Models (LLMs) reshaped scientific discourse at a linguistic level. LLMs are assumed to homogenize academic writing, flattening it into a single g…
Beyond "ChatGPT Can Make Mistakes": Designing Interventions to Support Metacognitive Monitoring in AI-Assisted Work
Manuel A. D. Santos, Paul Thiesse, Steeven Villa +4
AI assistance places a metacognitive demand on users, who must judge their own competence and the system's. Yet designers lack comparative evidence on which interventions to choose…
Available but Unclaimed: An Empirical Study of Human-AI Synergy
Robin Welsch, Michelle Rausch, Pascal Knierim +4
People increasingly reason with large language models (LLMs), yet complementary capabilities do not guarantee outperforming both components. In a between-subjects study, participan…
When the AI Leaves the Tailorshop: Measuring What an LLM Advisor Leaves Behind in Complex Problem Solving
Robin Welsch
Complex problem solving depends on acting effectively and understanding how a system works. AI advice may support these outcomes unequally. Two preregistered experiments compared p…
Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition
Daniela Fernandes, Daniel Buschek, Lev Tankelevitch +2
Large Language Model interfaces are increasingly verbose, exposing intermediate reasoning traces alongside final answers. Traces are framed as transparency mechanisms, yet it is un…
The AI Memory Gap: Users Misremember What They Created With AI or Without
Tim Zindulka, Sven Goller, Daniela Fernandes +2
As large language models (LLMs) become embedded in interactive text generation, disclosure of AI as a source depends on people remembering which ideas or texts came from themselves…
Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction
Daniela Fernandes, Steeven Villa, Salla Nicholls +6
Optimizing human-AI interaction requires users to reflect on their own performance critically. Our paper examines whether people using AI to complete tasks can accurately monitor h…
Questionnaires for Everyone: Streamlining Cross-Cultural Questionnaire Adaptation with GPT-Based Translation Quality Evaluation
Otso Haavisto, Robin Welsch
Adapting questionnaires to new languages is a resource-intensive process often requiring the hiring of multiple independent translators, which limits the ability of researchers to…