44 citations · 48 across the 4 of their papers we have counts for
6 papers
Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimization
Changkun Ou, Sven Mayer, Daniel Buschek +1
Human-in-the-loop optimization refers to systems that present choices to a human decision-maker and query their judgment to optimize system parameters iteratively. The user interfa…
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…
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…
Conversations in Space: Non-Linear LLM Interaction in Everyday Use
Rifat Mehreen Amin, Alperen Adatepe, Daniela Fernandes +2
As LLM conversations grow, their histories capture alternative directions, decisions, and evolving lines of thought that can be difficult to navigate through chat alone. We investi…
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…