4 citations · 4 across the 3 of their papers we have counts for
4 papers · 1 filter
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…