5 papers
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: Structuring Non-Linear LLM Interactions on a Canvas
Rifat Mehreen Amin, Alperen Adatepe, Daniela Fernandes +2
Conversational interfaces powered by large language models (LLMs) are widely used for ideation and analysis, yet their linear structure limits exploration of alternatives and manag…
A Deep Learning Model of Mental Rotation Informed by Interactive VR Experiments
Raymond Khazoum, Daniela Fernandes, Aleksandr Krylov +2
Mental rotation -- the ability to compare objects seen from different viewpoints -- is a fundamental example of mental simulation and spatial world modeling in humans. Here we prop…
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…