activity
20242026
most citedExploration of LLMs, EEG, and behavioral data to measure and support attention and sleep

2 citations · 2 across the 2 of their papers we have counts for

collaborators

8 papers

eess.SP20262 cited

Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep

Akane Sano, Judith Amores, Mary Czerwinski

We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving attention and sleep. We investigate the use of…

cs.HC2026

From Gaze to Guidance: Interpreting and Adapting to Users' Cognitive Needs with Multimodal Gaze-Aware AI Assistants

Valdemar Danry, Javier Hernandez, Andrew Wilson +2

Current LLM assistants are powerful at answering questions, but they have limited access to the behavioral context that reveals when and where a user is struggling. We present a ga…

cs.HC2025

SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversations

Jina Suh, Lindy Le, Erfan Shayegani +5

Empathy is increasingly recognized as a key factor in human-AI communication, yet conventional approaches to "digital empathy" often focus on simulating internal, human-like emotio…

cs.HC2025

Affective Air Quality Dataset: Personal Chemical Emissions from Emotional Videos

Jas Brooks, Javier Hernandez, Mary Czerwinski +1

Inspired by the role of chemosignals in conveying emotional states, this paper introduces the Affective Air Quality (AAQ) dataset, a novel dataset collected to explore the potentia…

cs.HC2025

From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions

Subigya Nepal, Javier Hernandez, Talie Massachi +7

Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data…

cs.HC2025

Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration

Matthew Russell, Aman Shah, Giles Blaney +3

AI-based interactive assistants are advancing human-augmenting technology, yet their effects on users' mental and physiological states remain under-explored. We address this gap by…