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cs.HC updates on arXiv.org

A Framework for Measuring Appropriate Reliance on Set-Valued AI Advice DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Puzzle Solving From Explanation to Diagnosis: Next Generation Interactive Video Coach with Misstep Awareness SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice Formalizing all indexed mathematics as a benchmark for general reasoning, with the example of implementing dilatations of categories Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset From Prompt to Service: An SLM-Based Agent Orchestration Gateway for AI-Driven Virtual Worlds What LLMs Must Forget to Teach Effectively: A DIY Approach to Premodern Japanese Language Pedagogy Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build
Evaluation of Task Specific Productivity Improvements Usi...
Brian S. Freeman, Kendall Arriola, Dan Cottell, Emmett Lawlor, M · 2024-09-06 · via cs.HC updates on arXiv.org

This study evaluates the productivity improvements achieved using a generative artificial intelligence personal assistant tool (PAT) developed by Trane Technologies. The PAT, based on OpenAI's GPT 3.5 model, was deployed on Microsoft Azure to ensure secure access and protection of intellectual property. To assess the tool's productivity effectiveness, an experiment was conducted comparing the completion times and content quality of four common office tasks: writing an email, summarizing an article, creating instructions for a simple task, and preparing a presentation outline. Sixty-three (63) participants were randomly divided into a test group using the PAT and a control group performing the tasks manually. Results indicated significant productivity enhancements, particularly for tasks involving summarization and instruction creation, with improvements ranging from 3.3% to 69%. The study further analyzed factors such as the age of users, response word counts, and quality of responses, revealing that the PAT users generated more verbose and higher-quality content. An 'LLM-as-a-judge' method employing GPT-4 was used to grade the quality of responses, which effectively distinguished between high and low-quality outputs. The findings underscore the potential of PATs in enhancing workplace productivity and highlight areas for further research and optimization.