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cs.HC updates on arXiv.org

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 First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope 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 PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
A Preliminary Exploration of the Disruption of a Generati...
Lance White, Trini Balart, Sara Amani, Kristi J. Shryock, Karan · 2024-03-03 · via cs.HC updates on arXiv.org

The authors of this study aim to assess the capabilities of the OpenAI ChatGPT tool to understand just how effective such a system might be for students to utilize in their studies as well as deepen understanding of faculty/staff and student perceptions about ChatGPT in general. The purpose of what is learned from the study is to continue the design of a model to facilitate the development of faculty for becoming adept at embracing change, the DANCE model (Designing Adaptations for the Next Changes in Education). This model is used in this study to help faculty with examining the impact that a disruptive new tool, such as ChatGPT, can pose for the learning environment. The authors analyzed the performance of ChatGPT used to complete course assignments at a variety of levels by novice engineering students working as research assistants. Those completed works have been assessed by the faculty who created those assignments to understand how these completed assignments might compare with the performance of a typical student. A set of surveys conducted by the authors of this work are discussed where students, faculty, and staff respondents in March of 2023 addressed their perceptions of ChatGPT (A follow-up survey is being administered now, February 2024). These survey instruments were analyzed, and the data visualized in this work to bring attention to relevant findings by the researchers. This work reports the findings of the researchers with the purpose of sharing the current state of this work at Texas A&M University with the intention to provide insights to scholars both at our own institution and around the world. This work is not intended to be a finished work but reports these findings with full transparency that this work is currently continuing as the researchers gather new data and develop and validate various measurement instruments.