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cs.HC updates on arXiv.org

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 Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
GenAI Distortion: The Effect of GenAI Fluency and Positiv...
Xiantong Yang, Mengmeng Zhang · 2024-04-27 · via cs.HC updates on arXiv.org

The introduction of generative artificial intelligence (GenAI) into educational practices has been transformative, yet it brings a crucial concern about the potential distortion of users' beliefs. Given the prevalence of GenAI among college students, examining the psychological mechanisms that lead to GenAI distortion from both technological factors and the individual's psychological processes is a critical priority. A mixed-methods approach is employed to test the proposed hypotheses. Study 1 (N = 10) revealed through qualitative analysis that GenAI's fluent outputs significantly engaged college students, eliciting positive emotional responses during an interaction. GenAI's tendency to conflate fact with fiction often led to presentations of unrealistic and exaggerated information, potentially distorting users' perceptions of reality-a phenomenon termed GenAI distortion. Following these insights, Study 2 (cross-sectional survey, N = 999) and Study 3 (experimental manipulation, N = 175) explored how GenAI fluency affects college students' GenAI distortion and examined the mediating effect of positive affect. The results indicated that GenAI fluency predicts GenAI distortion via the mediating role of positive affect. Our findings provide theoretical foundations and practical implications for understanding GenAI distortion among college students.