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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 user co-designed digital INtervention for Child LangUag...
Rafiah Ansari · 2023-04-11 · via cs.HC updates on arXiv.org

Around ten percent of children may present with a disorder where language does not develop as expected. This often affects vocabulary skills, i.e., finding the words to express wants, needs and ideas, which can influence behaviours linked to wellbeing and daily functioning, such as concentration, independence, social interactions and managing emotions. Without specialist support, needs can increase in severity and continue to adulthood. The type of support, known as interventions, showing strongest evidence for improving vocabulary with some signs of improved behaviour and wellbeing are ones that use word webs. These are diagrams consisting of lines that connect sound and meaning information about a word to strengthen the child's word knowledge and use. The diagrams resemble what is commonly known as mind-maps and are widely used by Speech and Language Therapists in partnership with school educators to help children with language difficulties. In addition, interventions delivered through mobile-devices has led in some cases to increased vocabulary gains with positive influence on wellbeing and academic attainment. With advances in technology and availability of user-friendly mobile devices to capture, combine and replay multimedia, new opportunities for designing bespoke vocabulary instruction have emerged that are without timing and location constraints. This brings the potential to engage and motivate users and harbour independence through functional strategies that support each child's unique language needs. To achieve this, children with language disorder, their parents/carers, support professionals and software development team members must work jointly to create an intervention that is fit for purpose. This is the first research planned to explore the collaborative development and acceptability of a digitally enhanced vocabulary intervention for child language disorder.