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Knowing When to Ask: Self-Gated Clarification for Hierarchical Language Agents Collaborative Human-Agent Protocol (CHAP) UXBench: Benchmarking User Experience in AI Assistants Impedance MPC for Physical Human-Robot Interaction: Predictive Disturbance Rejection with Joint-Limit Safety 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 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 The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts 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 Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build Learning to Decide with AI Assistance under Human-Alignment Positive Alignment: Artificial Intelligence for Human Flourishing Sycophantic AI makes human interaction feel more effortful and less satisfying over time Exploring Interaction Paradigms for LLM Agents in Scientific Visualization The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers Participatory provenance as representational auditing for AI-mediated public consultation Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading Can LLMs Reason About Attention? 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Task-Redirecting Agent Persuasion Benchmark for Web Agents EEG-based AI-BCI Wheelchair Advancement: Hybrid Deep Learning with Motor Imagery for Brain Computer Interface K2MUSE: A human lower-limb multimodal walking dataset spanning task and acquisition variability for rehabilitation robotics Privacy-Preserving Empathy Detection in Video Interactions GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations Creating and Evaluating Personas Using Generative AI: A Scoping Review of 81 Articles Social Human Robot Embodied Conversation (SHREC) Dataset: Benchmarking Foundational Models' Social Reasoning Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitation FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users ExplainReduce: Generating global explanations from many local explanations AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results RECOVER: Designing a Large Language Model-based Remote Patient Monitoring System for Postoperative Gastrointestinal Cancer Care "Would You Want an AI Tutor?" 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Exploring Parent's Needs for Children-Centered AI to Support Preschoolers' Interactive Storytelling and Reading Activities
Yuling Sun, Jiaju Chen, Bingsheng Yao, Jiali Liu, Dakuo Wang, Xi · 2024-01-25 · via cs.HC updates on arXiv.org

Interactive storytelling is vital for preschooler development. While children's interactive partners have traditionally been their parents and teachers, recent advances in artificial intelligence (AI) have sparked a surge of AI-based storytelling and reading technologies. As these technologies become increasingly ubiquitous in preschoolers' lives, questions arise regarding how they function in practical storytelling and reading scenarios and, how parents, the most critical stakeholders, experience and perceive these technologies. This paper investigates these questions through a qualitative study with 17 parents of children aged 3-6. Our findings suggest that even though AI-based storytelling and reading technologies provide more immersive and engaging interaction, they still cannot meet parents' expectations due to a series of interactive and algorithmic challenges. We elaborate on these challenges and discuss the possible implications of future AI-based interactive storytelling technologies for preschoolers.