Channels - Evaluating the False Trust engendered by LLM Explanations :: FRELIP Discovery

Similar Items: Evaluating the False Trust engendered by LLM Explanations

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Less Interaction But More Explanation: A Communication Perspective on Agentic AI Interfaces
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LLARS: Enabling Domain Expert & Developer Collaboration for LLM Prompting, Generation and Evaluation
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LLM-Based Educational Simulation: Evaluating Temporal Student Persona Stability Across ADHD Profiles
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LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning
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Visual Fingerprints for LLM Generation Comparison
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Exploring Interaction Paradigms for LLM Agents in Scientific Visualization
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LLM-Augmented Semantic Steering of Text Embedding Projection Spaces
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Patterns of Developer Adoption of LLM-Generated Code Refactoring Suggestions
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Tailoring Scaffolding to Diagnostic Strategies: Theory-Informed LLM-Based Agents
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Characterizing Students' LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks
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GazeMind: A Gaze-Guided LLM Agent for Personalized Cognitive Load Assessment
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When Are LLM Inferences Acceptable? User Reactions and Control Preferences for Inferred Personal Information
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From LLM-Driven Trading Card Generation to Procedural Relatedness: A Pokémon Case Study
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AwareLLM: A Proactive Multimodal Ecosystem for Personalized Human-AI Collaboration to Enhance Productivity
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LaTA: A Drop-in, FERPA-Compliant Local-LLM Autograder for Upper-Division STEM Coursework
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Closing the Loop: Unified 3D Scene Generation and Immersive Interaction via LLM-RL Coupling
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Prober.ai: Gated Inquiry-Based Feedback via LLM-Constrained Personas for Argumentative Writing Development
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Attention: What Prevents Young Adults from Speaking Up Against Cyberbullying in an LLM-Powered Social Media Simulation
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Stayin' Aligned Over Time: Towards Longitudinal Human-LLM Alignment via Contextual Reflection and Privacy-Preserving Behavioral Data
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Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wild
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Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarios
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Principles and Guidelines for Randomized Controlled Trials in AI Evaluation
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Leveraging fNIRS to Evaluate Workload for Adaptive Training in Virtual Reality
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Useful for Exploration, Risky for Precision: Evaluating AI Tools in Academic Research