A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
Shneiderman, Human-centered artificial intelligence: Reliable, safe & trustworthy, International Journal of Human-Computer Interaction 36 (2020) 495–504
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Youth on Character.AI use chatbots for emotional restoration, creative exploration, and identity transformation, yielding a new three-intent framework and seven-archetype taxonomy from Discord discourse analysis.
Self-correction by social chatbots preserves trustworthiness and expertise while leveraging social connection to drive belief change, unlike external corrections which eliminate that link.
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The Perceived Cooperativity Scale and Teaming Perception Scale were developed from theory and validated across three studies to reliably measure subjective quality of human-AI cooperation.
Brief2Design supports a multi-phased workflow for AI-assisted graphic design from briefs, increasing prompt diversity and requirement handling ratings but requiring more generation time than conversational baselines.
Interviews and probe sessions show that mixed reality supports continuous hybrid workflows in UI/UX design, leading to four proposed design dimensions for future MR systems.
A work-in-progress curriculum mapping framework for AI education that connects technical systems, societal impacts, and workforce competencies, with early analysis of six courses showing strong technical coverage but weak workforce assessment.
Comparative study of VRChat Discord finds distinct engagement, response dynamics, and attitudes in human versus AI support channels.
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What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.