🏷️ trust-calibration
9 pages tagged with trust-calibration(6 articles, 3 concepts)
📄 ResidencyRL: Reinforcement Learning in Simulated Clinical Environments
> **Synthesis:** Liévin et al. (2026) present **ResidencyRL**, a reinforcement learning method for training clinical AI agents through simulated multi-turn clinical encounters (up to 60 dialogue turns…
📄 The Competence Paradox: Negotiating Ease, Risk, and Creative Identity in Text-to-Image Generative AI Use Among Art and Design Students
> **Synthesis:** Liu, Meng, and Zhang (2026) examined technology acceptance of text-to-image (T2I) generative AI in art and design education from both educators' and students' perspectives, using a mo…
🏷️ Student Misconceptions about AI
> **Student misconceptions about AI** — the inaccurate beliefs students hold about what AI systems are, what they do, and what using them means for learning, especially in academic contexts. Misconcep…
🏷️ Trust Calibration
> **Trust calibration** — the metacognitive capacity to align one's confidence in an AI system with its actual reliability in a given context, knowing when to trust and when to question its output. Tr…
🏷️ Over-Reliance
> **Over-reliance** — excessive or uncalibrated dependence on AI tools where students delegate cognitive work they should perform themselves, resulting in reduced learning, diminished agency, and the …
📄 Pragmatic users and skeptical nonusers: A qualitative typology of ChatGPT adoption in physics education
> **Synthesis:** Becker, Bauer, Schrader, Bitzenbauer & Veith (2026) analyze 1,189 survey responses from physics students using qualitative content analysis and latent class analysis, identifying two …
📄 Trust-utility gap in introductory physics education: Students' adoption, domain-specific skepticism, and preferences for AI integration
> **Synthesis:** Fouad & Bentley (2026) survey 81 introductory physics students and find a striking 50-percentage-point trust-utility gap: 91% use AI for coursework but only 41% trust AI physics expla…
📄 Calibrating Trustworthiness: Co-Designing Metrics and Visualizations for Evaluating LLMs in Education
> **Calibrating Trustworthiness: Co-Designing Metrics and Visualizations for Evaluating LLMs in Education** — Longitudinal co-design with learning engineers building an LLM-powered digital textbook. C…
📄 Warning About AI Fallibility Increases Help-Seeking in an Intelligent Tutoring System
> **Synthesis:** Recent work in Technology-Enhanced Learning and HumanComputer Interaction highlights the importance of transparency and trust calibration in AI-supported learning environments as they…