Cambridge Judge highlighted peer-reviewed studies examining trust in healthcare AI. Choi et al. found that AI-involved hospital adverse events increase perceived hospital responsibility and complaint or legal-action intentions unless physician-AI collaboration is visibly interactive.
Cambridge Judge highlighted peer-reviewed studies examining trust in healthcare AI.
Reis et al. found that participants who believed they were reporting symptoms to an AI chatbot gave lower-quality symptom reports than those reporting to a physician. AI performance can therefore degrade before the model sees the case because of how humans communicate with AI.
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Cambridge Judge Business School
A review of evidence across cognitive science, education, medicine, and human-factors research finds that fully offloading mental work to AI can weaken the acquisition and retention of the specific skills people stop practicing. The authors distinguish that evidence from broader claims about declining intelligence: effects on foundational abilities such as attention and working memory remain uncertain, while AI used as a collaborator, tutor, or source of feedback can preserve or improve learning.
In a randomized field experiment across a chain of middle and high schools in Turkey, giving teachers a generative-AI support tool reduced students’ intrinsic motivation by 0.11 standard deviations. Average academic performance did not change, but students taught by lower-performing teachers experienced significant declines in both performance and confidence, showing that a tool that makes lesson preparation easier for teachers does not automatically improve the student experience.
The European Commission has issued operational guidance for Article 50 of the AI Act before its transparency obligations begin applying on August 2, 2026. Providers must disclose when people are interacting with systems such as chatbots, agents, or avatars and make generative outputs detectable through machine-readable marking; deployers must disclose emotion-recognition or biometric-categorization uses and clearly label deepfakes and certain AI-generated public-interest text when it lacks human review or editorial control.