ai-expertise-interrogation-designer

An AI literacy activity helps students use genuine domain expertise to identify AI distortions and omissions. By activating prior knowledge before querying AI, learners compare their expertise directly against AI output, producing durable skepticism through structured interrogation and distortion annotation rather than generic warnings.

Helping students identify AI limitations requires comparing their genuine expertise against AI outputs using a structured distortion taxonomy. This Skill generates calibrated interrogation questions and annotation codes so students can classify specific distortion types and synthesize patterns of AI overconfidence or omission across different domains.

Structuring a discussion facilitation guide for AI literacy involves creating prompts for group synthesis of detected distortions. This Skill generates a complete discussion guide that helps learners classify distortion patterns, compare surface factual verification with deeper nuance checks, and share evidence of systematic AI limitations across various student-chosen expertise domains.

Yes, you can generate a calibrated AI interrogation activity for any student expertise domain and education level. You simply provide the specific student expertise domain and student level, and the Skill adapts the expertise activation protocol, distortion taxonomy, and discussion guide to fit those classroom parameters.

An expertise activation protocol requires students to document what they already know about their chosen domain before interacting with AI. This creates a direct reference point to compare against AI output, ensuring that detected distortions and omissions are grounded in the learner's actual knowledge rather than generic assumptions.