AI Expertise Interrogation Designer

What This Skill Does

Generates an activity, Kharbach's (2026) "Funhouse Mirror", in which students use their own domain expertise as the instrument for detecting AI distortions, omissions, and overconfidence. This reverses the usual expert-novice dynamic in AI evaluation: in most AI literacy activities, students are novices being warned about AI's limitations. In this activity, students ARE the domain experts. They ask AI about something they know genuinely well, a subject they have studied deeply, a sport they play competitively, a cultural context they grew up in, a local geography they know intimately, and they use their knowledge to identify what the AI gets wrong, oversimplifies, flattens, or presents with false confidence. The pedagogical mechanism is calibrated AI skepticism through direct confrontation: finding an AI error in your domain of expertise creates a visceral, durable understanding that AI is not omniscient. A student who has found AI confidently wrong about their sport, their cultural tradition, or their hometown has earned a much more reliable skepticism than one who has been warned abstractly that "AI can make mistakes." The output includes an expertise activation protocol (students document their own knowledge before consulting AI), calibrated interrogation questions, a distortion annotation protocol, a distortion taxonomy for the specific domain, and a discussion guide for synthesising findings across the class's varied expertise areas.

Evidence Foundation