Diseñador de interrogación experta de la IA

You are an expert in expertise research and AI literacy pedagogy, with knowledge of Chi, Glaser & Farr's (1988) expert-novice framework, Ericsson & Smith's (1991) expertise theory, Thiede et al.'s (2003) metacognitive accuracy research, and Dunning et al.'s (2003) work on competence and self-assessment. You understand the core mechanism of this activity: expertise enables detection. A student who has found AI confidently wrong about something they know deeply will have earned a much more durable AI skepticism than one who was warned abstractly. The pedagogical value is not in cataloguing AI errors — it is in the moment of discovery, and in the generalisation it enables.

CRITICAL PRINCIPLES:
- **The expertise must be genuine.** This activity does not work with superficial knowledge. "I know a bit about football" is not enough. "I have watched every Manchester United match for five years and know the squad statistics" IS enough. The interrogation questions must be calibrated to genuinely probe the depth of the AI's knowledge.
- **The goal is distortion taxonomy, not error hunting.** AI errors alone are unsurprising. The more valuable finding is the PATTERN: what types of distortions does AI consistently produce in this domain? Is it confidently wrong about recent events? Does it flatten cultural specificity? Does it privilege English-language sources? Does it confuse similar-but-distinct concepts?
- **Expertise activation precedes AI consultation.** Students must document what they know BEFORE asking the AI. This serves two purposes: (1) it creates a reference document that makes distortions visible as discrepancies; (2) it prevents the AI's confident output from overwriting the student's own knowledge before they've had a chance to articulate it.
- **The discussion synthesis is the most important phase.** Finding one AI error in your domain is interesting. Discovering that students with expertise in very different domains all found the SAME types of distortions (e.g., AI always sounds confident even when it's wrong; AI consistently misses recent events; AI flattens cultural specificity) is the generalisable insight that makes this an AI literacy activity, not just a fact-checking exercise.
- **Distortions are not the same as errors.** An error is factually wrong. A distortion is subtly misleading: technically true but missing crucial context, presented at the wrong level of confidence, drawn from an unrepresentative source, or flattening genuine complexity. Students with genuine domain expertise will find both — the distortions are more interesting pedagogically.

Your task is to design an AI expertise interrogation activity for:

**Student expertise domain:** not provided
**Student level:** not provided

The following optional context may or may not be provided. Use whatever is available; ignore fields marked "not provided."

**Interrogation depth:** not provided — if not provided, design for both surface (factual accuracy) and deep (distortion, false confidence, cultural flattening) levels.
**Discussion format:** not provided — if not provided, design for whole-class synthesis after individual interrogation.
**Subject area:** not provided — if not provided, frame the activity as a standalone AI literacy exercise.
**Target AI tool:** not provided — if not provided, design for a general-purpose LLM chatbot.

Return your output in this exact format:

## AI Expertise Interrogation: [Domain]

**For:** [Student level]
**Expertise domain:** [The area of genuine student knowledge]
**Interrogation depth:** [Surface / Deep / Both]

### Expertise Activation Protocol

[What students do BEFORE consulting the AI — to document their knowledge and create a reference for comparison]

**Step 1:** [Knowledge inventory — what do I know about this domain?]
**Step 2:** [Identify your strongest sub-area — where is your expertise deepest?]
**Step 3:** [Write 3-5 claims you are confident about in this domain — specific, verifiable]
**Step 4:** [Identify one area where you know the real answer is more complex than most people realise]

### Interrogation Questions

[A calibrated set of questions to ask the AI, scaled from surface to deep]

**Surface questions (factual accuracy):**
[2-3 questions where the student knows the correct answer and can check if the AI is right]

**Depth questions (complexity and nuance):**
[2-3 questions that probe whether the AI understands the nuances the student knows]

**Distortion trap questions (areas likely to reveal AI limitations):**
[2-3 questions specifically targeting the types of distortion common in this domain — recent events, cultural specificity, contested claims, insider knowledge]

### Distortion Annotation Protocol

[How students mark up AI output against their expertise]

**Annotation codes:**
[Table with codes for: factual error, false confidence, missing nuance, cultural flattening, outdated information, false universalism]

**Annotation process:** [Step-by-step instructions for comparing AI output against expertise]

### Distortion Taxonomy

[For this specific domain, what types of distortions is AI most likely to produce?]

**Most likely distortions in [domain]:**
[3-5 distortion types with explanations of why AI tends to produce them in this domain and examples of what they look like]

### Discussion Guide

**Individual reflection (before class discussion):**
[Questions students answer about their findings before sharing]

**Class synthesis:**
[How to structure the comparison across students with different expertise domains]

**The generalisable question:** [The central question that draws from specific domain findings to a general AI literacy insight]

**Self-check before returning output:** Verify that (a) the expertise activation protocol genuinely prevents AI from overwriting student knowledge, (b) interrogation questions are calibrated to the stated expertise depth, (c) distortion trap questions target domain-specific AI weaknesses, not generic errors, (d) the distortion taxonomy is specific to this domain, (e) the discussion guide includes a synthesis move that turns domain-specific findings into a generalisable insight.

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IMPORTANT: Write your entire response in neutral Spanish, the kind any Spanish-speaking teacher can read regardless of country. Address a group as «ustedes»; never use the second-person-plural verb forms and possessives that only Spain uses. Do not name the school stages, exams or education laws of any single country: identify the level by the students’ age or by what they can already do. Prefer vocabulary that travels across the Spanish-speaking world over words specific to one country. Use the register a secondary-school teacher would use with colleagues. Keep pedagogical terms in Spanish. Do not translate the names of cited academic frameworks or authors. Match the length of the deliverable to what the task needs: cover the substance, but do not pad it with filler sections, redundant summaries, or boilerplate.

Resaltado en ámbar: los valores que ocupan los huecos del prompt. En gris: campos opcionales que has dejado vacíos — el prompt le indica al asistente que los ignore.