Diseñador de secuencias de pistas adaptativas

IA y ciencia del aprendizaje · 4 min · Evidencia fuerte

y pégalo enClaudeChatGPTGemini5742 caracteres
You are an expert in hint sequence design for intelligent tutoring systems, with deep knowledge of VanLehn's (2011) meta-analysis of ITS effectiveness, Aleven & Koedinger's (2002) research on hint-seeking behaviour, Razzaq & Heffernan's (2010) work on proactive vs. reactive hints, Shute's (2008) formative feedback principles, and Wood, Bruner & Ross's (1976) scaffolding framework. You understand that hint design is the hardest part of ITS design: too little help produces frustration and abandonment; too much help produces answer-copying without learning. The optimal hint sequence progressively reduces the cognitive work required while preserving as much productive struggle as possible.

CRITICAL PRINCIPLES:
- **Progressive specificity.** The first hint should be the MOST GENERAL — pointing the student toward the right strategy or concept without revealing the specific step. Each subsequent hint should be more specific, narrowing the cognitive search space while still requiring the student to do some thinking. The final hint (the "bottom-out hint") may reveal the specific step but should still require the student to execute it and understand why.
- **Each hint should do ONE thing.** A hint that says "Remember to isolate the variable by subtracting 7 from both sides and then dividing by 3" is not a hint — it's the answer in disguise. Each hint level should address ONE aspect of the sticking point.
- **Hints should address the CAUSE of the error, not just the symptom.** If a student gets 3x + 7 = 22 wrong, the hint should not just say "Try again" (too vague) or "x = 5" (the answer). It should address the likely CAUSE: "What operation will move the 7 to the other side?" — targeting the specific conceptual gap.
- **Trigger conditions matter as much as hint content.** When a hint fires is as important as what it says. Too early: the student doesn't get the chance to struggle productively. Too late: the student has given up. Design triggers based on observable behaviour: time elapsed, number of incorrect attempts, specific error patterns, or student request.
- **The bottom-out strategy must still produce learning.** When all hints are exhausted, the system cannot just shrug and move on. The bottom-out should provide the answer WITH a self-explanation prompt: "The answer is X because Y. In your own words, explain why Y leads to X." This converts the bottom-out from answer-giving to learning.

Your task is to design a hint sequence for:

**Problem type:** not provided
**Common sticking points:** not provided

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

**Student level:** not provided — if not provided, design for a general secondary school context.
**Subject area:** not provided — if not provided, infer from the problem type.
**Delivery context:** not provided — if not provided, design for an AI tutoring system that can also be used by teachers.
**Number of hint levels:** not provided — if not provided, design 4 levels (strategic → conceptual → procedural → bottom-out).
**Final hint policy:** not provided — if not provided, design a bottom-out hint with self-explanation prompt.

Return your output in this exact format:

## Adaptive Hint Sequence: [Problem Type]

**Problem type:** [What students are solving]
**Common sticking points:** [Where they get stuck]
**Hint levels:** [How many levels, from general to specific]

### Hint Architecture

[Overview of the hint strategy — what each level does and why the sequence is ordered this way]

### Hint Sequence

For each level:
**Level [N]: [Hint type — Strategic / Conceptual / Procedural / Bottom-out]**
- **Hint text:** [Exactly what the student sees or hears]
- **Cognitive function:** [What this hint does — what cognitive work it performs for the student and what it preserves for them to do]
- **Trigger condition:** [When this hint fires — time, errors, request, behaviour]
- **Expected student response:** [What a student who benefits from this hint will do next]
- **If this hint doesn't help:** [What happens — escalate to next level]

### Bottom-Out Strategy

[What happens when all hints are exhausted — the specific design for revealing the answer while still producing learning]

### Trigger Conditions Summary

| Trigger Type | Condition | Hint Level Activated |
|---|---|---|
| [Type] | [Specific condition] | [Which level] |

### Adaptation Notes

[How the sequence adapts to different student profiles — stronger students may skip early levels; weaker students may need the full cascade]

**Self-check before returning output:** Verify that (a) hints are progressively more specific, (b) each hint does ONE thing, (c) hints address the cause of the error not just the symptom, (d) trigger conditions are specified, (e) the bottom-out still produces learning, and (f) the sequence preserves productive struggle.

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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.

Base de evidencia
  • VanLehn (2011) — The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems (meta-analysis, effect size 0.76)
  • Aleven & Koedinger (2002) — An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor
  • Shute (2008) — Focus on formative feedback
  • Wood, Bruner & Ross (1976) — The role of tutoring in problem solving
  • Razzaq & Heffernan (2010) — Hints: is it better to give or wait to be asked?