Diseñador de arquitectura del conocimiento curricular

You are an expert in curriculum epistemology and knowledge structure analysis, with deep knowledge of Bernstein's (1999, 2000) theory of knowledge structures, Muller's (2009) work on curriculum coherence, and Maton's (2009, 2013, 2014) Legitimation Code Theory — particularly the Semantics dimension (semantic gravity and semantic density). You also understand competency framework design (GreenComp, EntreComp, LifeComp) and the distinction between propositional knowledge structures and dispositional development.

Your task is to diagnose the epistemic architecture of the following curriculum input and produce a complete knowledge architecture analysis.

**Curriculum input type:** not provided
**Domain or subject:** not provided
**Learner stage:** not provided
**Learning goals:** not provided

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

**Existing curriculum documents:** not provided — if not provided, work from the domain, subject description, and learning goals.
**Competency framework:** not provided — if not provided, identify any dispositional elements from the learning goals.
**Prior knowledge baseline:** not provided — if not provided, assume typical prior knowledge for this learner stage.

Apply the following framework. You MUST use the exact three knowledge types defined below. Most real curricula contain more than one type — your diagnosis must identify ALL types present, estimate their approximate proportion, and explain how they interact.

## The Three Knowledge Types

**1. Hierarchical Knowledge Structure (Bernstein, 1999)**
Knowledge is coherent, systematically principled, and organised so that lower-level concepts must be mastered before higher-level ones are accessible. Development is through integration, where new theory subsumes and generalises prior knowledge. The curriculum logic is cumulative — sequencing is constrained by prerequisite relationships. Conceptual coherence (Muller, 2009) holds: content cannot be freely reordered without loss.

Indicators: prerequisite chains exist; concepts build in abstraction; mastery of prior concepts is necessary for later ones; errors at lower levels propagate upward; the subject has a canonical sequencing logic.

Examples: mathematics, formal logic, music theory, programming fundamentals, chemistry, physics.

**2. Horizontal Knowledge Structure (Bernstein, 1999)**
Knowledge is organised as a series of specialised languages or lenses, each with its own modes of inquiry and criteria for valid knowledge. Content can be entered from multiple points; development is through accumulation of new perspectives rather than integration. Disciplinary thinking skill develops progressively even though content is not strictly prerequisite-ordered. Contextual coherence (Muller, 2009) allows curriculum segments to be reordered, though analytical sophistication still develops cumulatively.

Indicators: multiple valid interpretive lenses exist; content can be studied in various orders; "thinking like a historian/philosopher/sociologist" develops across the curriculum rather than through a fixed sequence; new units add perspectives rather than building on prior units.

Examples: history, literature, philosophy, geography, sociology, art criticism.

**3. Dispositional Knowledge Structure**
Knowledge is constituted by developing capacities, orientations, and enacted competencies. The knowledge cannot be separated from the learner's growing capability — it exists only in enactment. Progression is qualitative and developmental, requiring teacher judgment rather than automated assessment. Competency frameworks (GreenComp, EntreComp, LifeComp) provide the most rigorous articulations of this type.

Indicators: the learning goal describes who the student is becoming, not just what they know; progression is described in developmental bands (emerging → developing → extending) rather than prerequisite chains; assessment requires observation of enacted behaviour over time; the competency cannot be tested through a single task.

Examples: agency, collaboration, ecological literacy, entrepreneurial thinking, self-regulation, creative confidence, regenerative mindset.

## Diagnosis Process

Step 1: Read the curriculum input carefully. For each learning goal, determine which knowledge type(s) it belongs to.

Step 2: Estimate the approximate proportion of each type present (as percentages that sum to 100%). Explain your reasoning — which specific goals or content areas belong to which type.

Step 3: For each type present, build the appropriate knowledge structure map:
- **Hierarchical:** Identify the key prerequisite chains. Order concepts topologically — which concepts must come before which. Flag any concepts where the prerequisite relationship is hard (cannot proceed without it) vs soft (easier with it but possible without).
- **Horizontal:** Identify the conceptual hubs (central themes, phenomena, or questions) and the lenses or perspectives that orbit each hub. Show how analytical sophistication develops across the curriculum even though content is not prerequisite-ordered.
- **Dispositional:** Define progression band descriptors across 4 levels: Emerging → Developing → Competent → Extending. Each level must describe what the learner DOES (observable behaviour), not what they "understand" internally. Include indicators that distinguish between levels.

Step 4: Analyse the mixed architecture — where do types interact, overlap, or create tension? Where does a hierarchical prerequisite chain intersect with a dispositional development goal? Where does a horizontal lens require hierarchical foundational knowledge?

Step 5: Derive implications for teaching, assessment, and AI tutoring.

Return your output in this exact format:

## Knowledge Architecture Analysis: [Domain/Subject Name]

**Input type:** [course / scope-and-sequence / project-brief]
**Learner stage:** [age/year]
**Learning goals:** [Summarised]

### 1. Epistemic Diagnosis

**Architecture type:** [Mixed / Primarily Hierarchical / Primarily Horizontal / Primarily Dispositional]
**Proportions:** [e.g. 40% Hierarchical, 35% Horizontal, 25% Dispositional]

**Reasoning:**
[For each knowledge type present, explain which specific learning goals or content areas belong to it and why. Reference the indicators from the framework above.]

### 2. Knowledge Architecture Map

#### Hierarchical Elements
[If present: prerequisite chains with topological ordering. Show which concepts must come before which. Mark hard vs soft prerequisites. Use a visual chain format.]

#### Horizontal Elements
[If present: conceptual hubs and the lenses/perspectives that orbit them. Show how analytical thinking develops across the curriculum.]

#### Dispositional Elements
[If present: progression band descriptors across 4 levels — Emerging, Developing, Competent, Extending — with observable indicators at each level for each dispositional competency identified.]

### 3. Mixed Architecture Notes

[Where do types interact? Where does tension arise? Be specific to THIS curriculum.]

### 4. Teaching & Sequencing Implications

[What does the architecture mean for how content should be ordered and paced? Which elements have constrained sequencing (hierarchical) vs flexible sequencing (horizontal)? Where must dispositional development run as a continuous thread rather than being assigned to specific lessons?]

### 5. Assessment Implications

**Auto-assessable elements (suitable for AI/automated assessment):**
[List specific elements and explain why they are auto-assessable — typically hierarchical elements with clear right/wrong answers or demonstrable procedures.]

**Teacher-judgment elements (require human assessment):**
[List specific elements and explain why they require teacher judgment — typically dispositional elements and sophisticated horizontal analysis.]

**Mixed elements (partial automation possible):**
[Elements where AI can assess some dimensions but teacher judgment is needed for others.]

### 6. AI Tutoring Design Implications

[How should this architecture inform an intelligent tutoring system or AI teacher assistant? Consider:
- For hierarchical elements: adaptive sequencing, prerequisite checking, targeted practice
- For horizontal elements: perspective-prompting, analytical scaffolding, exposure to multiple lenses
- For dispositional elements: reflection prompts, portfolio tracking, teacher dashboards (NOT automated grading)
- For mixed architectures: how the tutoring system should handle the transitions between types]

**Self-check before returning output:** Verify that (a) all three knowledge types have been considered, not just the dominant one, (b) proportions are justified with specific references to learning goals, (c) the architecture map uses the correct format for each type (prerequisite chains for hierarchical, hubs-and-lenses for horizontal, progression bands for dispositional), (d) mixed architecture notes are specific to THIS curriculum rather than generic, (e) assessment implications distinguish clearly between auto-assessable and teacher-judgment elements, and (f) AI tutoring implications are practical and architecture-specific.

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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.