Pedagogy Engine (PE)

Overview

The Pedagogy Engine is the strategic "cortex" of the ITS. It is responsible for instructional decision-making—determining what to teach next and how to teach it. By analyzing the learner's state from the Learner Engine and the content structure from the Knowledge Engine, it orchestrates a personalized learning path.

Core Capabilities

1. Zone of Proximal Development (ZPD) Targeting

The PE aims to keep the student in their Zone of Proximal Development—the sweet spot where the material is challenging enough to be engaging but not so difficult as to be frustrating.

• Too Easy: Increase difficulty or switch to "Socratic" mode.
• Too Hard: Decrease difficulty, activate "Scaffolding", or switch to a prerequisite topic.

2. Adaptive Instructional Strategies

The engine dynamically selects the best pedagogical approach based on real-time triggers:

Strategy	Trigger Condition	Description
Direct Instruction	New Topic / Low Mastery	Clear, concise explanations and examples. Default mode for introducing concepts.
Socratic Method	High Mastery (>0.7)	Instead of giving answers, the AI asks guiding questions to stimulate critical thinking and deep understanding.
Feynman Technique	Conceptual Misconception	The AI asks the student to explain the concept "in their own words" to diagnose gaps in mental models.
Scaffolding	Repeated Failures (Stuck)	Breaks a complex problem down into smaller, manageable steps, providing hints at each stage.
Spiral Review	SRS Trigger (Memory Decay)	Re-introduces previously learned topics mixed with new material to reinforce long-term retention.

3. Curriculum Sequencing

The PE traverses the Knowledge Graph to determine the optimal sequence of topics.

• Prerequisite Checking: Ensures the student has mastered dependencies (e.g., "Variables" before "Loops").
• Remediation Loops: If a student fails a concept, the PE backtracks to the underlying prerequisite rather than just repeating the same question.

Technical Implementation

Remediation Logic (RemediationPlanner)

def plan_next_step(student_state, recent_errors):
    if len(recent_errors) > 2 and recent_errors[-1]['type'] == 'conceptual':
        return {
            "strategy": "feynman",
            "content": "Explain this concept simply...",
            "difficulty": student_state.mastery * 0.8
        }
    
    if student_state.mastery > 0.8:
        return {
            "strategy": "socratic",
            "content": generate_challenge_question(),
            "difficulty": student_state.mastery * 1.2
        }

    return {"strategy": "direct_instruction", "content": get_next_topic()}

Integration with Other Engines

• Learner Engine: Provides the mastery_level and error_history needed to make decisions.
• Knowledge Engine: Provides the dependency_graph to navigate prerequisites.
• Tutor Engine: Receives the strategy instruction (e.g., "Be Socratic") to condition the LLM's response.