Adaptive Teaching Protocol

def diagnostic_probe(self, task: str, student_model: Model) -> DiagnosticResult:
    """
    Minimal-token assessment of student capability

    Args:
        task: Problem requiring solution
        student_model: Model being assessed

    Returns:
        DiagnosticResult containing capability assessment
    """
    # Generate targeted probe
    probe = self.create_diagnostic_probe(task)

    # Collect student response with token limit
    response = student_model.generate(
        prompt=probe,
        max_tokens=50,
        temperature=0.1  # Deterministic for assessment
    )

    # Analyze response indicators
    return DiagnosticResult(
        reasoning_depth=self.measure_reasoning_depth(response),
        domain_knowledge=self.assess_domain_knowledge(response),
        error_patterns=self.identify_error_patterns(response),
        confidence_level=self.estimate_confidence(response),
        capability_score=self.compute_overall_capability(response)
    )

def adaptive_guidance(self, task: str, diagnosis: DiagnosticResult) -> str:
    """
    Generate capability-appropriate guidance

    Returns guidance calibrated to student's diagnosed capability
    """
    if diagnosis.capability_score < 0.3:
        return self.comprehensive_guidance(task, diagnosis)
    elif diagnosis.capability_score < 0.7:
        return self.targeted_guidance(task, diagnosis)
    else:
        return self.minimal_guidance(task, diagnosis)

def comprehensive_guidance(self, task: str, diagnosis: DiagnosticResult) -> str:
    """
    Full scaffolding for weak students
    """
    guidance = []

    # Problem decomposition
    guidance.append(f"Break down the problem: {self.decompose_problem(task)}")

    # Prerequisite knowledge
    if diagnosis.domain_knowledge < 0.3:
        guidance.append(f"Key concepts: {self.provide_prerequisites(task)}")

    # Step-by-step methodology
    guidance.append(f"Approach: {self.generate_methodology(task)}")

    # Common pitfalls based on error patterns
    if diagnosis.error_patterns:
        guidance.append(f"Avoid: {self.highlight_pitfalls(diagnosis.error_patterns)}")

    # Verification strategy
    guidance.append(f"Verify by: {self.create_verification_steps(task)}")

    return "\n".join(guidance)

def targeted_guidance(self, task: str, diagnosis: DiagnosticResult) -> str:
    """
    Strategic hints for moderate students
    """
    guidance = []

    # Critical insight they're missing
    key_gap = self.identify_knowledge_gap(diagnosis)
    guidance.append(f"Key insight: {self.provide_missing_piece(task, key_gap)}")

    # Correct specific misconceptions
    if diagnosis.error_patterns:
        guidance.append(f"Correction: {self.correct_misconception(diagnosis.error_patterns[0])}")

    return "\n".join(guidance)

def minimal_guidance(self, task: str, diagnosis: DiagnosticResult) -> str:
    """
    Efficiency improvements for strong students
    """
    # Only intervene for edge cases or optimizations
    edge_cases = self.identify_edge_cases(task)
    if edge_cases:
        return f"Consider: {edge_cases[0]}"

    optimization = self.suggest_optimization(task, diagnosis)
    if optimization:
        return f"Optimize: {optimization}"

    return ""  # No intervention needed

def measure_adaptation_effectiveness(results: List[TeachingResult]) -> Dict:
    """
    Quantify how well teaching adapts to student capability
    """
    metrics = {
        'correlation': pearsonr(
            [r.diagnosis.capability_score for r in results],
            [r.guidance_length for r in results]
        )[0],  # Should be negative (weaker students get more guidance)

        'efficiency': np.mean([
            r.performance_gain / r.total_tokens for r in results
        ]),  # Gain per token

        'safety': np.mean([
            1 if r.enhanced_score >= r.baseline_score else 0
            for r in results
        ])  # Non-degradation rate
    }

    return metrics

class ProbeGenerator:
    def create_diagnostic_probe(self, task: str) -> str:
        """
        Generate minimal probe to assess capability
        """
        task_type = self.classify_task(task)

        if task_type == TaskType.DEBUGGING:
            return f"What's your first step to debug: {self.extract_problem(task)}?"
        elif task_type == TaskType.REASONING:
            return f"What approach would you use for: {self.simplify_problem(task)}?"
        elif task_type == TaskType.CODING:
            return f"What's the key challenge in: {self.extract_requirement(task)}?"

        return f"How would you approach: {task[:100]}?"

def compute_capability_score(self, response: str, task: str) -> float:
    """
    Multi-factor capability assessment
    """
    factors = {
        'keyword_presence': self.check_domain_keywords(response, task),
        'reasoning_structure': self.analyze_logical_flow(response),
        'specificity': self.measure_specificity(response),
        'correctness': self.verify_approach_validity(response, task)
    }

    weights = {
        'keyword_presence': 0.2,
        'reasoning_structure': 0.3,
        'specificity': 0.2,
        'correctness': 0.3
    }

    return sum(factors[k] * weights[k] for k in factors)

async def real_time_teaching(self, stream: AsyncIterator[str]) -> AsyncIterator[str]:
    """
    Adapt guidance based on streaming student output
    """
    buffer = []
    async for token in stream:
        buffer.append(token)

        # Periodic capability reassessment
        if len(buffer) % 100 == 0:
            current_capability = self.assess_partial_response(''.join(buffer))
            if current_capability < self.threshold:
                # Inject corrective guidance
                yield self.generate_correction(''.join(buffer[-100:]))

        yield token

def batch_adaptive_teaching(self, tasks: List[str], student: Model) -> List[Result]:
    """
    Efficient batch processing with shared diagnostics
    """
    # Group similar tasks
    task_clusters = self.cluster_tasks(tasks)

    results = []
    for cluster in task_clusters:
        # Single diagnostic for similar tasks
        representative_diagnosis = self.diagnostic_probe(cluster[0], student)

        # Apply shared guidance strategy
        for task in cluster:
            guidance = self.adapt_guidance_to_task(task, representative_diagnosis)
            results.append(self.execute_with_guidance(task, guidance, student))

    return results