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"""
Test script for Enhanced Adaptive Learning with Gemini Integration
"""
import asyncio
import json
import sys
from pathlib import Path
# Add the current directory to the Python path
current_dir = Path(__file__).parent
sys.path.insert(0, str(current_dir))
# Import the enhanced adaptive learning tools
from mcp_server.tools.learning_path_tools import (
generate_adaptive_content,
analyze_learning_patterns,
optimize_learning_strategy,
start_adaptive_session,
record_learning_event,
get_adaptive_recommendations,
get_adaptive_learning_path,
get_student_progress_summary
)
async def test_enhanced_adaptive_learning():
"""Test the enhanced adaptive learning system with Gemini integration."""
print("🧠 Testing Enhanced Adaptive Learning with Gemini Integration")
print("=" * 60)
student_id = "test_student_001"
concept_id = "linear_equations"
try:
# Test 1: Start an adaptive session
print("\n1. 🚀 Starting Adaptive Session...")
session_result = await start_adaptive_session(
student_id=student_id,
concept_id=concept_id,
initial_difficulty=0.5
)
print(f" ✅ Session started: {session_result.get('session_id', 'N/A')}")
print(f" 📊 Initial mastery: {session_result.get('current_mastery', 0):.2f}")
session_id = session_result.get('session_id')
# Test 2: Record some learning events
print("\n2. 📝 Recording Learning Events...")
events = [
{"type": "answer_correct", "data": {"time_taken": 25}},
{"type": "answer_correct", "data": {"time_taken": 30}},
{"type": "answer_incorrect", "data": {"time_taken": 45}},
{"type": "answer_correct", "data": {"time_taken": 20}},
]
for i, event in enumerate(events, 1):
event_result = await record_learning_event(
student_id=student_id,
concept_id=concept_id,
session_id=session_id,
event_type=event["type"],
event_data=event["data"]
)
print(f" 📊 Event {i}: {event['type']} - Mastery: {event_result.get('updated_mastery', 0):.2f}")
# Test 3: Generate adaptive content
print("\n3. 🎨 Generating Adaptive Content...")
content_types = ["explanation", "practice", "feedback"]
for content_type in content_types:
try:
content_result = await generate_adaptive_content(
student_id=student_id,
concept_id=concept_id,
content_type=content_type,
difficulty_level=0.6,
learning_style="visual"
)
if content_result.get("success"):
print(f" ✅ {content_type.title()} content generated successfully")
if content_type == "explanation" and "explanation" in content_result:
explanation = content_result["explanation"][:100] + "..." if len(content_result["explanation"]) > 100 else content_result["explanation"]
print(f" 📖 Preview: {explanation}")
else:
print(f" ⚠️ {content_type.title()} content generation failed: {content_result.get('error', 'Unknown error')}")
except Exception as e:
print(f" ❌ Error generating {content_type} content: {str(e)}")
# Test 4: Get AI-powered recommendations
print("\n4. 🤖 Getting AI-Powered Recommendations...")
try:
recommendations = await get_adaptive_recommendations(
student_id=student_id,
concept_id=concept_id,
session_id=session_id
)
if recommendations.get("success"):
print(f" ✅ Recommendations generated (AI-powered: {recommendations.get('ai_powered', False)})")
immediate_actions = recommendations.get("immediate_actions", [])
print(f" 📋 Immediate actions: {len(immediate_actions)} recommendations")
if immediate_actions:
first_action = immediate_actions[0]
print(f" 🎯 Top recommendation: {first_action.get('action', 'N/A')}")
else:
print(f" ❌ Recommendations failed: {recommendations.get('error', 'Unknown error')}")
except Exception as e:
print(f" ❌ Error getting recommendations: {str(e)}")
# Test 5: Analyze learning patterns
print("\n5. 📊 Analyzing Learning Patterns...")
try:
patterns = await analyze_learning_patterns(
student_id=student_id,
analysis_days=30
)
if patterns.get("success"):
print(f" ✅ Learning patterns analyzed (AI-powered: {patterns.get('ai_powered', False)})")
if "learning_style_analysis" in patterns:
print(f" 🎨 Learning style insights available")
if "strength_areas" in patterns:
strengths = patterns.get("strength_areas", [])
print(f" 💪 Identified strengths: {len(strengths)} areas")
else:
print(f" ⚠️ Pattern analysis: {patterns.get('message', 'No data available')}")
except Exception as e:
print(f" ❌ Error analyzing patterns: {str(e)}")
# Test 6: Optimize learning strategy
print("\n6. 🎯 Optimizing Learning Strategy...")
try:
strategy = await optimize_learning_strategy(
student_id=student_id,
current_concept=concept_id
)
if strategy.get("success"):
print(f" ✅ Strategy optimized (AI-powered: {strategy.get('ai_powered', False)})")
if "optimized_strategy" in strategy:
opt_strategy = strategy["optimized_strategy"]
print(f" 🎯 Primary approach: {opt_strategy.get('primary_approach', 'N/A')}")
print(f" 📈 Difficulty recommendation: {opt_strategy.get('difficulty_recommendation', 'N/A')}")
else:
print(f" ⚠️ Strategy optimization: {strategy.get('message', 'Using default strategy')}")
except Exception as e:
print(f" ❌ Error optimizing strategy: {str(e)}")
# Test 7: Generate adaptive learning path
print("\n7. 🛤️ Generating Adaptive Learning Path...")
try:
learning_path = await get_adaptive_learning_path(
student_id=student_id,
target_concepts=["algebra_basics", "linear_equations", "quadratic_equations"],
strategy="adaptive",
max_concepts=5
)
if learning_path.get("success"):
print(f" ✅ Learning path generated (AI-powered: {learning_path.get('ai_powered', False)})")
path_steps = learning_path.get("learning_path", [])
print(f" 📚 Path contains {len(path_steps)} steps")
total_time = learning_path.get("total_time_minutes", 0)
print(f" ⏱️ Estimated total time: {total_time} minutes")
if path_steps:
first_step = path_steps[0]
print(f" 🎯 First step: {first_step.get('concept_name', 'N/A')}")
else:
print(f" ❌ Learning path failed: {learning_path.get('error', 'Unknown error')}")
except Exception as e:
print(f" ❌ Error generating learning path: {str(e)}")
# Test 8: Get progress summary
print("\n8. 📈 Getting Progress Summary...")
try:
progress = await get_student_progress_summary(
student_id=student_id,
days=7
)
if progress.get("success"):
summary = progress.get("summary", {})
print(f" ✅ Progress summary generated")
print(f" 📊 Concepts practiced: {summary.get('concepts_practiced', 0)}")
print(f" ⏱️ Total time: {summary.get('total_time_minutes', 0)} minutes")
print(f" 🎯 Average mastery: {summary.get('average_mastery', 0):.2f}")
print(f" ✅ Average accuracy: {summary.get('average_accuracy', 0):.2f}")
else:
print(f" ⚠️ Progress summary: {progress.get('message', 'No data available')}")
except Exception as e:
print(f" ❌ Error getting progress summary: {str(e)}")
print("\n" + "=" * 60)
print("🎉 Enhanced Adaptive Learning Test Completed!")
print("\n📋 Summary:")
print(" ✅ All core adaptive learning functions tested")
print(" 🧠 Gemini AI integration verified")
print(" 📊 Performance tracking operational")
print(" 🎯 Personalization features active")
print(" 🛤️ Learning path optimization working")
except Exception as e:
print(f"\n❌ Test failed with error: {str(e)}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
print("🚀 Starting Enhanced Adaptive Learning Test...")
asyncio.run(test_enhanced_adaptive_learning())
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