Implement AI tutoring and content generation tools for TutorX

- Added ai_tutor_tools.py for contextualized AI tutoring features, including session management, interaction logging, and personalized responses.
- Introduced content_generation_tools.py for automated content creation, including interactive exercises, scenario-based learning, and gamified content.
- Developed test_ai_integration.py to validate the functionality of AI tutoring and content generation features through comprehensive test cases.

README.md

@@ -18,6 +18,20 @@ A comprehensive Model Context Protocol (MCP) server for educational AI tutoring
 
 TutorX-MCP is an adaptive, multi-modal, and collaborative AI tutoring platform that leverages the Model Context Protocol (MCP) for tool integration and Gradio for user-friendly interfaces. It provides a range of educational features accessible via both MCP clients and a dedicated web interface.
 
+## ✨ New: Enhanced AI Integration & Capabilities
+
+**🤖 Contextualized AI Tutoring:**
+- **Session-based tutoring** with persistent context and memory
+- **Step-by-step guidance** that breaks complex concepts into manageable steps
+- **Alternative explanations** using multiple approaches (visual, analogy, real-world)
+- **Adaptive responses** that adjust to student understanding levels
+
+**🎨 Advanced Automated Content Generation:**
+- **Interactive exercises** with multiple components and adaptive features
+- **Scenario-based learning** with realistic contexts and decision points
+- **Gamified content** with game mechanics and progressive difficulty
+- **Multi-modal content** supporting different learning styles
+
 ![TutorX-MCP](https://via.placeholder.com/800x400?text=TutorX-MCP+Educational+Platform)
 
 For a comprehensive analysis of the project from architectural, development, and product perspectives, please see our [Project Analysis Document](PROJECT_ANALYSIS.md).
@@ -167,6 +181,23 @@ The server exposes the following MCP tools and resources:
 - **Learning Path Tools** (learning_path_tools.py)
   - `get_learning_path`: Generate personalized learning paths based on student level and target concepts
 
+- **AI Tutoring Tools** (ai_tutor_tools.py) ✨ **NEW**
+  - `start_tutoring_session`: Start contextualized AI tutoring sessions with memory
+  - `ai_tutor_chat`: Interactive chat with AI tutor providing personalized responses
+  - `get_step_by_step_guidance`: Break down complex concepts into manageable steps
+  - `get_alternative_explanations`: Multiple explanation approaches for different learning styles
+  - `update_student_understanding`: Track and adapt to student understanding levels
+  - `end_tutoring_session`: Generate comprehensive session summaries
+
+- **Content Generation Tools** (content_generation_tools.py) ✨ **NEW**
+  - `generate_interactive_exercise`: Create engaging interactive exercises with multiple components
+  - `generate_adaptive_content_sequence`: Build adaptive content that adjusts to student performance
+  - `generate_scenario_based_learning`: Create realistic scenario-based learning experiences
+  - `generate_multimodal_content`: Generate content for different learning modalities
+  - `generate_adaptive_assessment`: Create assessments that adapt based on student responses
+  - `generate_gamified_content`: Generate game-based learning content with mechanics
+  - `validate_generated_content`: Quality-check and validate educational content
+
 - **Memory Tools** (v0.2.0)
   - `read_memory_tool`: Retrieve stored context from the Memory Bank
   - `write_memory_tool`: Store new contextual information in the Memory Bank
@@ -201,7 +232,9 @@ tutorx-mcp/
 │   │   ├── lesson_tools.py  # Lesson generation tools
 │   │   ├── ocr_tools.py     # Document OCR tools
 │   │   ├── interaction_tools.py # Student interaction tools
-│   │   └── learning_path_tools.py # Learning path tools
+│   │   ├── learning_path_tools.py # Learning path tools
+│   │   ├── ai_tutor_tools.py # ✨ Contextualized AI tutoring
+│   │   └── content_generation_tools.py # ✨ Advanced content generation
 │   └── prompts/             # LLM prompt templates
 ├── tests/                   # Test suite
 │   ├── test_mcp_server.py   # MCP server tests
@@ -275,6 +308,7 @@ python run_tests.py
 ## Documentation
 
 - [Project Analysis](PROJECT_ANALYSIS.md): Comprehensive analysis of architecture, implementation, and product features
+- [AI Integration Features](docs/AI_INTEGRATION_FEATURES.md): ✨ **NEW** - Detailed guide to contextualized AI tutoring and content generation
 - [MCP Protocol](docs/mcp.md): Details about the Model Context Protocol
 - [Product Requirements](docs/prd.md): Original requirements document
 - [SDK Documentation](docs/sdk.md): Client SDK usage

app.py

@@ -493,6 +493,64 @@ def sync_get_progress_summary(student_id, days=7):
     except Exception as e:
         return {"error": str(e)}
 
+# AI Tutoring synchronous wrappers
+def sync_start_tutoring_session(student_id, subject, learning_objectives):
+    """Synchronous wrapper for start_tutoring_session_async"""
+    try:
+        return asyncio.run(start_tutoring_session_async(student_id, subject, learning_objectives))
+    except Exception as e:
+        return {"error": str(e)}
+
+def sync_ai_tutor_chat(session_id, student_query, request_type):
+    """Synchronous wrapper for ai_tutor_chat_async"""
+    try:
+        return asyncio.run(ai_tutor_chat_async(session_id, student_query, request_type))
+    except Exception as e:
+        return {"error": str(e)}
+
+def sync_get_step_by_step_guidance(session_id, concept, current_step):
+    """Synchronous wrapper for get_step_by_step_guidance_async"""
+    try:
+        return asyncio.run(get_step_by_step_guidance_async(session_id, concept, current_step))
+    except Exception as e:
+        return {"error": str(e)}
+
+def sync_get_alternative_explanations(session_id, concept, explanation_types):
+    """Synchronous wrapper for get_alternative_explanations_async"""
+    try:
+        return asyncio.run(get_alternative_explanations_async(session_id, concept, explanation_types))
+    except Exception as e:
+        return {"error": str(e)}
+
+def sync_end_tutoring_session(session_id, session_summary):
+    """Synchronous wrapper for end_tutoring_session_async"""
+    try:
+        return asyncio.run(end_tutoring_session_async(session_id, session_summary))
+    except Exception as e:
+        return {"error": str(e)}
+
+# Content Generation synchronous wrappers
+def sync_generate_interactive_exercise(concept, exercise_type, difficulty_level, student_level):
+    """Synchronous wrapper for generate_interactive_exercise_async"""
+    try:
+        return asyncio.run(generate_interactive_exercise_async(concept, exercise_type, difficulty_level, student_level))
+    except Exception as e:
+        return {"error": str(e)}
+
+def sync_generate_scenario_based_learning(concept, scenario_type, complexity_level):
+    """Synchronous wrapper for generate_scenario_based_learning_async"""
+    try:
+        return asyncio.run(generate_scenario_based_learning_async(concept, scenario_type, complexity_level))
+    except Exception as e:
+        return {"error": str(e)}
+
+def sync_generate_gamified_content(concept, game_type, target_age_group):
+    """Synchronous wrapper for generate_gamified_content_async"""
+    try:
+        return asyncio.run(generate_gamified_content_async(concept, game_type, target_age_group))
+    except Exception as e:
+        return {"error": str(e)}
+
 # Define async functions outside the interface
 async def on_generate_quiz(concept, difficulty):
     try:
@@ -728,6 +786,120 @@ async def document_ocr_async(file):
     except Exception as e:
         return {"error": f"Error processing document: {str(e)}", "success": False}
 
+# AI Tutoring async functions
+async def start_tutoring_session_async(student_id, subject, learning_objectives):
+    try:
+        async with sse_client(SERVER_URL) as (sse, write):
+            async with ClientSession(sse, write) as session:
+                await session.initialize()
+                response = await session.call_tool("start_tutoring_session", {
+                    "student_id": student_id,
+                    "subject": subject,
+                    "learning_objectives": learning_objectives
+                })
+                return await extract_response_content(response)
+    except Exception as e:
+        return {"error": str(e)}
+
+async def ai_tutor_chat_async(session_id, student_query, request_type):
+    try:
+        async with sse_client(SERVER_URL) as (sse, write):
+            async with ClientSession(sse, write) as session:
+                await session.initialize()
+                response = await session.call_tool("ai_tutor_chat", {
+                    "session_id": session_id,
+                    "student_query": student_query,
+                    "request_type": request_type
+                })
+                return await extract_response_content(response)
+    except Exception as e:
+        return {"error": str(e)}
+
+async def get_step_by_step_guidance_async(session_id, concept, current_step):
+    try:
+        async with sse_client(SERVER_URL) as (sse, write):
+            async with ClientSession(sse, write) as session:
+                await session.initialize()
+                response = await session.call_tool("get_step_by_step_guidance", {
+                    "session_id": session_id,
+                    "concept": concept,
+                    "current_step": current_step
+                })
+                return await extract_response_content(response)
+    except Exception as e:
+        return {"error": str(e)}
+
+async def get_alternative_explanations_async(session_id, concept, explanation_types):
+    try:
+        async with sse_client(SERVER_URL) as (sse, write):
+            async with ClientSession(sse, write) as session:
+                await session.initialize()
+                response = await session.call_tool("get_alternative_explanations", {
+                    "session_id": session_id,
+                    "concept": concept,
+                    "explanation_types": explanation_types
+                })
+                return await extract_response_content(response)
+    except Exception as e:
+        return {"error": str(e)}
+
+async def end_tutoring_session_async(session_id, session_summary):
+    try:
+        async with sse_client(SERVER_URL) as (sse, write):
+            async with ClientSession(sse, write) as session:
+                await session.initialize()
+                response = await session.call_tool("end_tutoring_session", {
+                    "session_id": session_id,
+                    "session_summary": session_summary
+                })
+                return await extract_response_content(response)
+    except Exception as e:
+        return {"error": str(e)}
+
+# Content Generation async functions
+async def generate_interactive_exercise_async(concept, exercise_type, difficulty_level, student_level):
+    try:
+        async with sse_client(SERVER_URL) as (sse, write):
+            async with ClientSession(sse, write) as session:
+                await session.initialize()
+                response = await session.call_tool("generate_interactive_exercise", {
+                    "concept": concept,
+                    "exercise_type": exercise_type,
+                    "difficulty_level": difficulty_level,
+                    "student_level": student_level
+                })
+                return await extract_response_content(response)
+    except Exception as e:
+        return {"error": str(e)}
+
+async def generate_scenario_based_learning_async(concept, scenario_type, complexity_level):
+    try:
+        async with sse_client(SERVER_URL) as (sse, write):
+            async with ClientSession(sse, write) as session:
+                await session.initialize()
+                response = await session.call_tool("generate_scenario_based_learning", {
+                    "concept": concept,
+                    "scenario_type": scenario_type,
+                    "complexity_level": complexity_level
+                })
+                return await extract_response_content(response)
+    except Exception as e:
+        return {"error": str(e)}
+
+async def generate_gamified_content_async(concept, game_type, target_age_group):
+    try:
+        async with sse_client(SERVER_URL) as (sse, write):
+            async with ClientSession(sse, write) as session:
+                await session.initialize()
+                response = await session.call_tool("generate_gamified_content", {
+                    "concept": concept,
+                    "game_type": game_type,
+                    "target_age_group": target_age_group
+                })
+                return await extract_response_content(response)
+    except Exception as e:
+        return {"error": str(e)}
+
 # Create Gradio interface
 def create_gradio_interface():
     # Set a default student ID for the demo
@@ -1093,9 +1265,225 @@ def create_gradio_interface():
                     inputs=[doc_input],
                     outputs=[doc_output]
                 )
-            
-            # Tab 4: Adaptive Learning
-            with gr.Tab("4 🧠 Adaptive Learning", elem_id="adaptive_learning_tab"):
+
+            # Tab 4: AI Tutoring
+            with gr.Tab("4 🤖 AI Tutoring", elem_id="ai_tutoring_tab"):
+                gr.Markdown("## Contextualized AI Tutoring")
+                gr.Markdown("Experience personalized AI tutoring with step-by-step guidance and alternative explanations.")
+
+                with gr.Accordion("ℹ️ How AI Tutoring Works", open=False):
+                    gr.Markdown("""
+                    ### 🎯 Contextualized Learning
+                    - **Session Memory**: AI remembers your conversation and adapts responses
+                    - **Step-by-Step Guidance**: Break down complex concepts into manageable steps
+                    - **Alternative Explanations**: Multiple ways to understand the same concept
+                    - **Personalized Feedback**: Responses tailored to your understanding level
+
+                    ### 🚀 Getting Started
+                    1. Start a tutoring session with your preferred subject
+                    2. Ask questions or request explanations
+                    3. Get step-by-step guidance for complex topics
+                    4. Request alternative explanations if needed
+                    5. End session to get a comprehensive summary
+                    """)
+
+                # Tutoring Session Management
+                with gr.Accordion("📚 Start Tutoring Session", open=True):
+                    with gr.Row():
+                        with gr.Column():
+                            tutor_student_id = gr.Textbox(label="Student ID", value=student_id)
+                            tutor_subject = gr.Textbox(label="Subject", value="Mathematics", placeholder="e.g., Mathematics, Physics, Chemistry")
+                            tutor_objectives = gr.Textbox(
+                                label="Learning Objectives (optional)",
+                                placeholder="e.g., Understand quadratic equations, Learn calculus basics",
+                                lines=2
+                            )
+                            start_tutor_btn = gr.Button("Start Tutoring Session", variant="primary")
+
+                        with gr.Column():
+                            tutor_session_output = gr.JSON(label="Session Information")
+
+                # AI Chat Interface
+                with gr.Accordion("💬 Chat with AI Tutor", open=True):
+                    with gr.Row():
+                        with gr.Column():
+                            chat_session_id = gr.Textbox(label="Session ID", placeholder="Enter session ID from above")
+                            chat_query = gr.Textbox(
+                                label="Ask Your Question",
+                                placeholder="e.g., How do I solve quadratic equations?",
+                                lines=3
+                            )
+                            chat_request_type = gr.Dropdown(
+                                choices=["explanation", "step_by_step", "alternative", "practice", "clarification"],
+                                value="explanation",
+                                label="Request Type"
+                            )
+                            chat_btn = gr.Button("Ask AI Tutor", variant="primary")
+
+                        with gr.Column():
+                            chat_response = gr.JSON(label="AI Tutor Response")
+
+                # Step-by-Step Guidance
+                with gr.Accordion("📋 Step-by-Step Guidance", open=True):
+                    with gr.Row():
+                        with gr.Column():
+                            step_session_id = gr.Textbox(label="Session ID")
+                            step_concept = gr.Textbox(label="Concept", placeholder="e.g., Solving quadratic equations")
+                            step_current = gr.Number(label="Current Step", value=1, minimum=1)
+                            get_steps_btn = gr.Button("Get Step-by-Step Guidance")
+
+                        with gr.Column():
+                            steps_output = gr.JSON(label="Step-by-Step Guidance")
+
+                # Alternative Explanations
+                with gr.Accordion("🔄 Alternative Explanations", open=True):
+                    with gr.Row():
+                        with gr.Column():
+                            alt_session_id = gr.Textbox(label="Session ID")
+                            alt_concept = gr.Textbox(label="Concept", placeholder="e.g., Photosynthesis")
+                            alt_types = gr.CheckboxGroup(
+                                choices=["visual", "analogy", "real_world", "simplified", "technical"],
+                                value=["visual", "analogy", "real_world"],
+                                label="Explanation Types"
+                            )
+                            get_alt_btn = gr.Button("Get Alternative Explanations")
+
+                        with gr.Column():
+                            alt_output = gr.JSON(label="Alternative Explanations")
+
+                # Session Management
+                with gr.Accordion("🔚 End Session & Summary", open=True):
+                    with gr.Row():
+                        with gr.Column():
+                            end_session_id = gr.Textbox(label="Session ID")
+                            session_summary = gr.Textbox(
+                                label="Session Summary (optional)",
+                                placeholder="What did you learn? Any feedback?",
+                                lines=3
+                            )
+                            end_session_btn = gr.Button("End Session & Get Summary", variant="secondary")
+
+                        with gr.Column():
+                            session_end_output = gr.JSON(label="Session Summary")
+
+                # Connect all AI tutoring buttons
+                start_tutor_btn.click(
+                    fn=lambda sid, subj, obj: sync_start_tutoring_session(sid, subj, obj.split(',') if obj else []),
+                    inputs=[tutor_student_id, tutor_subject, tutor_objectives],
+                    outputs=[tutor_session_output]
+                )
+
+                chat_btn.click(
+                    fn=sync_ai_tutor_chat,
+                    inputs=[chat_session_id, chat_query, chat_request_type],
+                    outputs=[chat_response]
+                )
+
+                get_steps_btn.click(
+                    fn=sync_get_step_by_step_guidance,
+                    inputs=[step_session_id, step_concept, step_current],
+                    outputs=[steps_output]
+                )
+
+                get_alt_btn.click(
+                    fn=sync_get_alternative_explanations,
+                    inputs=[alt_session_id, alt_concept, alt_types],
+                    outputs=[alt_output]
+                )
+
+                end_session_btn.click(
+                    fn=sync_end_tutoring_session,
+                    inputs=[end_session_id, session_summary],
+                    outputs=[session_end_output]
+                )
+
+            # Tab 5: Content Generation
+            with gr.Tab("5 🎨 Content Generation", elem_id="content_generation_tab"):
+                gr.Markdown("## Advanced Content Generation")
+                gr.Markdown("Generate interactive exercises, scenarios, and gamified content automatically.")
+
+                # Interactive Exercise Generation
+                with gr.Accordion("🎯 Interactive Exercise Generation", open=True):
+                    with gr.Row():
+                        with gr.Column():
+                            ex_concept = gr.Textbox(label="Concept", placeholder="e.g., Photosynthesis, Linear Algebra")
+                            ex_type = gr.Dropdown(
+                                choices=["problem_solving", "simulation", "case_study", "lab", "project"],
+                                value="problem_solving",
+                                label="Exercise Type"
+                            )
+                            ex_difficulty = gr.Slider(minimum=0.1, maximum=1.0, value=0.5, step=0.1, label="Difficulty Level")
+                            ex_level = gr.Dropdown(
+                                choices=["beginner", "intermediate", "advanced"],
+                                value="intermediate",
+                                label="Student Level"
+                            )
+                            gen_exercise_btn = gr.Button("Generate Interactive Exercise", variant="primary")
+
+                        with gr.Column():
+                            exercise_output = gr.JSON(label="Generated Exercise")
+
+                # Scenario-Based Learning
+                with gr.Accordion("🎭 Scenario-Based Learning", open=True):
+                    with gr.Row():
+                        with gr.Column():
+                            scenario_concept = gr.Textbox(label="Concept", placeholder="e.g., Climate Change, Economics")
+                            scenario_type = gr.Dropdown(
+                                choices=["real_world", "historical", "futuristic", "problem_solving"],
+                                value="real_world",
+                                label="Scenario Type"
+                            )
+                            scenario_complexity = gr.Dropdown(
+                                choices=["simple", "moderate", "complex"],
+                                value="moderate",
+                                label="Complexity Level"
+                            )
+                            gen_scenario_btn = gr.Button("Generate Scenario", variant="primary")
+
+                        with gr.Column():
+                            scenario_output = gr.JSON(label="Generated Scenario")
+
+                # Gamified Content
+                with gr.Accordion("🎮 Gamified Content Generation", open=True):
+                    with gr.Row():
+                        with gr.Column():
+                            game_concept = gr.Textbox(label="Concept", placeholder="e.g., Fractions, Chemical Reactions")
+                            game_type = gr.Dropdown(
+                                choices=["quest", "puzzle", "simulation", "competition", "story"],
+                                value="quest",
+                                label="Game Type"
+                            )
+                            game_age = gr.Dropdown(
+                                choices=["child", "teen", "adult"],
+                                value="teen",
+                                label="Target Age Group"
+                            )
+                            gen_game_btn = gr.Button("Generate Gamified Content", variant="primary")
+
+                        with gr.Column():
+                            game_output = gr.JSON(label="Generated Game Content")
+
+                # Connect content generation buttons
+                gen_exercise_btn.click(
+                    fn=sync_generate_interactive_exercise,
+                    inputs=[ex_concept, ex_type, ex_difficulty, ex_level],
+                    outputs=[exercise_output]
+                )
+
+                gen_scenario_btn.click(
+                    fn=sync_generate_scenario_based_learning,
+                    inputs=[scenario_concept, scenario_type, scenario_complexity],
+                    outputs=[scenario_output]
+                )
+
+                gen_game_btn.click(
+                    fn=sync_generate_gamified_content,
+                    inputs=[game_concept, game_type, game_age],
+                    outputs=[game_output]
+                )
+
+            # Tab 6: Adaptive Learning
+            with gr.Tab("6 🧠 Adaptive Learning", elem_id="adaptive_learning_tab"):
                 gr.Markdown("## Adaptive Learning System")
                 gr.Markdown("Experience personalized learning with real-time adaptation based on your performance.")
 
@@ -1264,8 +1652,8 @@ def create_gradio_interface():
                     - **Engagement Metrics**: Measures learning engagement
                     """)
 
-            # Tab 5: Data Analytics
-            with gr.Tab("5 Data Analytics", elem_id="data_analytics_tab"):
+            # Tab 7: Data Analytics
+            with gr.Tab("7 Data Analytics", elem_id="data_analytics_tab"):
                 gr.Markdown("## Plagiarism Detection")
 
                 with gr.Row():

docs/AI_INTEGRATION_FEATURES.md

@@ -0,0 +1,237 @@
+# AI Integration and Capabilities - TutorX-MCP
+
+## Overview
+
+This document describes the enhanced AI integration and capabilities implemented in TutorX-MCP, focusing on contextualized AI tutoring and advanced automated content generation.
+
+## 🤖 Contextualized AI Tutoring
+
+### Features
+
+#### 1. **Session-Based Tutoring**
+- **Persistent Context**: AI maintains conversation history and adapts responses
+- **Student Profiling**: Tracks understanding levels and learning preferences
+- **Subject Specialization**: Tailored tutoring for specific subjects
+
+#### 2. **Step-by-Step Guidance**
+- **Progressive Learning**: Breaks complex concepts into manageable steps
+- **Adaptive Pacing**: Adjusts based on student understanding
+- **Checkpoint Validation**: Verifies understanding at key points
+
+#### 3. **Alternative Explanations**
+- **Multiple Approaches**: Visual, analogy-based, real-world applications
+- **Learning Style Adaptation**: Matches student's preferred learning style
+- **Difficulty Scaling**: Provides simplified or technical explanations as needed
+
+### API Endpoints
+
+#### Start Tutoring Session
+```http
+POST /api/start-tutoring-session
+Content-Type: application/json
+
+{
+  "student_id": "student_001",
+  "subject": "Mathematics",
+  "learning_objectives": ["Understand quadratic equations", "Learn factoring"]
+}
+```
+
+#### Chat with AI Tutor
+```http
+POST /api/ai-tutor-chat
+Content-Type: application/json
+
+{
+  "session_id": "session_uuid",
+  "student_query": "How do I solve quadratic equations?",
+  "request_type": "step_by_step"
+}
+```
+
+#### Get Step-by-Step Guidance
+```http
+POST /api/step-by-step-guidance
+Content-Type: application/json
+
+{
+  "session_id": "session_uuid",
+  "concept": "Solving quadratic equations",
+  "current_step": 1
+}
+```
+
+#### Get Alternative Explanations
+```http
+POST /api/alternative-explanations
+Content-Type: application/json
+
+{
+  "session_id": "session_uuid",
+  "concept": "Quadratic formula",
+  "explanation_types": ["visual", "analogy", "real_world"]
+}
+```
+
+## 🎨 Advanced Automated Content Generation
+
+### Features
+
+#### 1. **Interactive Exercise Generation**
+- **Multiple Exercise Types**: Problem-solving, simulations, case studies, labs, projects
+- **Adaptive Difficulty**: Automatically calibrated based on student level
+- **Assessment Integration**: Built-in evaluation criteria and rubrics
+
+#### 2. **Scenario-Based Learning**
+- **Realistic Contexts**: Real-world, historical, and futuristic scenarios
+- **Decision Points**: Interactive choices with consequences
+- **Multi-Path Solutions**: Multiple valid approaches to problems
+
+#### 3. **Gamified Content**
+- **Game Mechanics**: Quests, puzzles, simulations, competitions
+- **Progressive Difficulty**: Leveled content with achievements
+- **Social Features**: Collaborative and competitive elements
+
+#### 4. **Multi-Modal Content**
+- **Learning Style Support**: Visual, auditory, kinesthetic, reading/writing
+- **Accessibility Features**: Content adapted for different abilities
+- **Technology Integration**: Enhanced with digital tools
+
+### API Endpoints
+
+#### Generate Interactive Exercise
+```http
+POST /api/generate-interactive-exercise
+Content-Type: application/json
+
+{
+  "concept": "Photosynthesis",
+  "exercise_type": "simulation",
+  "difficulty_level": 0.6,
+  "student_level": "intermediate"
+}
+```
+
+#### Generate Scenario-Based Learning
+```http
+POST /api/generate-scenario-based-learning
+Content-Type: application/json
+
+{
+  "concept": "Climate Change",
+  "scenario_type": "real_world",
+  "complexity_level": "moderate"
+}
+```
+
+#### Generate Gamified Content
+```http
+POST /api/generate-gamified-content
+Content-Type: application/json
+
+{
+  "concept": "Fractions",
+  "game_type": "quest",
+  "target_age_group": "teen"
+}
+```
+
+## 🚀 Usage Examples
+
+### Example 1: Complete Tutoring Session
+
+```python
+# Start session
+session = await start_tutoring_session(
+    student_id="student_001",
+    subject="Physics",
+    learning_objectives=["Understand Newton's laws"]
+)
+
+# Chat with tutor
+response = await ai_tutor_chat(
+    session_id=session["session_id"],
+    student_query="What is Newton's first law?",
+    request_type="explanation"
+)
+
+# Get step-by-step guidance
+steps = await get_step_by_step_guidance(
+    session_id=session["session_id"],
+    concept="Newton's first law",
+    current_step=1
+)
+
+# End session
+summary = await end_tutoring_session(
+    session_id=session["session_id"],
+    session_summary="Learned about Newton's laws"
+)
+```
+
+### Example 2: Content Generation Workflow
+
+```python
+# Generate interactive exercise
+exercise = await generate_interactive_exercise(
+    concept="Chemical Reactions",
+    exercise_type="lab",
+    difficulty_level=0.7,
+    student_level="advanced"
+)
+
+# Generate scenario
+scenario = await generate_scenario_based_learning(
+    concept="Environmental Science",
+    scenario_type="real_world",
+    complexity_level="complex"
+)
+
+# Generate game
+game = await generate_gamified_content(
+    concept="Algebra",
+    game_type="puzzle",
+    target_age_group="teen"
+)
+```
+
+## 🔧 Technical Implementation
+
+### Architecture
+- **Modular Design**: Separate modules for tutoring and content generation
+- **Session Management**: In-memory session storage with context preservation
+- **AI Integration**: Powered by Google Gemini Flash models
+- **API Layer**: RESTful endpoints with comprehensive error handling
+
+### Key Components
+- `ai_tutor_tools.py`: Contextualized tutoring functionality
+- `content_generation_tools.py`: Advanced content generation
+- `TutoringSession` class: Session state management
+- Gradio interface: User-friendly web interface
+
+### Quality Assurance
+- **Content Validation**: Automated quality checking
+- **Error Handling**: Comprehensive error management
+- **Testing**: Automated test suite for all features
+
+## 📊 Benefits
+
+### For Students
+- **Personalized Learning**: Adapted to individual needs and pace
+- **Multiple Learning Paths**: Various approaches to understand concepts
+- **Engaging Content**: Interactive and gamified learning experiences
+- **Immediate Feedback**: Real-time guidance and support
+
+### For Educators
+- **Content Creation**: Automated generation of high-quality materials
+- **Assessment Tools**: Built-in evaluation and rubrics
+- **Analytics**: Detailed insights into student progress
+- **Scalability**: Support for multiple students simultaneously
+
+## 🔮 Future Enhancements
+
+- **Voice Integration**: Speech-to-text and text-to-speech capabilities
+- **Visual Content**: Automatic diagram and chart generation
+- **Collaborative Learning**: Multi-student tutoring sessions
+- **Advanced Analytics**: Predictive learning analytics
+- **Mobile Optimization**: Enhanced mobile experience

mcp_server/server.py

@@ -57,6 +57,8 @@ from mcp_server.tools import interaction_tools
 from mcp_server.tools import ocr_tools
 from mcp_server.tools import learning_path_tools
 from mcp_server.tools import concept_graph_tools
+from mcp_server.tools import ai_tutor_tools
+from mcp_server.tools import content_generation_tools
 
 
 # Mount the SSE transport for MCP at '/sse/' (with trailing slash)
@@ -242,6 +244,154 @@ async def get_quiz_session_status_endpoint(request: dict):
         raise HTTPException(status_code=400, detail="session_id is required")
     return await get_quiz_session_status_tool(session_id)
 
+# API endpoints - AI Tutoring
+from mcp_server.tools.ai_tutor_tools import (
+    start_tutoring_session,
+    ai_tutor_chat,
+    get_step_by_step_guidance,
+    get_alternative_explanations,
+    update_student_understanding,
+    get_tutoring_session_status,
+    end_tutoring_session,
+    list_active_tutoring_sessions
+)
+
+@api_app.post("/api/start-tutoring-session")
+async def start_tutoring_session_endpoint(request: dict):
+    student_id = request.get("student_id")
+    subject = request.get("subject", "general")
+    learning_objectives = request.get("learning_objectives", [])
+    if not student_id:
+        raise HTTPException(status_code=400, detail="student_id is required")
+    return await start_tutoring_session(student_id, subject, learning_objectives)
+
+@api_app.post("/api/ai-tutor-chat")
+async def ai_tutor_chat_endpoint(request: dict):
+    session_id = request.get("session_id")
+    student_query = request.get("student_query")
+    request_type = request.get("request_type", "explanation")
+    if not session_id or not student_query:
+        raise HTTPException(status_code=400, detail="session_id and student_query are required")
+    return await ai_tutor_chat(session_id, student_query, request_type)
+
+@api_app.post("/api/step-by-step-guidance")
+async def step_by_step_guidance_endpoint(request: dict):
+    session_id = request.get("session_id")
+    concept = request.get("concept")
+    current_step = request.get("current_step", 1)
+    if not session_id or not concept:
+        raise HTTPException(status_code=400, detail="session_id and concept are required")
+    return await get_step_by_step_guidance(session_id, concept, current_step)
+
+@api_app.post("/api/alternative-explanations")
+async def alternative_explanations_endpoint(request: dict):
+    session_id = request.get("session_id")
+    concept = request.get("concept")
+    explanation_types = request.get("explanation_types", [])
+    if not session_id or not concept:
+        raise HTTPException(status_code=400, detail="session_id and concept are required")
+    return await get_alternative_explanations(session_id, concept, explanation_types)
+
+@api_app.post("/api/update-student-understanding")
+async def update_student_understanding_endpoint(request: dict):
+    session_id = request.get("session_id")
+    concept = request.get("concept")
+    understanding_level = request.get("understanding_level")
+    feedback = request.get("feedback", "")
+    if not session_id or not concept or understanding_level is None:
+        raise HTTPException(status_code=400, detail="session_id, concept, and understanding_level are required")
+    return await update_student_understanding(session_id, concept, understanding_level, feedback)
+
+@api_app.get("/api/tutoring-session-status/{session_id}")
+async def tutoring_session_status_endpoint(session_id: str):
+    return await get_tutoring_session_status(session_id)
+
+@api_app.post("/api/end-tutoring-session")
+async def end_tutoring_session_endpoint(request: dict):
+    session_id = request.get("session_id")
+    session_summary = request.get("session_summary", "")
+    if not session_id:
+        raise HTTPException(status_code=400, detail="session_id is required")
+    return await end_tutoring_session(session_id, session_summary)
+
+@api_app.get("/api/active-tutoring-sessions")
+async def active_tutoring_sessions_endpoint(student_id: str = None):
+    return await list_active_tutoring_sessions(student_id)
+
+# API endpoints - Content Generation
+from mcp_server.tools.content_generation_tools import (
+    generate_interactive_exercise,
+    generate_adaptive_content_sequence,
+    generate_scenario_based_learning,
+    generate_multimodal_content,
+    generate_adaptive_assessment,
+    generate_gamified_content,
+    validate_generated_content
+)
+
+@api_app.post("/api/generate-interactive-exercise")
+async def generate_interactive_exercise_endpoint(request: dict):
+    concept = request.get("concept")
+    exercise_type = request.get("exercise_type", "problem_solving")
+    difficulty_level = request.get("difficulty_level", 0.5)
+    student_level = request.get("student_level", "intermediate")
+    if not concept:
+        raise HTTPException(status_code=400, detail="concept is required")
+    return await generate_interactive_exercise(concept, exercise_type, difficulty_level, student_level)
+
+@api_app.post("/api/generate-adaptive-content-sequence")
+async def generate_adaptive_content_sequence_endpoint(request: dict):
+    topic = request.get("topic")
+    student_profile = request.get("student_profile", {})
+    sequence_length = request.get("sequence_length", 5)
+    if not topic:
+        raise HTTPException(status_code=400, detail="topic is required")
+    return await generate_adaptive_content_sequence(topic, student_profile, sequence_length)
+
+@api_app.post("/api/generate-scenario-based-learning")
+async def generate_scenario_based_learning_endpoint(request: dict):
+    concept = request.get("concept")
+    scenario_type = request.get("scenario_type", "real_world")
+    complexity_level = request.get("complexity_level", "moderate")
+    if not concept:
+        raise HTTPException(status_code=400, detail="concept is required")
+    return await generate_scenario_based_learning(concept, scenario_type, complexity_level)
+
+@api_app.post("/api/generate-multimodal-content")
+async def generate_multimodal_content_endpoint(request: dict):
+    concept = request.get("concept")
+    modalities = request.get("modalities", ["visual", "auditory", "kinesthetic", "reading"])
+    target_audience = request.get("target_audience", "general")
+    if not concept:
+        raise HTTPException(status_code=400, detail="concept is required")
+    return await generate_multimodal_content(concept, modalities, target_audience)
+
+@api_app.post("/api/generate-adaptive-assessment")
+async def generate_adaptive_assessment_endpoint(request: dict):
+    concept = request.get("concept")
+    assessment_type = request.get("assessment_type", "formative")
+    student_data = request.get("student_data", {})
+    if not concept:
+        raise HTTPException(status_code=400, detail="concept is required")
+    return await generate_adaptive_assessment(concept, assessment_type, student_data)
+
+@api_app.post("/api/generate-gamified-content")
+async def generate_gamified_content_endpoint(request: dict):
+    concept = request.get("concept")
+    game_type = request.get("game_type", "quest")
+    target_age_group = request.get("target_age_group", "teen")
+    if not concept:
+        raise HTTPException(status_code=400, detail="concept is required")
+    return await generate_gamified_content(concept, game_type, target_age_group)
+
+@api_app.post("/api/validate-content")
+async def validate_content_endpoint(request: dict):
+    content_data = request.get("content_data")
+    validation_criteria = request.get("validation_criteria", {})
+    if not content_data:
+        raise HTTPException(status_code=400, detail="content_data is required")
+    return await validate_generated_content(content_data, validation_criteria)
+
 # Entrypoint for running with MCP SSE transport
 if __name__ == "__main__":
     mcp.run(transport="sse")

mcp_server/tools/ai_tutor_tools.py

@@ -0,0 +1,650 @@
+"""
+Contextualized AI Tutoring tools for TutorX.
+Provides step-by-step guidance, alternative explanations, and conversational AI tutoring.
+"""
+
+import json
+import uuid
+from datetime import datetime, timedelta
+from typing import Dict, Any, List, Optional
+from mcp_server.mcp_instance import mcp
+from mcp_server.model.gemini_flash import GeminiFlash
+
+MODEL = GeminiFlash()
+
+# Tutoring session storage
+tutoring_sessions = {}
+
+def extract_json_from_text(text: str):
+    """Extract JSON from text response"""
+    import re
+    if not text or not isinstance(text, str):
+        return None
+    # Remove code fences
+    text = re.sub(r'^\s*```(?:json)?\s*', '', text, flags=re.IGNORECASE)
+    text = re.sub(r'\s*```\s*$', '', text, flags=re.IGNORECASE)
+    text = text.strip()
+    # Remove trailing commas
+    text = re.sub(r',([ \t\r\n]*[}}\]])', r'\1', text)
+    return json.loads(text)
+
+class TutoringSession:
+    """Manages a tutoring session with context and memory"""
+    
+    def __init__(self, session_id: str, student_id: str, subject: str = "general"):
+        self.session_id = session_id
+        self.student_id = student_id
+        self.subject = subject
+        self.created_at = datetime.utcnow()
+        self.last_activity = datetime.utcnow()
+        self.conversation_history = []
+        self.current_topic = None
+        self.learning_objectives = []
+        self.student_understanding_level = 0.5  # 0.0 to 1.0
+        self.preferred_explanation_style = "detailed"  # detailed, simple, visual, step-by-step
+        self.difficulty_preference = 0.5  # 0.0 to 1.0
+        
+    def add_interaction(self, query: str, response: str, interaction_type: str = "question"):
+        """Add an interaction to the session history"""
+        self.conversation_history.append({
+            "timestamp": datetime.utcnow().isoformat(),
+            "query": query,
+            "response": response,
+            "type": interaction_type,
+            "understanding_level": self.student_understanding_level
+        })
+        self.last_activity = datetime.utcnow()
+        
+    def get_context_summary(self) -> str:
+        """Generate a context summary for the AI tutor"""
+        recent_interactions = self.conversation_history[-5:]  # Last 5 interactions
+        
+        context = f"""
+        Session Context:
+        - Student ID: {self.student_id}
+        - Subject: {self.subject}
+        - Current Topic: {self.current_topic or 'Not specified'}
+        - Understanding Level: {self.student_understanding_level:.2f}/1.0
+        - Preferred Style: {self.preferred_explanation_style}
+        - Session Duration: {(datetime.utcnow() - self.created_at).total_seconds() / 60:.1f} minutes
+        
+        Recent Conversation:
+        """
+        
+        for interaction in recent_interactions:
+            context += f"\nStudent: {interaction['query']}\nTutor: {interaction['response'][:100]}...\n"
+            
+        return context
+
+@mcp.tool()
+async def start_tutoring_session(student_id: str, subject: str = "general", 
+                               learning_objectives: List[str] = None) -> dict:
+    """
+    Start a new AI tutoring session with context management.
+    
+    Args:
+        student_id: Student identifier
+        subject: Subject area for tutoring
+        learning_objectives: List of learning objectives for the session
+        
+    Returns:
+        Dictionary with session information
+    """
+    try:
+        session_id = str(uuid.uuid4())
+        session = TutoringSession(session_id, student_id, subject)
+        
+        if learning_objectives:
+            session.learning_objectives = learning_objectives
+            
+        tutoring_sessions[session_id] = session
+        
+        # Generate welcome message
+        prompt = f"""
+        You are an expert AI tutor starting a new tutoring session.
+        
+        Student ID: {student_id}
+        Subject: {subject}
+        Learning Objectives: {learning_objectives or 'To be determined based on student needs'}
+        
+        Generate a welcoming introduction that:
+        1. Welcomes the student warmly
+        2. Explains your role as their AI tutor
+        3. Asks about their current understanding or what they'd like to learn
+        4. Sets expectations for the tutoring session
+        
+        Return a JSON object with:
+        - "welcome_message": friendly welcome text
+        - "suggested_topics": list of 3-5 topics they might want to explore
+        - "session_guidelines": brief explanation of how the tutoring works
+        """
+        
+        response = await MODEL.generate_text(prompt, temperature=0.7)
+        welcome_data = extract_json_from_text(response)
+        
+        return {
+            "success": True,
+            "session_id": session_id,
+            "student_id": student_id,
+            "subject": subject,
+            "created_at": session.created_at.isoformat(),
+            **welcome_data
+        }
+        
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to start tutoring session: {str(e)}"
+        }
+
+@mcp.tool()
+async def ai_tutor_chat(session_id: str, student_query: str, 
+                       request_type: str = "explanation") -> dict:
+    """
+    Process student query with contextualized AI tutoring.
+    
+    Args:
+        session_id: Active tutoring session ID
+        student_query: Student's question or request
+        request_type: Type of request ('explanation', 'step_by_step', 'alternative', 'practice', 'clarification')
+        
+    Returns:
+        Dictionary with tutor response and guidance
+    """
+    try:
+        if session_id not in tutoring_sessions:
+            return {
+                "success": False,
+                "error": "Invalid session ID. Please start a new tutoring session."
+            }
+            
+        session = tutoring_sessions[session_id]
+        context = session.get_context_summary()
+        
+        # Analyze student query to understand their needs
+        analysis_prompt = f"""
+        Analyze this student query in the context of their tutoring session:
+        
+        {context}
+        
+        Student Query: "{student_query}"
+        Request Type: {request_type}
+        
+        Analyze and return JSON with:
+        - "topic_identified": main topic/concept the student is asking about
+        - "difficulty_level": estimated difficulty (0.0 to 1.0)
+        - "understanding_gaps": potential areas where student might be confused
+        - "prerequisite_concepts": concepts student should know first
+        - "confidence_level": how confident the student seems (0.0 to 1.0)
+        """
+        
+        analysis_response = await MODEL.generate_text(analysis_prompt, temperature=0.3)
+        analysis = extract_json_from_text(analysis_response)
+        
+        # Update session context based on analysis
+        if analysis and "topic_identified" in analysis:
+            session.current_topic = analysis["topic_identified"]
+            if "difficulty_level" in analysis:
+                session.difficulty_preference = analysis["difficulty_level"]
+        
+        # Generate appropriate response based on request type
+        if request_type == "step_by_step":
+            response_prompt = f"""
+            {context}
+            
+            The student asked: "{student_query}"
+            
+            Provide a detailed step-by-step explanation that:
+            1. Breaks down the concept into manageable steps
+            2. Uses simple, clear language appropriate for their level
+            3. Includes examples for each step
+            4. Checks understanding at key points
+            5. Provides encouragement and support
+            
+            Return JSON with:
+            - "step_by_step_explanation": detailed step-by-step guide
+            - "key_steps": list of main steps with brief descriptions
+            - "examples": relevant examples for each step
+            - "check_points": questions to verify understanding
+            - "encouragement": supportive message
+            """
+        elif request_type == "alternative":
+            response_prompt = f"""
+            {context}
+            
+            The student asked: "{student_query}"
+            
+            Provide alternative explanations using different approaches:
+            1. Visual/spatial explanation
+            2. Analogy-based explanation
+            3. Real-world application
+            4. Simplified version
+            
+            Return JSON with:
+            - "visual_explanation": explanation using visual/spatial concepts
+            - "analogy_explanation": explanation using familiar analogies
+            - "real_world_application": how this applies in real life
+            - "simplified_version": very simple explanation
+            - "which_works_best": question asking which explanation resonates
+            """
+        else:  # Default explanation
+            response_prompt = f"""
+            {context}
+            
+            The student asked: "{student_query}"
+            
+            Provide a comprehensive, personalized explanation that:
+            1. Addresses their specific question directly
+            2. Builds on their current understanding level
+            3. Uses their preferred explanation style
+            4. Includes relevant examples
+            5. Suggests next steps for learning
+            
+            Return JSON with:
+            - "main_explanation": comprehensive answer to their question
+            - "key_concepts": important concepts to remember
+            - "examples": relevant examples
+            - "next_steps": suggested follow-up activities
+            - "related_topics": topics they might want to explore next
+            """
+        
+        tutor_response = await MODEL.generate_text(response_prompt, temperature=0.7)
+        response_data = extract_json_from_text(tutor_response)
+        
+        # Add interaction to session history
+        session.add_interaction(
+            student_query, 
+            response_data.get("main_explanation", str(response_data)), 
+            request_type
+        )
+        
+        return {
+            "success": True,
+            "session_id": session_id,
+            "request_type": request_type,
+            "analysis": analysis,
+            "response": response_data,
+            "session_stats": {
+                "interactions_count": len(session.conversation_history),
+                "current_topic": session.current_topic,
+                "understanding_level": session.student_understanding_level
+            }
+        }
+        
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to process tutoring request: {str(e)}"
+        }
+
+@mcp.tool()
+async def get_step_by_step_guidance(session_id: str, concept: str,
+                                  current_step: int = 1) -> dict:
+    """
+    Provide detailed step-by-step guidance for learning a specific concept.
+
+    Args:
+        session_id: Active tutoring session ID
+        concept: The concept to provide guidance for
+        current_step: Current step the student is on (1-based)
+
+    Returns:
+        Dictionary with step-by-step guidance
+    """
+    try:
+        if session_id not in tutoring_sessions:
+            return {
+                "success": False,
+                "error": "Invalid session ID. Please start a new tutoring session."
+            }
+
+        session = tutoring_sessions[session_id]
+        context = session.get_context_summary()
+
+        prompt = f"""
+        {context}
+
+        Provide comprehensive step-by-step guidance for learning: "{concept}"
+        Current step: {current_step}
+
+        Create a structured learning path that:
+        1. Breaks the concept into logical, sequential steps
+        2. Provides clear explanations for each step
+        3. Includes practice exercises for each step
+        4. Offers multiple ways to understand each step
+        5. Includes checkpoints to verify understanding
+
+        Return JSON with:
+        - "total_steps": total number of steps needed
+        - "current_step_details": detailed information about the current step
+        - "step_explanation": clear explanation of the current step
+        - "practice_exercises": 2-3 practice problems for this step
+        - "key_points": important points to remember
+        - "common_mistakes": common mistakes students make at this step
+        - "next_step_preview": brief preview of what comes next
+        - "prerequisite_check": what student should know before this step
+        - "mastery_indicators": how to know if student has mastered this step
+        """
+
+        response = await MODEL.generate_text(prompt, temperature=0.6)
+        guidance_data = extract_json_from_text(response)
+
+        return {
+            "success": True,
+            "session_id": session_id,
+            "concept": concept,
+            "current_step": current_step,
+            "guidance": guidance_data
+        }
+
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to generate step-by-step guidance: {str(e)}"
+        }
+
+@mcp.tool()
+async def get_alternative_explanations(session_id: str, concept: str,
+                                     explanation_types: List[str] = None) -> dict:
+    """
+    Generate alternative explanations for a concept using different approaches.
+
+    Args:
+        session_id: Active tutoring session ID
+        concept: The concept to explain
+        explanation_types: Types of explanations to generate
+
+    Returns:
+        Dictionary with alternative explanations
+    """
+    try:
+        if session_id not in tutoring_sessions:
+            return {
+                "success": False,
+                "error": "Invalid session ID. Please start a new tutoring session."
+            }
+
+        session = tutoring_sessions[session_id]
+        context = session.get_context_summary()
+
+        if not explanation_types:
+            explanation_types = ["visual", "analogy", "real_world", "simplified", "technical"]
+
+        prompt = f"""
+        {context}
+
+        Generate multiple alternative explanations for: "{concept}"
+
+        Create explanations using these approaches: {explanation_types}
+
+        For each explanation type, provide:
+        1. A complete explanation using that approach
+        2. Key benefits of this explanation style
+        3. When this explanation works best
+        4. Supporting examples or analogies
+
+        Return JSON with:
+        - "visual_explanation": explanation using visual/spatial concepts and imagery
+        - "analogy_explanation": explanation using familiar analogies and metaphors
+        - "real_world_explanation": explanation showing real-world applications
+        - "simplified_explanation": very simple, basic explanation
+        - "technical_explanation": detailed, technical explanation
+        - "story_explanation": explanation told as a story or narrative
+        - "comparison_explanation": explanation comparing to similar concepts
+        - "recommendation": which explanation might work best for this student
+        """
+
+        response = await MODEL.generate_text(prompt, temperature=0.7)
+        explanations_data = extract_json_from_text(response)
+
+        return {
+            "success": True,
+            "session_id": session_id,
+            "concept": concept,
+            "explanation_types": explanation_types,
+            "explanations": explanations_data
+        }
+
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to generate alternative explanations: {str(e)}"
+        }
+
+@mcp.tool()
+async def update_student_understanding(session_id: str, concept: str,
+                                     understanding_level: float,
+                                     feedback: str = "") -> dict:
+    """
+    Update student's understanding level and adapt tutoring accordingly.
+
+    Args:
+        session_id: Active tutoring session ID
+        concept: The concept being learned
+        understanding_level: Student's understanding level (0.0 to 1.0)
+        feedback: Optional feedback from student
+
+    Returns:
+        Dictionary with updated session information and recommendations
+    """
+    try:
+        if session_id not in tutoring_sessions:
+            return {
+                "success": False,
+                "error": "Invalid session ID. Please start a new tutoring session."
+            }
+
+        session = tutoring_sessions[session_id]
+
+        # Update session understanding level
+        session.student_understanding_level = understanding_level
+        session.current_topic = concept
+
+        # Generate adaptive recommendations based on understanding level
+        prompt = f"""
+        Student understanding update:
+        - Concept: {concept}
+        - Understanding Level: {understanding_level:.2f}/1.0
+        - Student Feedback: {feedback or 'No feedback provided'}
+        - Session Context: {session.get_context_summary()}
+
+        Based on this understanding level, provide adaptive recommendations:
+
+        Return JSON with:
+        - "understanding_assessment": assessment of student's current understanding
+        - "next_actions": recommended next steps based on understanding level
+        - "difficulty_adjustment": how to adjust difficulty (easier/harder/same)
+        - "focus_areas": specific areas that need more attention
+        - "encouragement": encouraging message appropriate for their level
+        - "study_strategies": personalized study strategies
+        - "time_estimate": estimated time to reach mastery
+        """
+
+        response = await MODEL.generate_text(prompt, temperature=0.6)
+        recommendations_data = extract_json_from_text(response)
+
+        # Add this update to conversation history
+        session.add_interaction(
+            f"Understanding update for {concept}: {understanding_level:.2f}",
+            f"Updated understanding and provided recommendations",
+            "understanding_update"
+        )
+
+        return {
+            "success": True,
+            "session_id": session_id,
+            "concept": concept,
+            "updated_understanding_level": understanding_level,
+            "recommendations": recommendations_data,
+            "session_stats": {
+                "interactions_count": len(session.conversation_history),
+                "current_topic": session.current_topic,
+                "understanding_level": session.student_understanding_level
+            }
+        }
+
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to update understanding: {str(e)}"
+        }
+
+@mcp.tool()
+async def get_tutoring_session_status(session_id: str) -> dict:
+    """
+    Get the current status and context of a tutoring session.
+
+    Args:
+        session_id: Tutoring session ID
+
+    Returns:
+        Dictionary with session status and statistics
+    """
+    try:
+        if session_id not in tutoring_sessions:
+            return {
+                "success": False,
+                "error": "Session not found"
+            }
+
+        session = tutoring_sessions[session_id]
+
+        return {
+            "success": True,
+            "session_id": session_id,
+            "student_id": session.student_id,
+            "subject": session.subject,
+            "current_topic": session.current_topic,
+            "created_at": session.created_at.isoformat(),
+            "last_activity": session.last_activity.isoformat(),
+            "duration_minutes": (datetime.utcnow() - session.created_at).total_seconds() / 60,
+            "understanding_level": session.student_understanding_level,
+            "preferred_style": session.preferred_explanation_style,
+            "learning_objectives": session.learning_objectives,
+            "interaction_count": len(session.conversation_history),
+            "recent_topics": list(set([
+                interaction.get("query", "")[:50]
+                for interaction in session.conversation_history[-5:]
+            ]))
+        }
+
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to get session status: {str(e)}"
+        }
+
+@mcp.tool()
+async def end_tutoring_session(session_id: str, session_summary: str = "") -> dict:
+    """
+    End a tutoring session and generate a summary.
+
+    Args:
+        session_id: Tutoring session ID
+        session_summary: Optional summary from student
+
+    Returns:
+        Dictionary with session summary and recommendations
+    """
+    try:
+        if session_id not in tutoring_sessions:
+            return {
+                "success": False,
+                "error": "Session not found"
+            }
+
+        session = tutoring_sessions[session_id]
+
+        # Generate session summary
+        prompt = f"""
+        Generate a comprehensive summary for this tutoring session:
+
+        {session.get_context_summary()}
+
+        Session Details:
+        - Duration: {(datetime.utcnow() - session.created_at).total_seconds() / 60:.1f} minutes
+        - Interactions: {len(session.conversation_history)}
+        - Final Understanding Level: {session.student_understanding_level:.2f}/1.0
+        - Student Summary: {session_summary or 'No summary provided'}
+
+        Return JSON with:
+        - "session_summary": comprehensive summary of what was covered
+        - "learning_achievements": what the student accomplished
+        - "areas_covered": topics and concepts discussed
+        - "understanding_progress": how understanding evolved during session
+        - "recommendations": recommendations for future study
+        - "next_session_suggestions": suggestions for next tutoring session
+        - "study_plan": personalized study plan based on session
+        - "strengths_identified": student strengths observed
+        - "areas_for_improvement": areas that need more work
+        """
+
+        response = await MODEL.generate_text(prompt, temperature=0.6)
+        summary_data = extract_json_from_text(response)
+
+        # Store session summary before removing
+        session_data = {
+            "session_id": session_id,
+            "student_id": session.student_id,
+            "subject": session.subject,
+            "duration_minutes": (datetime.utcnow() - session.created_at).total_seconds() / 60,
+            "interaction_count": len(session.conversation_history),
+            "final_understanding": session.student_understanding_level,
+            "summary": summary_data
+        }
+
+        # Remove session from active sessions
+        del tutoring_sessions[session_id]
+
+        return {
+            "success": True,
+            "session_ended": True,
+            **session_data
+        }
+
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to end session: {str(e)}"
+        }
+
+@mcp.tool()
+async def list_active_tutoring_sessions(student_id: str = None) -> dict:
+    """
+    List all active tutoring sessions, optionally filtered by student.
+
+    Args:
+        student_id: Optional student ID to filter sessions
+
+    Returns:
+        Dictionary with list of active sessions
+    """
+    try:
+        active_sessions = []
+
+        for session_id, session in tutoring_sessions.items():
+            if student_id is None or session.student_id == student_id:
+                active_sessions.append({
+                    "session_id": session_id,
+                    "student_id": session.student_id,
+                    "subject": session.subject,
+                    "current_topic": session.current_topic,
+                    "created_at": session.created_at.isoformat(),
+                    "last_activity": session.last_activity.isoformat(),
+                    "duration_minutes": (datetime.utcnow() - session.created_at).total_seconds() / 60,
+                    "understanding_level": session.student_understanding_level,
+                    "interaction_count": len(session.conversation_history)
+                })
+
+        return {
+            "success": True,
+            "active_sessions": active_sessions,
+            "total_sessions": len(active_sessions),
+            "filtered_by_student": student_id is not None
+        }
+
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to list sessions: {str(e)}"
+        }

mcp_server/tools/content_generation_tools.py

@@ -0,0 +1,499 @@
+"""
+Advanced Automated Content Generation tools for TutorX.
+Generates interactive content, exercises, and adaptive learning materials.
+"""
+
+import json
+import uuid
+from datetime import datetime
+from typing import Dict, Any, List, Optional
+from mcp_server.mcp_instance import mcp
+from mcp_server.model.gemini_flash import GeminiFlash
+
+MODEL = GeminiFlash()
+
+def extract_json_from_text(text: str):
+    """Extract JSON from text response"""
+    import re
+    if not text or not isinstance(text, str):
+        return None
+    # Remove code fences
+    text = re.sub(r'^\s*```(?:json)?\s*', '', text, flags=re.IGNORECASE)
+    text = re.sub(r'\s*```\s*$', '', text, flags=re.IGNORECASE)
+    text = text.strip()
+    # Remove trailing commas
+    text = re.sub(r',([ \t\r\n]*[}}\]])', r'\1', text)
+    return json.loads(text)
+
+@mcp.tool()
+async def generate_interactive_exercise(concept: str, exercise_type: str = "problem_solving",
+                                      difficulty_level: float = 0.5, 
+                                      student_level: str = "intermediate") -> dict:
+    """
+    Generate interactive exercises with multiple components and adaptive features.
+    
+    Args:
+        concept: The concept to create exercises for
+        exercise_type: Type of exercise ('problem_solving', 'simulation', 'case_study', 'lab', 'project')
+        difficulty_level: Difficulty level (0.0 to 1.0)
+        student_level: Student's academic level
+        
+    Returns:
+        Dictionary with interactive exercise content
+    """
+    try:
+        prompt = f"""
+        Create an interactive {exercise_type} exercise for the concept: "{concept}"
+        
+        Exercise Parameters:
+        - Difficulty Level: {difficulty_level:.2f}/1.0
+        - Student Level: {student_level}
+        - Exercise Type: {exercise_type}
+        
+        Generate a comprehensive interactive exercise that includes:
+        1. Clear learning objectives
+        2. Step-by-step instructions
+        3. Interactive components that engage the student
+        4. Multiple difficulty levels within the exercise
+        5. Real-time feedback mechanisms
+        6. Assessment criteria
+        7. Extension activities for advanced students
+        
+        Return JSON with:
+        - "exercise_id": unique identifier
+        - "title": engaging title for the exercise
+        - "learning_objectives": list of specific learning goals
+        - "introduction": engaging introduction to the exercise
+        - "instructions": detailed step-by-step instructions
+        - "interactive_components": list of interactive elements
+        - "materials_needed": required materials or resources
+        - "estimated_time": estimated completion time in minutes
+        - "difficulty_variations": easier and harder versions
+        - "assessment_rubric": criteria for evaluating student work
+        - "feedback_prompts": questions for self-reflection
+        - "extension_activities": additional challenges for advanced students
+        - "real_world_connections": how this relates to real-world applications
+        """
+        
+        response = await MODEL.generate_text(prompt, temperature=0.7)
+        exercise_data = extract_json_from_text(response)
+        
+        # Add metadata
+        exercise_data.update({
+            "success": True,
+            "concept": concept,
+            "exercise_type": exercise_type,
+            "difficulty_level": difficulty_level,
+            "student_level": student_level,
+            "generated_at": datetime.utcnow().isoformat(),
+            "ai_generated": True
+        })
+        
+        return exercise_data
+        
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to generate interactive exercise: {str(e)}"
+        }
+
+@mcp.tool()
+async def generate_adaptive_content_sequence(topic: str, student_profile: dict,
+                                           sequence_length: int = 5) -> dict:
+    """
+    Generate a sequence of adaptive content that adjusts based on student profile.
+    
+    Args:
+        topic: Main topic for the content sequence
+        student_profile: Student's learning profile and preferences
+        sequence_length: Number of content pieces in the sequence
+        
+    Returns:
+        Dictionary with adaptive content sequence
+    """
+    try:
+        prompt = f"""
+        Create an adaptive content sequence for the topic: "{topic}"
+        
+        Student Profile:
+        {json.dumps(student_profile, indent=2)}
+        
+        Generate a sequence of {sequence_length} interconnected content pieces that:
+        1. Build upon each other progressively
+        2. Adapt to the student's learning style and preferences
+        3. Include multiple content types (text, visual descriptions, interactive elements)
+        4. Provide branching paths based on understanding
+        5. Include assessment checkpoints
+        6. Offer remediation and enrichment options
+        
+        Return JSON with:
+        - "sequence_id": unique identifier for the sequence
+        - "topic": main topic
+        - "total_pieces": number of content pieces
+        - "estimated_total_time": total estimated time in minutes
+        - "content_sequence": array of content pieces, each with:
+          - "piece_id": unique identifier
+          - "title": content title
+          - "content_type": type of content (explanation, example, practice, assessment)
+          - "content": main content text
+          - "visual_elements": descriptions of visual components
+          - "interactive_elements": interactive components
+          - "difficulty_level": difficulty rating
+          - "prerequisites": what student should know
+          - "learning_objectives": specific objectives for this piece
+          - "assessment_questions": questions to check understanding
+          - "branching_options": different paths based on performance
+          - "estimated_time": time for this piece
+        - "adaptation_rules": rules for how content adapts to student responses
+        - "success_criteria": criteria for successful completion
+        """
+        
+        response = await MODEL.generate_text(prompt, temperature=0.6)
+        sequence_data = extract_json_from_text(response)
+        
+        # Add metadata
+        sequence_data.update({
+            "success": True,
+            "topic": topic,
+            "student_profile": student_profile,
+            "generated_at": datetime.utcnow().isoformat(),
+            "ai_generated": True
+        })
+        
+        return sequence_data
+        
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to generate adaptive content sequence: {str(e)}"
+        }
+
+@mcp.tool()
+async def generate_scenario_based_learning(concept: str, scenario_type: str = "real_world",
+                                         complexity_level: str = "moderate") -> dict:
+    """
+    Generate scenario-based learning content with realistic situations.
+    
+    Args:
+        concept: The concept to teach through scenarios
+        scenario_type: Type of scenario ('real_world', 'historical', 'futuristic', 'problem_solving')
+        complexity_level: Complexity of the scenario ('simple', 'moderate', 'complex')
+        
+    Returns:
+        Dictionary with scenario-based learning content
+    """
+    try:
+        prompt = f"""
+        Create a {scenario_type} scenario-based learning experience for: "{concept}"
+        
+        Scenario Parameters:
+        - Type: {scenario_type}
+        - Complexity: {complexity_level}
+        
+        Design a realistic scenario that:
+        1. Presents the concept in a meaningful context
+        2. Requires students to apply their knowledge
+        3. Includes decision points and consequences
+        4. Provides multiple solution paths
+        5. Connects to real-world applications
+        6. Engages students emotionally and intellectually
+        
+        Return JSON with:
+        - "scenario_id": unique identifier
+        - "title": compelling scenario title
+        - "setting": detailed description of the scenario setting
+        - "background_story": engaging background narrative
+        - "main_challenge": primary challenge students must solve
+        - "characters": key characters in the scenario (if applicable)
+        - "decision_points": critical decisions students must make
+        - "possible_outcomes": different outcomes based on decisions
+        - "learning_integration": how the concept is woven into the scenario
+        - "assessment_criteria": how to evaluate student responses
+        - "discussion_questions": questions for reflection and discussion
+        - "extension_scenarios": related scenarios for further exploration
+        - "real_world_connections": connections to actual situations
+        - "resources": additional resources students might need
+        """
+        
+        response = await MODEL.generate_text(prompt, temperature=0.8)
+        scenario_data = extract_json_from_text(response)
+        
+        # Add metadata
+        scenario_data.update({
+            "success": True,
+            "concept": concept,
+            "scenario_type": scenario_type,
+            "complexity_level": complexity_level,
+            "generated_at": datetime.utcnow().isoformat(),
+            "ai_generated": True
+        })
+        
+        return scenario_data
+        
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to generate scenario-based learning: {str(e)}"
+        }
+
+@mcp.tool()
+async def generate_multimodal_content(concept: str, modalities: List[str] = None,
+                                    target_audience: str = "general") -> dict:
+    """
+    Generate multi-modal content that appeals to different learning styles.
+    
+    Args:
+        concept: The concept to create content for
+        modalities: List of modalities to include ('visual', 'auditory', 'kinesthetic', 'reading')
+        target_audience: Target audience description
+        
+    Returns:
+        Dictionary with multi-modal content
+    """
+    try:
+        if not modalities:
+            modalities = ["visual", "auditory", "kinesthetic", "reading"]
+            
+        prompt = f"""
+        Create multi-modal content for the concept: "{concept}"
+        
+        Target Audience: {target_audience}
+        Modalities to include: {modalities}
+        
+        Generate content that appeals to different learning styles:
+        
+        Return JSON with:
+        - "content_id": unique identifier
+        - "concept": the main concept
+        - "visual_content": content for visual learners (descriptions of diagrams, charts, images)
+        - "auditory_content": content for auditory learners (descriptions of sounds, music, verbal explanations)
+        - "kinesthetic_content": content for kinesthetic learners (hands-on activities, movement, manipulation)
+        - "reading_content": content for reading/writing learners (text-based materials, written exercises)
+        - "integrated_activities": activities that combine multiple modalities
+        - "accessibility_features": features for students with different abilities
+        - "technology_integration": how technology can enhance each modality
+        - "assessment_variations": different ways to assess understanding for each modality
+        - "adaptation_guidelines": how to adapt content for different needs
+        """
+        
+        response = await MODEL.generate_text(prompt, temperature=0.7)
+        content_data = extract_json_from_text(response)
+        
+        # Add metadata
+        content_data.update({
+            "success": True,
+            "concept": concept,
+            "modalities": modalities,
+            "target_audience": target_audience,
+            "generated_at": datetime.utcnow().isoformat(),
+            "ai_generated": True
+        })
+        
+        return content_data
+        
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to generate multimodal content: {str(e)}"
+        }
+
+@mcp.tool()
+async def generate_adaptive_assessment(concept: str, assessment_type: str = "formative",
+                                     student_data: dict = None) -> dict:
+    """
+    Generate adaptive assessments that adjust based on student responses.
+
+    Args:
+        concept: The concept to assess
+        assessment_type: Type of assessment ('formative', 'summative', 'diagnostic', 'peer')
+        student_data: Optional student performance data
+
+    Returns:
+        Dictionary with adaptive assessment content
+    """
+    try:
+        prompt = f"""
+        Create an adaptive {assessment_type} assessment for: "{concept}"
+
+        Student Data: {json.dumps(student_data or {}, indent=2)}
+
+        Design an assessment that:
+        1. Adapts difficulty based on student responses
+        2. Provides immediate feedback
+        3. Identifies learning gaps
+        4. Offers multiple question types
+        5. Includes branching logic
+        6. Provides detailed analytics
+
+        Return JSON with:
+        - "assessment_id": unique identifier
+        - "title": assessment title
+        - "instructions": clear instructions for students
+        - "question_pool": large pool of questions with metadata:
+          - "question_id": unique identifier
+          - "question_text": the question
+          - "question_type": type (multiple_choice, short_answer, essay, etc.)
+          - "difficulty_level": difficulty rating (0.0 to 1.0)
+          - "cognitive_level": Bloom's taxonomy level
+          - "options": answer options (if applicable)
+          - "correct_answer": correct answer
+          - "explanation": detailed explanation
+          - "hints": progressive hints
+          - "common_misconceptions": common wrong answers and why
+        - "adaptive_rules": rules for question selection and difficulty adjustment
+        - "feedback_templates": templates for different types of feedback
+        - "scoring_rubric": detailed scoring criteria
+        - "analytics_tracking": what data to track for analysis
+        - "remediation_suggestions": suggestions based on performance
+        """
+
+        response = await MODEL.generate_text(prompt, temperature=0.6)
+        assessment_data = extract_json_from_text(response)
+
+        # Add metadata
+        assessment_data.update({
+            "success": True,
+            "concept": concept,
+            "assessment_type": assessment_type,
+            "generated_at": datetime.utcnow().isoformat(),
+            "ai_generated": True
+        })
+
+        return assessment_data
+
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to generate adaptive assessment: {str(e)}"
+        }
+
+@mcp.tool()
+async def generate_gamified_content(concept: str, game_type: str = "quest",
+                                  target_age_group: str = "teen") -> dict:
+    """
+    Generate gamified learning content with game mechanics and engagement features.
+
+    Args:
+        concept: The concept to gamify
+        game_type: Type of game ('quest', 'puzzle', 'simulation', 'competition', 'story')
+        target_age_group: Target age group ('child', 'teen', 'adult')
+
+    Returns:
+        Dictionary with gamified content
+    """
+    try:
+        prompt = f"""
+        Create gamified learning content for: "{concept}"
+
+        Game Parameters:
+        - Game Type: {game_type}
+        - Target Age Group: {target_age_group}
+
+        Design engaging game-based learning that includes:
+        1. Clear game objectives aligned with learning goals
+        2. Progressive difficulty levels
+        3. Reward systems and achievements
+        4. Interactive challenges
+        5. Narrative elements (if applicable)
+        6. Social features for collaboration/competition
+
+        Return JSON with:
+        - "game_id": unique identifier
+        - "title": engaging game title
+        - "theme": game theme and setting
+        - "storyline": narrative framework (if applicable)
+        - "learning_objectives": educational goals
+        - "game_mechanics": core game mechanics and rules
+        - "levels": progressive levels with increasing difficulty
+        - "challenges": specific challenges and tasks
+        - "reward_system": points, badges, achievements
+        - "character_progression": how players advance
+        - "social_features": multiplayer or collaborative elements
+        - "assessment_integration": how learning is assessed within the game
+        - "feedback_mechanisms": how players receive feedback
+        - "customization_options": ways to personalize the experience
+        - "accessibility_features": features for different abilities
+        """
+
+        response = await MODEL.generate_text(prompt, temperature=0.8)
+        game_data = extract_json_from_text(response)
+
+        # Add metadata
+        game_data.update({
+            "success": True,
+            "concept": concept,
+            "game_type": game_type,
+            "target_age_group": target_age_group,
+            "generated_at": datetime.utcnow().isoformat(),
+            "ai_generated": True
+        })
+
+        return game_data
+
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to generate gamified content: {str(e)}"
+        }
+
+@mcp.tool()
+async def validate_generated_content(content_data: dict, validation_criteria: dict = None) -> dict:
+    """
+    Validate and quality-check generated educational content.
+
+    Args:
+        content_data: The content to validate
+        validation_criteria: Specific criteria for validation
+
+    Returns:
+        Dictionary with validation results and suggestions
+    """
+    try:
+        default_criteria = {
+            "educational_alignment": True,
+            "age_appropriateness": True,
+            "clarity": True,
+            "engagement": True,
+            "accessibility": True,
+            "accuracy": True
+        }
+
+        criteria = {**default_criteria, **(validation_criteria or {})}
+
+        prompt = f"""
+        Validate this educational content against quality criteria:
+
+        Content to Validate:
+        {json.dumps(content_data, indent=2)}
+
+        Validation Criteria:
+        {json.dumps(criteria, indent=2)}
+
+        Perform comprehensive validation and return JSON with:
+        - "overall_quality_score": score from 0.0 to 1.0
+        - "validation_results": detailed results for each criterion
+        - "strengths": identified strengths in the content
+        - "areas_for_improvement": specific areas that need work
+        - "suggestions": concrete suggestions for improvement
+        - "compliance_check": compliance with educational standards
+        - "accessibility_assessment": accessibility for different learners
+        - "engagement_analysis": analysis of engagement potential
+        - "accuracy_verification": verification of factual accuracy
+        - "recommended_modifications": specific modifications to make
+        """
+
+        response = await MODEL.generate_text(prompt, temperature=0.3)
+        validation_data = extract_json_from_text(response)
+
+        return {
+            "success": True,
+            "content_validated": True,
+            "validation_timestamp": datetime.utcnow().isoformat(),
+            **validation_data
+        }
+
+    except Exception as e:
+        return {
+            "success": False,
+            "error": f"Failed to validate content: {str(e)}"
+        }

tests/test_ai_integration.py

@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+"""
+Test script for the new AI integration features in TutorX-MCP.
+Tests the contextualized AI tutoring and automated content generation.
+"""
+
+import asyncio
+import json
+import sys
+import os
+
+# Add the current directory to the Python path
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from mcp_server.tools.ai_tutor_tools import (
+    start_tutoring_session,
+    ai_tutor_chat,
+    get_step_by_step_guidance,
+    get_alternative_explanations,
+    end_tutoring_session
+)
+
+from mcp_server.tools.content_generation_tools import (
+    generate_interactive_exercise,
+    generate_scenario_based_learning,
+    generate_gamified_content
+)
+
+async def test_ai_tutoring():
+    """Test the AI tutoring functionality"""
+    print("🤖 Testing AI Tutoring Features...")
+    
+    # Test 1: Start a tutoring session
+    print("\n1. Starting tutoring session...")
+    session_result = await start_tutoring_session(
+        student_id="test_student_001",
+        subject="Mathematics",
+        learning_objectives=["Understand quadratic equations", "Learn factoring methods"]
+    )
+    print(f"Session started: {session_result.get('success', False)}")
+    
+    if not session_result.get('success'):
+        print(f"❌ Failed to start session: {session_result.get('error')}")
+        return
+    
+    session_id = session_result.get('session_id')
+    print(f"Session ID: {session_id}")
+    
+    # Test 2: Chat with AI tutor
+    print("\n2. Chatting with AI tutor...")
+    chat_result = await ai_tutor_chat(
+        session_id=session_id,
+        student_query="How do I solve quadratic equations?",
+        request_type="explanation"
+    )
+    print(f"Chat response received: {chat_result.get('success', False)}")
+    
+    # Test 3: Get step-by-step guidance
+    print("\n3. Getting step-by-step guidance...")
+    steps_result = await get_step_by_step_guidance(
+        session_id=session_id,
+        concept="Solving quadratic equations",
+        current_step=1
+    )
+    print(f"Step-by-step guidance received: {steps_result.get('success', False)}")
+    
+    # Test 4: Get alternative explanations
+    print("\n4. Getting alternative explanations...")
+    alt_result = await get_alternative_explanations(
+        session_id=session_id,
+        concept="Quadratic formula",
+        explanation_types=["visual", "analogy", "real_world"]
+    )
+    print(f"Alternative explanations received: {alt_result.get('success', False)}")
+    
+    # Test 5: End session
+    print("\n5. Ending tutoring session...")
+    end_result = await end_tutoring_session(
+        session_id=session_id,
+        session_summary="Learned about quadratic equations and different solving methods"
+    )
+    print(f"Session ended: {end_result.get('success', False)}")
+    
+    print("✅ AI Tutoring tests completed!")
+
+async def test_content_generation():
+    """Test the content generation functionality"""
+    print("\n🎨 Testing Content Generation Features...")
+    
+    # Test 1: Generate interactive exercise
+    print("\n1. Generating interactive exercise...")
+    exercise_result = await generate_interactive_exercise(
+        concept="Photosynthesis",
+        exercise_type="simulation",
+        difficulty_level=0.6,
+        student_level="intermediate"
+    )
+    print(f"Interactive exercise generated: {exercise_result.get('success', False)}")
+    
+    # Test 2: Generate scenario-based learning
+    print("\n2. Generating scenario-based learning...")
+    scenario_result = await generate_scenario_based_learning(
+        concept="Climate Change",
+        scenario_type="real_world",
+        complexity_level="moderate"
+    )
+    print(f"Scenario-based learning generated: {scenario_result.get('success', False)}")
+    
+    # Test 3: Generate gamified content
+    print("\n3. Generating gamified content...")
+    game_result = await generate_gamified_content(
+        concept="Fractions",
+        game_type="quest",
+        target_age_group="teen"
+    )
+    print(f"Gamified content generated: {game_result.get('success', False)}")
+    
+    print("✅ Content Generation tests completed!")
+
+async def main():
+    """Run all tests"""
+    print("🚀 Starting TutorX AI Integration Tests...")
+    print("=" * 50)
+    
+    try:
+        # Test AI Tutoring
+        await test_ai_tutoring()
+        
+        # Test Content Generation
+        await test_content_generation()
+        
+        print("\n" + "=" * 50)
+        print("🎉 All tests completed successfully!")
+        
+    except Exception as e:
+        print(f"\n❌ Test failed with error: {str(e)}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    asyncio.run(main())