AI Education and Personalized Learning

class StudentProfile: def __init__(self, student_id): self.student_id = student_id self.knowledge_state = {} # topic -> mastery level (0-1) self.learning_style = "adaptive" # visual, auditory, kinesthetic, reading self.difficulty_preference = "adaptive" # easy, medium, hard, adaptive self.session_history = [] self.weak_areas = [] self.strong_areas = [] def update_mastery(self, topic, correctness, confidence): """Update knowledge state based on performance.""" current = self.knowledge_state.get(topic, 0.5) # Bayesian update simplified if correctness: new_mastery = current + (1 - current) * confidence * 0.3 else: new_mastery = current - current * confidence * 0.4 self.knowledge_state[topic] = max(0.1, min(0.95, new_mastery)) def get_recommended_topic(self, curriculum): """Recommend next topic based on prerequisites and mastery.""" candidates = [] for topic in curriculum: prereqs_met = all( self.knowledge_state.get(prereq, 0) > 0.6 for prereq in topic.get('prerequisites', []) ) if prereqs_met and self.knowledge_state.get(topic['id'], 0) < 0.8: candidates.append(topic) # Prioritize weak areas candidates.sort(key=lambda t: self.knowledge_state.get(t['id'], 0)) return candidates[0] if candidates else None

def generate_question(topic, difficulty, student_level, model="deepseek"): """Generate a personalized practice question.""" prompt = f"""Generate an educational practice question on the following topic. Topic: {topic['name']} Subject: {topic['subject']} Student Level: {student_level} (beginner/intermediate/advanced) Target Difficulty: {difficulty} (0=easiest, 1=hardest) Prerequisites Known: {', '.join(topic.get('prerequisites', []))} Requirements: - Match difficulty to student level - Include step-by-step solution process - Provide clear, educational explanation - Include 1-2 common misconceptions as distractors (for multiple choice) - Format: Question, Solution Steps, Final Answer, Common Mistakes to Avoid For math/science: Include LaTeX formatting for equations. For humanities: Include context and analytical framework.""" return call_llm_api(prompt, temperature=0.5, max_tokens=1200)

def socratic_tutor_turn(student_message, topic, conversation_history, model="deepseek"): """Generate Socratic-style tutoring response.""" prompt = f"""You are a patient, encouraging tutor using the Socratic method. Help the student discover the answer through guided questions rather than giving it directly. Topic: {topic} Conversation History: {chr(10).join([f"{msg['role']}: {msg['content']}" for msg in conversation_history[-5:]])} Student's latest message: {student_message} Guidelines: - NEVER give the answer directly on the first attempt - Ask 1-2 guiding questions that point toward the solution - If student is stuck after 3 attempts, provide a partial hint - Acknowledge correct reasoning enthusiastically - Gently redirect misconceptions without being dismissive - Adapt question difficulty based on student's responses - Keep responses concise (2-4 sentences per turn) Your response:""" return call_llm_api(prompt, temperature=0.6, max_tokens=400)

def generate_lesson_plan(topic, grade_level, duration_minutes, model="deepseek"): """Generate structured lesson plan with activities and assessments.""" prompt = f"""Create a detailed lesson plan for the following topic. Topic: {topic} Grade Level: {grade_level} Duration: {duration_minutes} minutes Include: 1. Learning Objectives (3-4 specific, measurable objectives) 2. Prerequisites (what students should know before) 3. Materials Needed 4. Lesson Structure: - Opening/Hook (5-10 min): Engaging introduction - Direct Instruction (10-15 min): Core concept explanation - Guided Practice (10-15 min): Worked examples with student participation - Independent Practice (10-15 min): Exercises for individual work - Closure (5 min): Summary and exit ticket 5. Differentiation Strategies (for struggling and advanced students) 6. Homework Assignment (if applicable) 7. Assessment Methods Make it practical and ready to teach. Include specific examples and questions to ask students.""" return call_llm_api(prompt, temperature=0.4, max_tokens=2000) def generate_study_guide(topic, key_concepts, difficulty="intermediate"): """Generate student study guide with examples and practice problems.""" prompt = f"""Create a comprehensive study guide for students. Topic: {topic} Key Concepts: {', '.join(key_concepts)} Level: {difficulty} Include: 1. Concept Summary (brief overview of each key concept) 2. Formula Sheet (if applicable, with variable definitions) 3. Worked Examples (2-3 detailed examples with full solutions) 4. Practice Problems (5 problems with answers at the end) 5. Common Mistakes (list of typical errors and how to avoid them) 6. Study Tips (memorization techniques, concept connections) 7. Self-Assessment Checklist (can the student do X, Y, Z?) Format for easy printing or digital use.""" return call_llm_api(prompt, temperature=0.4, max_tokens=2500)

def grade_essay(essay_text, rubric, assignment_prompt, model="deepseek"): """Generate structured essay feedback based on rubric.""" prompt = f"""Grade and provide detailed feedback on the following student essay. Assignment Prompt: {assignment_prompt} Rubric Criteria: {chr(10).join([f"- {c['name']} ({c['points']} points): {c['description']}" for c in rubric])} Student Essay: {essay_text} Provide feedback in this structure: 1. Overall Assessment (strengths and areas for improvement) 2. Rubric Scores (score each criterion with justification) 3. Specific Feedback: - Highlight 2-3 specific strong passages - Identify 2-3 areas needing revision with concrete suggestions 4. Grammar/Mechanics (top 5 issues, if any) 5. Revision Plan (prioritized list of changes to make) 6. Encouraging closing message Be constructive and specific. Avoid generic praise. Focus on actionable improvements.""" return call_llm_api(prompt, temperature=0.4, max_tokens=2000)

def translate_educational_content(content, target_language, preserve_formatting=True): """Translate educational content while preserving pedagogical intent.""" prompt = f"""Translate the following educational content into {target_language}. Original Content: {content} Requirements: - Maintain educational accuracy and technical precision - Adapt examples to be culturally relevant where appropriate - Preserve formatting (headings, lists, equations) - Ensure reading level matches original - For technical terms: provide term in target language followed by English in parentheses on first use - Maintain instructional tone Output only the translated content, no meta-commentary.""" return call_llm_api(prompt, temperature=0.3, max_tokens=2500)

def generate_progress_summary(student_profile, time_period="week"): """Generate natural language progress report for student/parent.""" topics_studied = student_profile.get_recent_topics(time_period) assessments = student_profile.get_recent_assessments(time_period) prompt = f"""Generate a progress report based on the following learning data. Student: {student_profile.name} Time Period: Last {time_period} Topics Studied: {', '.join(topics_studied)} Assessment Results: {assessments} Knowledge State: {student_profile.knowledge_state} Generate: 1. Progress Overview (2-3 sentences on overall trajectory) 2. Strengths (topics showing strong mastery) 3. Growth Areas (topics needing more practice) 4. Recommended Next Steps (specific topics and study strategies) 5. Encouragement (personalized motivational message) Write in an encouraging, parent-friendly tone. Be specific about accomplishments.""" return call_llm_api(prompt, temperature=0.5, max_tokens=800)