AI技术 - 数字人教育培训应用技术

# 智能教学助手示例 - Python import numpy as np import pandas as pd from typing import List # import Dict import Tuple import json from datetime import datetime class IntelligentTeachingAssistant: def __init__(self, knowledge_base_path: str, student_model_path: str): self.knowledge_base = self.load_knowledge_base(knowledge_base_path) self.student_model = self.load_student_model(student_model_path) self.conversation_history = [] self.learning_analytics = LearningAnalytics() def load_knowledge_base(self, path: str) -> Dict: """加载知识库""" with open(path, 'r', encoding='utf-8') as f: return json.load(f) def load_student_model(self, path: str) -> Dict: """加载学生模型""" with open(path, 'r', encoding='utf-8') as f: return json.load(f) def process_student_query(self, student_id: str, query: str) -> Dict: """处理学生查询""" # 分析查询意图 # intent = self.analyze_intent(query) # 获取学生信息 student_info = self.student_model.get(student_id, {}) # # 生成回答 response = self.generate_response(query, intent, student_info)# 记录对话历史 # self.record_conversation(student_id, query, response) # 更新学习分析 # self.learning_analytics.update_learning_data(student_id, query, response) return { 'response': response, 'intent': intent, 'suggestions': self.generate_suggestions(student_info, intent), 'learning_tips': self.generate_learning_tips(student_info) } def analyze_intent(self, query: str) -> str: """分析查询意图""" # 使用NLP技术分析查询意图 intent_keywords = { 'concept_explanation': ['什么是', '解释', '概念', '定义'], # 'problem_solving': ['如何', '怎么', '解决', '方法'], 'example_request': ['例子', '示例', '举例', '实例'], 'practice_request': ['练习', '题目', '作业', '测试'], 'review_request': ['复习', '回顾', '总结', '梳理'] } query_lower = query.lower() for intent, keywords in intent_keywords.items(): if any(keyword in query_lower for keyword in keywords): return intent return 'general_query' def generate_response(self, query: str, intent: str, student_info: Dict) -> str: """生成回答""" if intent == 'concept_explanation': return self.explain_concept(query, student_info) elif intent == 'problem_solving': return self.solve_problem(query, student_info) elif intent == 'example_request': return self.provide_examples(query, student_info) elif intent == 'practice_request': return self.provide_practice(query, student_info) elif intent == 'review_request': return self.provide_review(query, student_info) else: return self.general_response(query, student_info) def explain_concept(self, query: str, student_info: Dict) -> str: """解释概念""" # 从知识库中查找相关概念 # concept = self.find_concept_in_knowledge_base(query) if concept: # 根据学生水平调整解释难度 # difficulty_level = student_info.get('difficulty_level', 'intermediate') explanation = self.adjust_explanation_difficulty(concept, difficulty_level) return f"让我来解释一下{concept['name']}：\n\n{explanation}" else: return "抱歉，我没有找到相关的概念解释。请尝试用其他方式描述您的问题。" def solve_problem(self, query: str, student_info: Dict) -> str: """解决问题""" # 分析问题类型 # problem_type = self.classify_problem_type(query) # 获取解题步骤 solution_steps = # self.get_solution_steps(problem_type, query) # 根据学生水平调整解题过程 student_level = # student_info.get('math_level', 'intermediate') adjusted_steps = self.adjust_solution_difficulty(solution_steps, student_level) return self.format_solution(adjusted_steps) def provide_examples(self, query: str, student_info: Dict) -> str: """提供例子""" # 从知识库中查找相关例子 examples = self.find_examples_in_knowledge_base(query) if # examples: # 选择适合学生水平的例子 student_level = student_info.get('level', 'intermediate') # selected_examples = self.select_appropriate_examples(examples, student_level) return self.format_examples(selected_examples) else: return "抱歉，我没有找到相关的例子。请尝试用其他方式描述您的问题。" def generate_suggestions(self, student_info: Dict, intent: str) -> List[str]: """生成学习建议""" suggestions = [] # 基于学习历史生成建议 learning_history = student_info.get('learning_history', []) # weak_areas = self.identify_weak_areas(learning_history) if weak_areas: suggestions.append(f"建议重点复习：{', '.join(weak_areas)}") # 基于学习进度生成建议 learning_progress = student_info.get('learning_progress', # {}) next_topics = self.recommend_next_topics(learning_progress) if next_topics: suggestions.append(f"建议学习：{', '.join(next_topics)}") return suggestions def generate_learning_tips(self, student_info: Dict) -> List[str]: """生成学习技巧""" tips = [] # 基于学习风格生成技巧 learning_style = student_info.get('learning_style', 'visual') if learning_style == # 'visual': tips.append("尝试使用图表和图像来理解概念") elif learning_style == 'auditory': tips.append("尝试通过讲解和讨论来学习") elif learning_style == 'kinesthetic': tips.append("尝试通过实践和操作来学习") # 基于学习时间生成技巧 study_time = # student_info.get('study_time', 'evening') if study_time == 'morning': tips.append("早晨是学习的最佳时间，建议安排重要内容") elif study_time == 'evening': tips.append("晚上学习时注意休息，避免过度疲劳") return tips def record_conversation(self, student_id: str, query: str, response: str): """记录对话历史""" conversation = { 'student_id': student_id, 'query': query, 'response': response, 'timestamp': datetime.now().isoformat() } self.conversation_history.append(conversation) def find_concept_in_knowledge_base(self, query: str) -> Dict: """在知识库中查找概念""" # 实现概念查找逻辑 # pass def adjust_explanation_difficulty(self, concept: Dict, difficulty_level: str) -> str: """调整解释难度""" # 实现难度调整逻辑 pass def classify_problem_type(self, query: str) -> str: # """分类问题类型""" # 实现问题分类逻辑 pass def get_solution_steps(self, problem_type: str, query: str) -> # List[str]: """获取解题步骤""" # 实现解题步骤获取逻辑 pass def adjust_solution_difficulty(self, steps: # List[str], student_level: str) -> List[str]: """调整解题难度""" # 实现难度调整逻辑 pass def # format_solution(self, steps: List[str]) -> str: """格式化解题过程""" formatted = "解题步骤：\n\n" for i, step in enumerate(steps, 1): formatted += f"{i}. {step}\n" return formatted def find_examples_in_knowledge_base(self, query: str) -> List[Dict]: """在知识库中查找例子""" # 实现例子查找逻辑 # pass def select_appropriate_examples(self, examples: List[Dict], student_level: str) -> List[Dict]: """选择适合的例子""" # 实现例子选择逻辑 pass def format_examples(self, examples: List[Dict]) -> str: # """格式化例子""" formatted = "相关例子：\n\n" for i, example in enumerate(examples, 1): formatted += f"{i}. {example['description']}\n" return formatted def identify_weak_areas(self, learning_history: List[Dict]) -> List[str]: """识别薄弱环节""" # 实现薄弱环节识别逻辑 pass def # recommend_next_topics(self, learning_progress: Dict) -> List[str]: """推荐下一个学习主题""" # 实现主题推荐逻辑 pass class LearningAnalytics: def __init__(self): self.learning_data = {} def update_learning_data(self, student_id: str, query: str, response: str): """更新学习数据""" if student_id # not in self.learning_data: self.learning_data[student_id] = { 'queries': [], 'responses': [], 'learning_patterns': {}, 'progress_metrics': {} } self.learning_data[student_id]['queries'].append(query) self.learning_data[student_id]['responses'].append(response) # 分析学习模式 # self.analyze_learning_patterns(student_id) def analyze_learning_patterns(self, student_id: str): """分析学习模式""" # 实现学习模式分析逻辑 pass# 使用示例 if __name__ == "__main__":# 初始化教学助手 # assistant = IntelligentTeachingAssistant( knowledge_base_path="knowledge_base.json", student_model_path="student_model.json" ) # 处理学生查询 student_id = "student_001" query = "什么是机器学习？" # result = assistant.process_student_query(student_id, query) print("回答:", result['response']) print("建议:", result['suggestions']) print("学习技巧:", result['learning_tips'])