人工智能在教育中的创新应用：打造未来的智慧课堂

    from sklearn.ensemble import RandomForestClassifier
    import pandas as pd
    
    # 学生学习数据：正确率、学习时间、题目类型等
    data = pd.DataFrame({
    'accuracy': [0.8, 0.6, 0.7, 0.5],
    'study_time': [30, 45, 25, 60],
    'topic_geometry': [1, 0, 1, 0]
    })
    
    # 标签：是否需要加强某领域
    labels = [0, 1, 0, 1]
    
    # 模型训练
    model = RandomForestClassifier()
    model.fit(data, labels)
    
    # 输入新学生的数据
    new_student = pd.DataFrame({
    'accuracy': [0.6],
    'study_time': [20],
    'topic_geometry': [1]
    })
    
    # 预测结果
    recommendation = model.predict(new_student)
    print("是否需要推荐几何相关学习资源：", recommendation[0])

    from transformers import pipeline
    
    # 使用预训练的语言模型进行文本纠错
    nlp = pipeline("text2text-generation", model="t5-small")
    
    # 学生作文
    student_essay = "I has a pen. It are red."
    
    # 生成修改后的文本
    corrected_text = nlp("grammar correction: " + student_essay)
    print("批改建议：", corrected_text[0]['generated_text'])

    import cv2
    import mediapipe as mp
    
    # 初始化表情识别模型
    mp_face_detection = mp.solutions.face_detection
    
    # 从摄像头获取图像数据
    cap = cv2.VideoCapture(0)
    with mp_face_detection.FaceDetection(min_detection_confidence=0.2) as face_detection:
    while cap.isOpened():
        success, image = cap.read()
        if not success:
            break
    
        # 处理图像
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        results = face_detection.process(image)
    
        # 分析注意力
        if results.detections:
            print("注意力集中")
        else:
            print("注意力分散")
    
    cap.release()