AI在教育中的潜力与挑战：开启智慧教育的新时代

import numpy as np
from sklearn.cluster import KMeans

# 示例数据：学生的学习成绩（模拟数据）
data = np.array([[85, 70, 90], [60, 80, 70], [78, 85, 88], [50, 60, 65], [92, 95, 93]])

# 使用KMeans算法对学生进行分类，提供个性化学习计划
kmeans = KMeans(n_clusters=3, random_state=0).fit(data)
labels = kmeans.labels_

# 根据分类结果生成个性化学习计划
for i in range(len(data)):
    print(f"学生 {i+1}: 学习计划 {labels[i]+1}")

from transformers import pipeline

# 使用预训练的AI模型进行作文评估
classifier = pipeline('sentiment-analysis')

# 示例作文文本（简化示例）
essay = "This is a very good essay about the importance of AI in education. It covers many important points and provides insightful analysis."

# 评估作文
result = classifier(essay)
print("评估结果:", result)

import pandas as pd
from ortools.sat.python import cp_model

# 示例数据：教师和教室的使用情况（简化示例）
teachers = ["A", "B", "C"]
rooms = ["101", "102", "103"]
times = ["Monday 8-10", "Monday 10-12", "Monday 14-16"]

# 使用约束编程生成最优课程表
model = cp_model.CpModel()
assignments = {}
for t in teachers:
    for r in rooms:
        for time in times:
            assignments[(t, r, time)] = model.NewBoolVar(f'{t}_{r}_{time}')

# 添加约束条件
for t in teachers:
    for time in times:
        model.Add(sum(assignments[(t, r, time)] for r in rooms) == 1)

for r in rooms:
    for time in times:
        model.Add(sum(assignments[(t, r, time)] for t in teachers) <= 1)

# 生成最优课程表
solver = cp_model.CpSolver()
status = solver.Solve(model)

# 打印课程表
if status == cp_model.OPTIMAL:
    for t in teachers:
        for time in times:
            for r in rooms:
                if solver.Value(assignments[(t, r, time)]):
                    print(f"教师 {t} 在 {time} 使用教室 {r}")