Description

Artificial Intelligence in education enhances learning experiences by personalizing content, automating assessments, predicting student performance, and supporting educators with intelligent tools. AI technologies are reshaping traditional teaching by making education more interactive, adaptive, and inclusive.

Examples (Code)

Below is the sample code

# Predicting student performance using logistic regression
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

import pandas as pd

# Sample data: [study_hours, attendance_rate, passed_exam (1 = yes, 0 = no)]
data = {
    'study_hours': [2, 4, 6, 8, 10, 12],
    'attendance_rate': [50, 60, 70, 80, 90, 95],
    'passed_exam': [0, 0, 1, 1, 1, 1]
}

df = pd.DataFrame(data)

X = df[['study_hours', 'attendance_rate']]
y = df['passed_exam']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LogisticRegression()
model.fit(X_train, y_train)

predictions = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, predictions))

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