Definitions in AI Education

To ensure clear understanding of AI implementation in our educational context, here are the key terms and concepts:

Artificial Intelligence (AI)
Computer systems capable of performing tasks that typically require human intelligence, including learning, problem-solving, and pattern recognition.

Machine Learning (ML)
Systems that improve their performance through experience and data analysis without explicit programming.

Learning Analytics
The measurement, collection, analysis, and reporting of data about learners to understand and optimize learning environments.

Adaptive Learning
Educational systems that adjust to individual student needs based on performance and interaction data.

Intelligent Tutoring Systems (ITS)
Software designed to simulate one-on-one tutoring by providing immediate feedback and personalized instruction.

Educational Data Mining (EDM)
The process of analyzing educational data to better understand students and their learning environments.

Personalized Learning Path
A customized learning journey created using AI algorithms based on individual student progress, preferences, and goals.

Predictive Analytics
Using data, statistical algorithms, and machine learning techniques to identify the likelihood of future learning outcomes.

Smart Content
Educational materials that adapt and respond to student interactions and learning patterns.

Automated Assessment
AI-powered systems that evaluate student work and provide immediate feedback.