We apply machine learning in education to constantly track students' progress and further adjust content to their needs. ML algorithms also provide educators with feedback to define students' interests, identify pain points, detect knowledge gaps, and provide needed support. ML ensures personalized experience to tailor the learning content to the student's learning style.

Modern NLP techniques empower foreign language learners with speech processing techniques via analyzing the phrases learners are saying and providing immediate feedback. Also, automatic text summarization, machine translation (MT), semantic and sentiment analysis techniques come in handy for educators and students.

Centralizing education data and analyzing it with the help of predictive modeling is crucial to identify trends in the class. What could students be at risk during the following exams? How to adjust the curriculum to those who lag behind and succeed in their studying? How to minimize drop-out rates? The modern educational system, enjoying the machine learning benefits, can quickly answer these and many other questions.

Analyzing the video frames from cameras with AI algorithms, computer vision "understands" the image and can cope with essential tasks to guarantee security at the educational facility. So this is how Computer Vision systems are crucial for attendance monitoring, suspicious objects detection, facial emotion analysis, vandalism prevention systems, parking management systems, intrusion detection at educational facilities, and social distancing and mask detection.