First an overview of Machine Learning is presented, focusing on the use of data in learning. Then Natural Language Processing is detailed, starting with word embedding, namely the transformation of words in vectors. This approach enabled the development of the Large language Models that are trained on huge data texts and exploit statistical relations between words to model text. GPT and ChatGPT are overviewed, as well as their qualities and shortcomings. The use of LLMs and AI in education are also presented. The distinction between morphosis and education is detailed, as well as its application on citizen and scientist morphosis. Educational systems modeling is overviewed from a Systems and Information Theory point-of-view.   Finally, the effects of AI on University education are presented, particularly on Education Sciences.