The application of Artificial Intelligence or AI in education has been the subject of academic research for more than 30 years. The field examines learning wherever it occurs, in traditional classrooms or at workplaces so to support formal education and lifelong learning. It combines interdisciplinary AI and learning sciences (such as education, psychology, neuroscience, linguistics, sociology and anthropology) in order to facilitate the development of effective adaptive learning environments and various flexible, inclusive tools. Nowadays, there are several new challenges in the field of education technology in the era of smart phones, tablets, cloud computing, Big Data, etc., whose current research questions focus on concepts such as ICT-enabled personalized learning, mobile learning, educational games, collaborative learning on social media, MOOCs, augmented reality application in education and so on. Therefore, to meet these new challenges in education, several fields of research using AI have emerged over time to improve teaching and learning using digital technologies. Moreover, each field of research is distinguished by its own vision and methodologies. In this article, to the authors present a state of the art finding in the fields of research of Artificial Intelligence in Education or AIED, Educational Data Mining or EDM and Learning Analytics or LA. We discuss their historical elements, definition attempts, objectives, adopted methodologies, application examples and challenges.

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Despite the great success of Massive Open Online Courses (MOOCs), the success rate of learners remains very low. Therefore, a lot of research has been done to understand and solve this abandonment problem. This work is part of the same effort which aims to improve learning in MOOCs. We offer an intelligent system capable of assisting the learner by providing answers to all his questions on the subjects covered in the MOOC. The architecture of our system is based on new advances in artificial intelligence, in particular the applications of deep learning in the field of natural language processing (NLP). The results obtained are quite interesting and demonstrate the relevance of our solution.

Nowadays, AI is a real springboard for finding solutions to optimize and improve learning and teaching processes. This issue has been a focus of humanity for millennia, and very significant advances have been made in this quest. This article aims to address the issue of optimizing and improving learning and teaching processes through AI (Artificial Intelligence), considering crossroads of research fields AIED (Artificial Intelligence in Education), EDM (Educational Data Mining) and LA (Learning Analytic). The research made use of secondary data collected from previous research on the topic and primary data was collected using a case study. A comparative analysis was conducted and based on this opportunity, we propose a multi-agent system based on AI techniques, which is capable of performing broader analyses of learning and teaching processes. The research also implemented a prototype of EMAS. Through this system, teachers and learners will be able to access a wide range of relevant and reliable information about learning and teaching processes.   Key words: AIED, EDM, EMAS, LA, recommendation system, education dropping out, emotion detection