Abstract:

The latest developments in the field of Artificial Intelligence (AI) have showcased the immense power of the algorithms that constitute this technology. Countless fields of application have harnessed these technological advancements, and the field of education is no exception.
Academic tools, including Intelligent Tutoring Systems, Knowledge Tracing Models, Natural Language Processing (NLP) Applications, Automated Grading and Feedback Systems, etc., have been developed to enhance the process of learning acquisition.
Thanks to the rapid development of data science facilitated by big data, these AI algorithms have been consistently improving as data generation expands; big data extract and construct new knowledge from the available data.
However, despite these tools potentially utilizing similar mathematical algorithms within their architecture, it may seem that they produce comparable outcomes, such as personalized study materials or identification of knowledge gaps in students. This leads to the question of why multiple tools are being developed, which can be quite puzzling.
This paper provides a concise overview of widely recognized tools that have played a significant role in enhancing education. It offers a succinct pedagogical explanation of these technologies and conducts an analysis of their individual contribution to education, somehow expanding the educational vision by addressing the questions of what to learn and how to learn.
By conducting a comparative analysis of these tools, we can gain a clearer understanding of their similarities and differences. Furthermore, this examination allows us to explore their potential future developments and provide insights into their anticipated trajectories.

Keywords:

Intelligent systems, Intelligent education, knowledge generation, knowledge tracing, Intelligent tutoring systems.