The integration of artificial intelligence (AI) in education is a promising transformation. Drawing on advanced technologies, AI enriches the learning experience through intelligent systems capable of analyzing, adapting and personalizing teaching. Despite a growing volume of scientific publications, there remains a lack of critical synthesis on the real impact of AI on the role of teachers, student learning and the transmission of knowledge. To fill this gap, this article proposes a systematic literature review, conducted using the PRISMA method, to identify the opportunities and limitations of AI in educational environments. From 1,248 publications extracted from the Scopus database between 2018 and 2024, 20 relevant studies were selected and analyzed after applying inclusion and exclusion criteria. The results show significant growth in research in this field, and demonstrate that AI enables teachers to automate certain tasks, personalize teaching and better meet learners' individual needs. However, significant obstacles remain, including lack of digital skills, resistance to change, and ethical concerns. The study also points out that AI enhances learners' skills, promoting the personalization of pathways, the identification of struggling students, the adaptation of materials, as well as real-time engagement and monitoring. It also makes it possible to model and transmit knowledge through the creation and adaptation of digital educational resources. However, AI also presents certain limitations in the educational context, such as excessive dependence on technology, inequalities of access, automatic generation of answers without real learning, as well as issues relating to the confidentiality of personal data. AI is a powerful but complex lever in the field of education. Its effective integration requires targeted training for teachers, critical reflection on its uses, and a rigorous ethical framework. This review thus provides a solid basis for guiding future research towards complementary empirical studies, while accompanying practitioners in a reasoned and beneficial adoption of AI in educational contexts.

The mobile agent security problem limits the use of mobile agent technology and hinders its extensibility and application because the constantly progressed complexity and extension at the level of systems and applications level increase the difficulty to implement a common security system as well as an anticipated security policy.
Ontology is considered one of the most important solutions to the problem of heterogeneity. In this context, our work consists of constructing mobile agent domain security ontology (MASO) in order to eliminate semantic differences between security policies in this domain. We use the OWL language under the protected software to construct this ontology. Then, we chose the WS-Policy standard to model security policies, these policies are structured in forms of security requirements and capabilities. To determine the level of semantic correspondence between security policies we are developing an algorithm called "Matching-algorithm" with Java language and two APIs (Jena API and Jdom API) to manipulate the MASO ontology and security policies.