Generative AI is rapidly reshaping educational environments, offering transformative possibilities for teaching, learning, and student engagement. As UNESCO DirectorGeneral Audrey Azoulay aptly remarked, “Education will be profoundly transformed by AI.” This transformation impacts not only how knowledge is accessed but also how learning experiences are personalized, how curricula are developed, and how educators are trained. Despite the growing integration of AI tools into education, particularly generative AI, several critical questions remain: How effectively is generative AI being used to support and enhance learning? Are students merely passive recipients of AI-generated content, or are they active participants in more meaningful, personalized learning experiences? What specific generative AI tools are currently being implemented in classrooms, and how do both educators and students perceive their impact and integration? This paper presents the findings of a comprehensive survey conducted at Esprit School of Engineering (ESE) and Esprit School of Business (ESB). The goal is to capture the perspectives of students and teachers on the application and perceived impact of generative AI tools in educational settings. Using a Likert scale, the survey evaluates attitudes toward usability, effectiveness, and the potential of these tools to enhance both teaching and learning outcomes. Additionally, the study explores whether generative AI helps create a more engaging and interactive learning environment. The primary objective of this research is to explore how generative AI is perceived and utilized in classrooms, as well as the barriers and facilitators that affect its adoption. We begin by reviewing the current landscape of generative AI in education, focusing on its most prevalent applications. We then detail the survey methodology and analyze the results, offering insights into both opportunities and challenges posed by these AI tools. Finally, we conclude by discussing the broader implications of our findings and providing recommendations for future AI integration in education.

To reduce the spread of COVID-19 pandemic, educational institutions were closed in all countries. This closure deteriorated the level of all learners and resulted in a considerable disturbance of the education system. In this context, distance learning was the best solution. This paper reports the findings obtained Likert scale survey on April 2020 sent to learners and teachers of the Tunisian universities. In this survey, the interviewees were asked about their opinions concerning e-learning in the new situation resulting from covid19 crisis. After describing the existing learner models, we present the findings provided by examining the impact of the learning situation on the learner model. Learners and teachers were asked to capture four profile dimensions: interaction, involvement, motivation and emotions during the COVID -19 health crisis. The analysis of the obtained results show that a new learning situation influences negatively the learner model, which proves the importance of considering the situational dimension in such a learner model.

The Multi-Robot Task Allocation (MRTA) problem has recently become a key research topic. Task allocation is the problem of mapping tasks to robots, such that the most appropriate robot is selected to perform the most fitting task, leading to all tasks being optimally accomplished. Expanding the number of tasks and robots may cause the collaboration among the robots to become tougher. Since this process requires high computational time, this paper describes a technique that reduces the size of the explored state space, by partitioning the tasks into clusters. In real-world problems, the absence of information regarding the number of clusters is ordinarily occurring. Hence, a dynamic clustering is auspicious for partitioning the tasks to an appropriate number of clusters. In this paper, we address the problem of MRTA by putting forward a new simple, automatic and efficient clustering algorithm of the robots’ tasks based on a dynamic distributed particle swarm optimization, namely, ACD 2 PSO. Our approach is made out of two stages: stage I groups the tasks into clusters using the dynamic distributed particle swarm optimization (D 2 PSO) algorithm and stage II allocates the robots to the clusters. The assignment of robots to the clusters is represented as multiple traveling salesman problems (MTSP). Computational experiments were carried out to prove the effectiveness of our approach in term of clustering time, cost, and the MRTA time, compared to the distributed particle swarm optimization (dPSO) and genetic algorithm (GA). Thanks to the D 2 PSO algorithm, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing the fast convergence of PSO.

Path planning problem for large number of robots is a quite challenging problem in mobile robotics since their control and coordination becomes unreliable and sometimes unfeasible. Particle Swarm Optimization (PSO) has been demonstrated to be a useful technique in the field of robotic research. This paper discusses an optimal path planning algorithm based on a Dynamic Distributed Particle Swarm Optimization Algorithm (D2PSO). The purpose of this approach is to find collision free optimal paths using two local optima detectors. This would add diversity to the population and hence avoid stagnation problem. The results show that the D2PSO has a better ability to get away from local optimums than the distributed PSO (dPSO). Simulations prove that this methodology is effective for every robot in multi-robot framework to discover its own proper path from the start to the destination position with minimum distance and no collision with obstacles.