In a world where artificial intelligence is constantly changing education, taking students’ feelings into account is a crucial framework for enhancing their engagement and academic performance. This article presents LearnerEmotions, an online application that employs machine vision technology to determine how learners are feeling in real time through their facial expressions. Teachers and institutions can access analytical dashboards and monitor students’ emotions with this tool, which is designed for use in both in-person and remote classes. The facial expression recognition model used in this application achieved an average accuracy of 0.91 and a loss of 0.3 in the real environment. More than 9 million emotional data points were gathered from an experiment involving 65 computer engineering students, and these insights were correlated with attendance and academic performance. While negative emotions like anger, sadness, and fear are associated with decreased performance and lower attendance, the statistical study shows a strong correlation between positive feelings like surprise and joy and successful academic performance. These results underline the necessity of technological tools that offer immediate pedagogical regulation and support the notion that emotions play an important role in the learning process. Thus, LearnerEmotions, which considers students’ emotional states, is a potential first step toward more adaptive learning.

Comprehending the personality of students is a crucial issue of education. Students interact in learning environments using a variety of social media technologies. therefore, in the availability of several artificial intelligence-based solutions that discern personality from written language, we aim to use students’ text data to determine their personalities. Due to these considerations, the primary purpose of this research is to determine whether the text produced by students in social media-based learning environments can provide insight into their personalities. This work also aims to find out whether students exhibit their real personalities while interacting in the learning environment, mainly with their classmates. This paper describes an experimental study reported on a sample of students at the Private University of Fez, in which their data was gathered and analyzed by three Artificial Intelligence-based personality detection tools. Personality traits discovered by these instruments were evaluated and compared to the findings of big five questionnaire assessed to the same students. The findings demonstrate that the majority of the students’ personality traits were accurately identified in the written text of the students based on tested Natural Language Processing tools, and that strong data quality is a crucial factor in achieving good accuracy.

In the contemporary digital era, social media platforms have a big influence on students’ lives. They use these platforms for self-expression, opinion sharing, and experience reporting (writing or sharing videos or photos about personal experiences) in addition to social interaction. Education professionals and academics may get valuable insights into students’ thoughts, sentiments, interests, academic success, and even personalities by studying their writing on social media. We can improve our teaching, enhance students’ social and emotional development, and create a more engaging learning environment if we have a better knowledge of the student. The purpose of this study is to ascertain whether or not students interact with classmates and other participants in learning platforms in a way that accurately represents their personalities. Data from a sample of students at Abdelmalek Essaadi University of Tetouan were collected from various social media learning environments for the experimental investigation presented in this work, and Symanto AI-based personality tool was used to assess the data. The Big Five Questionnaire was then utilized to assess the personalities of the same students, and the findings were compared to the personality traits discovered by the AI-based approach. The study has shown that the AI based tool has correctly predicted the personality traits of 7 students out of 10 with a correlation of about 0,9 which means that social media-based learning environments can be used by institutions to understand the personality of the student. This paper also gives recommendations about data for obtaining good quality in personality prediction.

Digital image signature is a technique used to protect the image integrity. The application of this technique can serve several areas of imaging applied to smart cities. The objective of this work is to propose two methods to protect digital image integrity. We present a description of two approaches using artificial neural networks (ANN) to digitally sign an image. The first one is "Direct Signature without learning" and the second is "Direct Signature with learning". This paper presents the theory of proposed approaches and an experimental study to test their effectiveness.