Motivation is an essential element in the learning process. In computer-based tutoring environments, motivational components are as significant as cognitive ones. Previous work established that automatic affective feedback improves student motivation when he/she uses Tutoring Systems. Also, prior work examines the relationship between student’s motivation to learn and personality traits, but only from a partial point of view. The present study analyzes whole personality traits on motivation to learn by students using a Tutoring System. The work involved 30 undergraduate students in a qualitative experiment. The authors examined the results using Chi² to determine the relationship between motivation to use the system and personality; a Naïve Bayes classifier was applied. The findings suggested that Neuroticism is a factor that influences student’s motivation to use the tutoring system. Also, the Naïve Bayes algorithm reaches an accuracy of 90% for the classification.

In Mexico, the community of people with hearing disabilities faces significant communication barriers due to the limited availability of interpreters and the lack of supportive technological tools. This paper presents the development of a sign language system using artificial intelligence (SLSAI), a mobile application that uses artificial intelligence (AI) for the recognition of Mexican sign language (MSL). Using machine learning techniques and image processing, SLSAI translates signs from the alphabet in real-time, facilitating interaction between deaf and hearing individuals. Additionally, the tool features a module where students can learn sign language through interactive games, creating a fun and engaging learning environment. The development methods, obtained results, and social implications of the tool are discussed.

In recent years, the integration of machine learning (ML) techniques into school management systems offers several opportunities to enhance efficiency and decision-making in the educational field. Applying ML in education can yield significant benefits. However, it is important to note that the successful integration of ML techniques into school management systems requires a robust data infrastructure, proper data collection, and consideration of ethical and privacy issues. Furthermore, ML should not replace human interaction in education but rather complement and improve it by providing educators and students with additional tools for educational success. Due to the increased complexity in the curriculum of the Computer Systems Engineering program, it is necessary to carry out a more reliable and automated prediction of semester schedules. To address the manual scheduling generation problem, a comprehensive analysis of the existing process was conducted. This involved gathering relevant information on how semester schedules are currently generated in the educational institution. The current approach used for scheduling was studied, along with an analysis of the problems and limitations associated with the manual process. Various ML techniques that could be applied to the scheduling generation problem were investigated. This could include optimization algorithms, clustering or classification algorithms, genetic algorithms, or other machine learning approaches that can be adapted to the specific problem.