Generative artificial intelligence (GenAI) is transforming various sectors, including education. This study investigates the integration of GenAI in higher education, focusing on its potential to enhance teaching and learning. Through a series of workshops and courses delivered to university professors, it examines opportunities such as improved resource creation and challenges like ethical AI usage, proposing best practices for the sustainable implementation of GenAI in the classroom. The main objective is to analyze how the use of GenAI tools such as ChatGPT, Gemini, and Claude can improve teachers’ professional skills and the overall educational experience while ensuring ethical and responsible use. The methodology comprised a literature review and practical experimentation with university professors. Data collection involved observations, surveys, discussion forums, cooperative activities, and exercises focused on evaluating AI-generated educational resources and analyzing forum insights to identify best practices. The results highlight several opportunities around the use of GenAI in education, including improving writing, creating educational resources, supporting lesson planning, and increasing teacher productivity. In addition, significant challenges were identified, such as the ethical use of AI and strategies for detecting AI-generated text. For instance, workshops demonstrated a 30% increase in teacher confidence with GenAI tools like ChatGPT, highlighting the effectiveness of these technologies in professional development. To address these challenges, best practices for the responsible integration of GenAI in education are presented, focusing primarily on ongoing training, the establishment of institutional policies, the encouragement of responsible use, and the ongoing evaluation of impact in the educational setting. Best practices include clear ethical guidelines, prompt development techniques, and continuous professional training to ensure teachers can effectively and responsibly integrate GenAI tools into their instructional practices. These practices for the effective use of GenAI tools in education aim to maximize benefits while mitigating risks. These include the development of effective prompts for various activities and guidance on the ethical use of AI to ensure a balanced and responsible approach to the integration of GenAI in higher education.

This paper presents three different approaches to recognize driving style based on a hierarchical-model. Specifically, it proposes a hierarchical model for the recognition of the driving style for advanced driver-assistance systems (ADAS) for vehicles. This hierarchical model for the recognition of the style of the car driving considers three aspects: the driver emotions, the driver state, and finally, the driving style itself. In this way, the proposed hierarchical pattern is composed of three levels of descriptors/features, one to recognize the emotional states, another to recognize the driver state, and the last one to recognize the driving style. Each level has a set of descriptors, which can be sensed in a real context. Finally, the paper presents three driving style recognition algorithms based on different paradigms. One is based on fuzzy logic, another is based on chronicles (a temporal logic paradigm), and the last is based on an algorithm that uses the idea of the recognition process of the neocortex, called Ar2p (Algoritmo Recursivo de Reconocimiento de Patrones, for its acronym in Spanish). In the paper, these approaches are compared using real datasets, using different metrics of interest in the context of the Internet of the Things, in order to determine their capabilities of reasoning, adaptation, and the communication of information. In general, the initial results are encouraging, specifically in the cases of chronicles and Ar2p, which give the best results.

Purpose: This research work aims to arise from developing new communication channels for customer service in micro, small and medium enterprises (MSMEs), such as chatbots. In particular, the results of the usability testing of three chatbots implemented in MSMEs are presented. Design/methodology/approach: The methodology employed includes participants, chatbot development platform, research methodology, software development methodology and usability test to contextualize the study's results. Findings: Based on the results obtained from the System Usability Scale (SUS) and considering the accuracy of the chatbot's responses, it is concluded that the level of satisfaction in using chatbots is high; therefore, if the chatbot is well integrated with the communication systems/channels of the MSMEs, the client receives an excellent, fast and efficient service. Originality/value: The paper analyzes chatbots for customer service and presents the usability testing results of three chatbots implemented in MSMEs.

In this article, we propose the concept of “Autonomic Cycle Of Learning Analysis Tasks” (ACOLAT), which defines a set of tasks of learning analysis, whose objective is to improve the learning process. The data analysis has become a fundamental area for the knowledge discovery from data extracted from different sources. In the autonomic cycle, each learning analysis task interacts with each other and has different roles: Some of them must observe the learning process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the learning process. In this article, we study the application of the autonomic cycle in a smart classroom, which is composed of a set of intelligent components of hardware (e.g., smart board) and software (e.g., virtual learning environments), which must exploit the knowledge generated by the ACOLAT to improve the learning process in the smart classroom. Moreover, we present the set of ACOLATs present in a smart classroom and the implementation of some of them.