Artificial Intelligence (AI) is rapidly transforming the educational landscape, offering unprecedented opportunities to personalize learning, optimize administrative processes, and enhance student outcomes. However, alongside its benefits, the integration of AI into education presents significant ethical challenges that require structured and responsible approaches to ensure its effective and equitable use. This paper introduces the INTEGRITY AI Model, a structured and progressive framework designed to guide the ethical adoption of AI in educational settings. The foundation of the INTEGRITY AI Model is an initial diagnostic assessment that identified critical gaps in teachers' understanding of academic integrity, the subtle forms of misconduct facilitated by AI, and the need for continuous professional development in technological literacy. Based on these insights, the model addresses three key levels: Guiding Principles, which provide ethical guidelines and best practices to establish a shared foundation for teachers and students; Training Actions, which focus on capacity-building through specialized courses, workshops, and awareness initiatives to enhance ethical AI integration; and Impact and Empowerment, which prioritizes the dissemination of resources, the promotion of accessibility, and the creation of spaces for reflection and dialogue to foster a culture of academic integrity. This study underscores the importance of aligning the adoption of AI technologies with robust ethical frameworks, addressing both pedagogical and institutional dimensions. By incorporating actionable strategies and a scalable structure, the INTEGRITY AI Model serves as a replicable resource for educational institutions seeking to navigate the complexities of AI integration responsibly. The model's implementation highlights its relevance and effectiveness, as evidenced by the positive reception of its guides, workshops, and online resources. The results of this research affirm the value of structured ethical approaches in enhancing the responsible use of AI, promoting awareness, and equipping educators and students with tools to address ethical challenges. By fostering a reflective academic culture, the INTEGRITY AI Model maximizes the potential of AI technologies while ensuring their ethical and equitable application in education. This framework provides a path forward for institutions aiming to embrace AI's transformative potential responsibly and sustainably.

This work-in-progress paper explores the integration of Digital Credentials in the context of engineering and STEAM education, offering insights into their evolving role within the future educational landscape. In a brief literature overview, current trends and legislative considerations relevant to Digital Credentials are highlighted, emphasizing their anticipated significance in shaping the educational environment. Digital Credentials, a manifestation of individuals' skills, achievements, and qualifications in electronic format, serve as verifiable and portable representations of educational accomplishments. Unlike traditional paper-based certificates, Digital Credentials harness the power of technology to encapsulate a comprehensive range of information, including specific skills, competencies, and the context in which they were acquired. The focal point of this study is the practical experience of incorporating Digital Credentials into a Virtual Learning Environment. In a first experience 1496 digital diplomas were generated to certify four STEAM courses. Findings from the initial implementation within diverse engineering-focused continuous education programs are presented, shedding light on participant responses and the potential influence of Digital Credentials on motivation and engagement. This practical insight contributes to the ongoing discourse on innovative educational practices, showcasing the transformative potential of Digital Credentials. Looking ahead, the paper advocates for the establishment of a common space or observatory dedicated to Digital Credentials in education. This call is rooted in the recognition of the need for ongoing dialogue and collaboration among educators, researchers, and policymakers to collectively navigate the evolving landscape of Digital Credentials and harness their potential in shaping the future of education.

The integration of Artificial Intelligence (AI) into the field of education is an unprecedented trend with the potential to revolutionize teaching approaches and significantly improve the overall learning experience. This workshop offers an opportunity for a strategic implementation of generative artificial intelligence in higher education, demonstrating its capacity to substantially enhance the creation and customization of digital educational materials. It is essential for educators to possess the capacity to utilize generative artificial intelligence tools, specifically when it comes to developing prompts for Large Language Models (LLMs). In addition to fostering a more interactive learning environment, these LLMs are driving the transition to educational systems that are more autonomous and adaptable. An in-depth exploration of the pragmatic and ethical aspects of generative AI implementation is undertaken to equip educators with the necessary knowledge and skills to employ AI in a responsible manner, thereby cultivating an engaging and equitable learning environment. This workshop was prepared based on successful previous experiences in different conferences and meetings in Spain, Portugal, Germany, México, Guatemala, Colombia, and USA.

A remote laboratory is a software and hardware tool that enables students to access real equipment located somewhere else through the Internet. The access to the equipment is just the first part, for a correct implementation, the interaction, feedback and troubleshooting is vital. The laboratories are typically deployed in universities or research centers. A key factor of remote laboratories is that once they are available through the Internet their usage can be scaled up and naturally used by students. The installation and maintenance of multifunctional remote laboratories can represent high costs, that is the reason why some institutions choose to rent the equipment for short periods of time. Thus, two or more institutions can share different equipment to reduce costs. Furthermore, this enables a sharing economy where multiple institutions provide access to their laboratories to each other, in different economic models (freely or with a cost). However, the demand identified in times of pandemic has required great efforts on the part of engineering faculties to continue training their students. The COVID-19 pandemic suddenly paralyzed universities all around the world. During the COVID- 19 lockdown, universities switched to distance teaching and canceled face-to-face education affecting the way of teaching. IT staff was urged to find alternatives to train teachers in designing diverse learning activities to meet the learning objectives. This work presents the experience of implementing a group of remote laboratories in courses related to Electronics and Electric subjects at various levels of engineering. By incorporating the principles of Kolb's Experiential Learning Cycle, this work investigates key factors and pedagogical considerations for the design of effective remote laboratory activities. The findings highlight the importance of providing students with hands-on, experiential learning opportunities, even in remote settings, to enhance their understanding and application of theoretical concepts in engineering education. To date, the institution represented by the authors' research group has implemented over 44 courses with learning activities utilizing pilot remote laboratories, benefiting more than 375 students. This paper presents the main lessons learned from these experiences.

The irruption of Artificial Intelligence (AI) based chatbot tools is undoubtedly at the frontiers of education. AI chatbots in education has emerged as a promising solution to enhance the quality of education and to improve learning outcomes. As all new technology does, it has begun to generate news about prohibition, ethical aspects, anti-plagiarism detection tools, and a series of policies from different educational systems. However, we should not deny the positive aspects of these tools if they are well used. AI-based chatbots have interesting potential to help both teachers and students, who must learn to use them well for their own benefit. This article provides an overview of AI-based chatbots, particularly ChatGPT, an artificial intelligence language model developed by OpenAI. GPT, or “Generative Pre-Training Transformer” is a neural network trained to generate “human-like text” by predicting the next word in a sequence given a large dataset of examples. ChatGPT uses the neural network model and is used to generate responses to students' questions in real time, in the sense of a personal teacher assistant. Chatbots are designed to be able to carryon a natural conversation by understanding the context of the conversation, generating appropriate responses, and engaging in active interaction. Nowadays, this type of tool can generate more than just text; for example, the use of LaTeX code (using TeXGPT) or tools for coding and debugging programming exercises. This work explores the potential role that AI-based chatbots can have in engineering education, by examining the answers of a group of teachers' about how the use of AI-based chatbots can improve the learning process of the students in engineering education. The questions that teachers had to answer, from a pedagogical and technological perspective, are related to how chatbots can be integrated into the curriculum to enhance the efficiency of engineering education, their potential impact on the learning process, and actual examples of possible learning activities using AI-based chatbots in their courses.

This paper presents the Telescope project, initiated and hosted at Galileo University in Guatemala, which is an initiative for Latin American Region with the similar objective as Coursera or EdX. First it is presented and analyzes the current state of the MOOCs, showing real progress, the broader scope in the academic field and it's potential as a tool to support education. For Telescope, behavior of students, completion rates and drop-out will be detailed and analyzed, along with assessment approaches and communication methods from four different MOOC experiences given at The MOOCs 2013 experiences presented are from the following courses: Cloud based tools for education, Introduction to e-Learning, Medical Urgencies and Android development. With over 15,000 enrolled students, and learners from over 15 countries. Also, we will present result from drop out learners, from no shows to those who did not complete the whole course experience, presenting the main reasons for drop out: personal reasons, academic and other influencing factors such as course quality and interaction with the tutors, course contents, workload and time management; being the most cited reasons for dropout: personal and workload reasons such as not enough time, increase workload at the job, and also the academic workload was evaluated as too-high in some cases.