This study applies a qualitative documentary analysis to examine 22 guidelines, laws, and reports published between 2022 and 2025 by international, European, and national bodies (UNESCO, EU, INTEF, OEI, universities, etc.) on the use of generative artificial intelligence (GenAI) in higher education and research. The overall objective is to conceptualize a corpus of good practices and guide education policy makers. The systematic search in institutional repositories was governed by criteria of authenticity, credibility, representativeness, and meaning. The selected documents were inductively coded following a reflexive thematic analysis, which generated ten key dimensions: regulation and governance, research, ethics, curriculum design, teaching methodology, teacher training, institutional management, assessment, administration, and social impact. The findings show regulatory convergence around the need fortransparency, human oversight, data protection, and risk assessment. Good practices are identified such as public disclosure of GenAI use (name, version, prompts), prior ethical review, and ongoing training of teachers in technical and critical aspects. Likewise, uses of high risk, low risk, and prohibited uses are mapped in accordance with the European AI Regulation, and new teaching roles centered on critical facilitation and co-creation with AI are outlined. The study concludes by proposing actionable regulatory guidelines for governments and institutions: creation of participatory observatories, integration of AI ethics into curricula, and development of open repositories of projects using GenAI. Among the limitations, the rapid obsolescence of regulations and unequal access to non-public documents are acknowledged.
This paper is part of the research carried out in the competitive European project Educational Knowledge Transfer (EKT), whose purpose is the development and experimentation of an educational methodology and an intelligent technological environment (EKT system) to offer an effective, scalable and sustainable support in the process of carrying out initial teacher training internships. The EKT system has been designed to reinforce the collaborative processes between tutors, favor the collaborative monitoring of students in school placement and enhance reflective learning of the profession thanks to the use of digital tools. This publication presents the results of the study focused on the experimentation of the educational methodology and the use of the digital portfolio, the reflective processes during school placement and the relevance of the portfolio to facilitate collaboration between supervisors, tutors and students. A descriptive cross-sectional observational study was carried out a quantitative methodology. The purposive sample of 202 school placements tutors. The need for greater horizontality, collaboration and involvement of the three agents and the absence of institutional tools to support the reflective and collaborative processes were noted. The usefulness of the digital portfolio to promote reflection and collaboration is confirmed.
This study arises from a reflection on the need for teachers at all educational levels to continue their ongoing training throughout their teaching careers. Initial training is a starting point that must be complemented and updated through experience and training, whether informal, formal, or non-formal. The aim is to analyze what an ideal continuing education course would look like, from the perspective of teaching assistants. To this end, the opinions of 454 teachers from different educational levels were collected through a custom-designed questionnaire. Descriptive statistics, using absolute frequencies, relative frequencies, and cumulative relative frequencies, and nonparametric statistics, specifically the Kruskal-Wallis and Dwass-Steel-Critchlow-Fligner tests, were used for pairwise comparisons, accompanied by effect sizes. The results indicate that the ideal continuing education course for teachers would be a distance learning course, approximately one month in length, with the possibility of some voluntary, complementary synchronous sessions and direct email contact with the teaching staff. Additionally, it would include practical activities distributed throughout the course, offering feedback on the activities, as well as various customization options, such as completing assignments individually or in groups. It would also include a forum for teachers to resolve any questions. Apart from that, younger teachers emphasize the importance of courses serving as opportunities to earn points for competitive examinations, and all teachers generally value the fact that courses can be subsidized. The subject matter is the main reason for choosing a course, although the institution organizing it or the individuals who teach it are also important. In short, although training courses have been gradually updated, they still have areas for improvement that could make them more attractive and accessible to all teachers. In this regard, incorporating their perspective is essential. Expanding the sample size of this study would allow for the establishment of specific action plans to implement significant changes in the much-needed ongoing professional development of teachers.
This systematic review aims to analyze the educational interventions used to develop Computational THinking 8CT) in Early Childhood Education (ECE). In recent years, CT has become established as a key competence for the development of cognitive and technological skills, enabling the approach to elements such as decomposition, abstraction, pattern recognition, and algorithms. The review was carried out following the PRISMA protocol, through a search in the Web of Science, Scopus and ERIC databases. The following descriptors were used: "Education" AND "Computational Thinking"; "Educational Technology" AND Computational Thinking"; "Early Childhood Education" AND ("Computational Thinking" OR "Digital Skills" OR "Educational Technology" OR "Programming" OR "Algorithms" OR "Game-Based Learning"). The final sample included 17 studies published between 2020 and 2024. The results show that the interventions applied in ECE generate significant improvements in basic cognitive skills, while more advanced aspects of CT, such as debugging or the use of hardware and software, are addressed less frequently. Moreover, there is a growing interest in the use of robotics as a didactic tool, although the number of studies that use unplugged activities or tangible materials is still limited. In conclusion, the reviewed interventions prove effective for introducing CT at an early age, and future lines of research are proposed focusing on the design of manipulative resources and the assessment of the impact of such activities in real educational contexts. This systematic review explores the interventions used to develop Computational Thinking (CT) in Early Childhood Education (ECE). In response to the growing demand for technological skills in recent years, CT has become a key competency for laying the foundation for skills such as decomposition, abstraction, pattern recognition, and algorithms. ECE is a stage where students can learn to think computationally in order to face the complex challenges of the future. This review was conducted using the PRISMA method, which allows for a rigorous filtering process across the following database: Web of Science, Scopus and ERIC. The results of the interventions show significant improvements in cognitive skills, although more advanced areas, such as debugging or the use of hardware/software, have received less attention. Additionally, a notable number of studies involving robotics in early childhood were found. The review concludes that the interventions analyzed are effective for developing CT in ECE, and future research lines are proposed to explore the design of tangible materials and the role of unplugged activities.
Generative Artificial Intelligence (GAI) tools have become widespread in various educational fields, particularly in higher education. However, further exploration is needed regarding the integration of these models into the academic activities of students in computer science programs. This study aims to conduct a descriptive exploration of the uses of GAI, the fre-quency of use, the output modalities employed (text, image, and video), and students' perceptions of the benefits and op-portunities it offers. Aquantitative and descriptive study was conducted usinga questionnaire administered to a sample of 190 students from public institutions in the State of Mexico. The instrument covered generative tools in their three modali-ties and included items for evaluating perceptions. Subsequently, Spearman's rho correlations were performed to identify potential associations between output modalities and perceived benefits and opportunities. The findings show that text-based generative models are widely used for information retrieval, code explanation and generation, and error resolution, contrasting with the other models, which are used less frequently. Regarding perceptions of benefits and opportunities, students expressed an average rating for problem-solving and task automation, and a lower rating for skills development and collaboration. Furthermore, relevant correlations were identified with the opportunity for collaboration: while the as-sociation with its use for information retrieval was negative (rho =-0.313), it was positive with diagram creation (rho = 0.219). It is concluded that, although Genetic AI has been integrated into academic activities, it is essential to promote collaborative work in its educational practice, as well as ethics and responsibility, to avoid dependence on these tools.
