In recent years, higher education (HE) globally has witnessed extensive adoption of technology, particularly in teaching and research. The emergence of generative Artificial Intelligence (GenAI) further accelerates this trend. However, the increasing sophistication of GenAI tools has raised concerns about their potential to automate teaching and research processes. Despite widespread research on GenAI in various fields, there is a lack of multicultural perspectives on its impact and concerns in HE. This study addresses this gap by examining the usage, benefits, and concerns of GenAI in higher education from a multicultural standpoint. We employed an online survey that collected responses from 1217 participants across 76 countries, encompassing a broad range of gender categories, academic disciplines, geographical locations, and cultural orientations. Our findings revealed a high level of awareness and familiarity with GenAI tools among respondents. A significant portion had prior experience and expressed the intention to continue using these tools, primarily for information retrieval and text paraphrasing. The study emphasizes the importance of GenAI integration in higher education, highlighting both its potential benefits and concerns. Notably, there is a strong correlation between cultural dimensions and respondents’ views on the benefits and concerns related to GenAI, including its potential as academic dishonesty and the need for ethical guidelines. We, therefore, argued that responsible use of GenAI tools can enhance learning processes, but addressing concerns may require robust policies that are responsive to cultural expectations. We discussed the findings and offered recommendations for researchers, educators, and policymakers, aiming to promote the ethical and effective integration of GenAI tools in higher education.

Given that computational thinking (CT) is still a blurry psychological construct, its assessment remains as a thorny, unresolved issue. Hence, in recent years, several assessment tools have been developed from different approaches and operational definitions of CT. However, very little research has been conducted to study whether these instruments provide convergent measurements, and how to combine them properly in educational settings. In response, we first review a myriad of CT assessment tools and classify them according to their evaluative approach. Second, we report the results of two convergent validity studies that involve three of these CT assessment tools, which come from different perspectives: the Computational Thinking Test, the Bebras Tasks, and Dr. Scratch. Finally, we propose a comprehensive model to evaluate the development of CT within educational scenarios and interventions, which includes the aforementioned and other reviewed assessment tools. Our comprehensive model intends to assess CT along every cognitive level of Bloom’s taxonomy and throughout the various stages of typical educational interventions. Furthermore, the model explicitly indicates how to harmoniously combine the different types of CT assessment tools in order to give answer to the most common research questions in the field of CT Education. Thus, this contribution may lead scholars and policy-makers to perform accurate evaluation designs of CT according to their inquiry goals.

El uso de sistemas de inteligencia artificial en múltiples niveles de la sociedad ofrece nuevas y prósperas oportunidades, pero también introduce nuevos riesgos y cuestiones éticas que deben abordarse. Sostenemos que la introducción de contenidos de inteligencia artificial en las escuelas a través de proyectos prácticos es el camino a seguir para educar ciudadanos conscientes y críticos, para despertar vocaciones entre los jóvenes, y para fomentar las habilidades de pensamiento computacional de los estudiantes. Sin embargo, la mayoría de las plataformas educativas de programación existentes carecen de algunas características necesarias para desarrollar proyectos completos de IA y, en consecuencia, se requieren nuevas herramientas. En este artículo presentamos LearningML, una nueva plataforma dirigida al aprendizaje automático supervisado, una de las técnicas de IA más exitosas que se encuentra en la base de casi todas las aplicaciones actuales de IA. Este trabajo describe las principales funcionalidades de la herramienta y discute algunas decisiones tomadas durante su diseño, para el que hemos tenido en cuenta las lecciones aprendidas al revisar trabajos anteriores realizados para introducir la IA en la escuela y el análisis de otras soluciones que permiten proyectos prácticos de IA. También se presentan los próximos pasos en el desarrollo de LearningML, que se centran en la validación, tanto aparente como instruccional, de la herramienta. The use of artificial intelligence systems in multiple levels of society offers new and thriving opportunities, but also introduces new risks and ethical issues that should be dealt with. We argue that the introduction of artificial intelligence contents at schools through practical, hands-on, projects is the way to go in order to educate conscientious and critical citizens, to awaken vocations among youth people, as well as to foster students’ computational thinking skills. However, most existing programming platforms for education lack some required features to develop complete AI projects and, consequently, new tools are required. In this paper we present LearningML, a new platform aimed at learning supervised Machine Learning, one of the most successful AI techniques that is in the basis of almost every current AI application. This work describes the main functionalities of the tool and discusses some decisions taken during its design, for which we took into account the lessons learned while reviewing previous works carried out for introducing AI in school and from the analysis of other solutions that enable practical AI projects. The next steps in the development of LearningML are also presented, which are focused on both the face and instructional validation of the tool.