Since the beginning of the 21st century, major innovations and important technological advances have led to a socio-economic paradigm shift characterized by robotics, Artificial Intelligence, Big Data, as well as the use of Social Networks in companies, creating the industrial revolution 5.0, based on digital transformation. The aim of this research focuses on analyzing the implementation of digitalization in European companies, through socio-economic variables, delving into SDG4 -Quality Education- and SDG5 -Gender Equality and Women’s Empowerment-. A quantitative multivariate methodology, cluster analysis, was used for them. Due to the novel implementation of most of these technologies, and the recent measurement of the data, the longitudinal analysis is carried out from the first year with contrasted statistical data, from 2017 to 2022. The results show that the speed of adoption of new technologies is uneven across the European Union, with countries such as Sweden, Finland, Denmark and the Netherlands with Very High Digitalization, compared to Romania and Bulgaria with Very Low Digitalization. Likewise, the countries that make up the cluster of countries with the highest digitisation rates also coincide with the countries with the most equal employment conditions and the lowest ratio of working women at risk of poverty, although the gender gap is still evident in all the countries analyzed. The Women Work Risk Poverty variable has decreased the least between 2017 and 2022.

Digital technology is central to economic and social development, but it also raises social issues related to inequality and exclusion. Studies show that, without specific policies, technology can reproduce structural gaps in the labor market. In the case of women, data reveal persistent gaps in access to digital training and participation in highly skilled technology jobs. In this context, reducing the gender gap emerges as a necessary condition for digitalization to contribute to the SDG of the 2030 Agenda. This research aims to analyze the implementation of SDGs 4 (Quality Education) and 8 (Decent Work and Economic Growth) in the context of digital transformation, with an emphasis on their impact on gender equality (SDG 5) with regard to access to quality education and decent employment in Europe. Analyses Eurostat data between 2017 and 2023 using Jamovi software, a hierarchical multiple linear regression was applied. The results show that digitization emerges as a key factor in explaining the persistence of the gender gap in the 27 countries of the European Union, conditioned by the economic and social characteristics of each country. In conclusion, the advancement of digitization and technology in the European Union is conditioned by structural factors such as education, job stability, and gender equality in leadership, rather than by the mere availability of ICT specialists. These findings highlight the need for differentiated public policies that not only boost competitiveness but also address the social problems arising from the digital and gender divides, thus ensuring a more inclusive technological transition.

El artículo contrasta las calificaciones otorgadas por docentes universitarios con las generadas por inteligencia artificial al evaluar 70 resoluciones de diez casos prácticos, con el fin de conocer el grado de concordancia y valorar la viabilidad de la IA como herramienta de apoyo en la evaluación académica. Para este estudio se analizó una matriz profesor-IA mediante Python 3.12. Se calcularon MAE, correlaciones de Pearson y Spearman, el índice de concordancia de Lin y el coeficiente kappa. Además, se elaboraron boxplots y gráficos de Bland-Altman para detectar sesgos y dispersión. Las notas de ChatGPT mostraron correlaciones moderadamente altas con las humanas (r 0,72) y un MAE de 1,12 puntos en una escala de 0-10. El índice de Lin (0,67) indica concordancia moderada, aunque la IA tiende a ser 0,9 puntos más benévola. El acuerdo categórico (kappa = 0,46) es aceptable, con discrepancias localizadas en un grupo y en una unidad didáctica. En conjunto, ChatGPT-4o reproduce la pauta docente con suficiente precisión para ofrecer retroalimentación rápida y homogénea, pero requiere supervisión y ajustes de prompting para minimizar sesgos sistemáticos. De este modo, se aprovecha la eficiencia de la IA; pero sin renunciar al juicio profesional del docente, responsable último de la evaluación.