Education is another field which generative artificial intelligence has made its way into, intervening in students’ learning processes. This article explores students’ perspectives on the use of generative AI tools, specifically ChatGPT-3.5 (free version) and ChatGPT-4 (with a subscription). The results of the survey revealed a correlation between the use of ChatGPT and the perception of grade improvement by students. In addition, this article proposes an architecture for an adaptive learning system based on generative artificial intelligence (AI). To develop the architectural proposal, we incorporated the results of the student survey along with insights gained from analyzing the architectures of other learning platforms. The proposed architecture is based on a study of adaptive learning platforms with classically virtual assistants. The main question from which the current research started was how artificial intelligence can be integrated into a learning system to improve student outcomes based on their experience with generative AI. This has been sectioned into two more specific questions: 1. How do students perceive the use of generative artificial intelligence tools, such as ChatGPT, in enhancing their learning journey? 2. Is it possible to integrate generative AI into a learning system used in education? Consequently, this article concludes with a proposed architecture for a learning platform incorporating generative artificial intelligence technologies. This article aims to present a way to understand how generative AI technologies support education and contribute to improving academic performance.

This article introduces sequential strategies regarding the way to use artificial intelligence as a learning facilitator for both teachers and students while preserving ethics and the legislative norms launched. Learning solutions are proposed to be used both by students, to study at school, but also at home, and by teachers to meet all the needs of students. The intelligent systems that are in general proposed to be part of the educational process can be designed so that the norms of ethics and responsibility are taken into account and as many actors involved in the educational environment, whether they are teachers, students, people who run educational institutions or decide to country level to be as it embraces with a much higher level of confidence artificial intelligence learning systems. For this work, a qualitative analysis was carried out to answer the question of how learning systems implemented with artificial intelligence should be built so that ethical principles are not neglected. While ensuring a sustainable environment. Specifically, a review of academic articles was conducted to highlight the concerns and importance of artificial intelligence in education. An analysis was also made of documents produced by specialised institutions such as the Organisation for Economic Co-operation and Development (OECD), United Nations Educational, Scientific and Cultural Organisation (UNESCO), U.S. Department of Education, World Economic Forum, European Commission, European Parliament, Council of the European Union, and The Institute for Ethical AI in Education on the topic of ethics in artificial intelligence and and the guidelines outlined for building ethical systems and including sustainability. In the conclusion section of the article, admissions are made that there is necessary support from the institutions, so it is still possible to make educational systems with artificial intelligence that respect ethical norms and pay attention to sustainability.

Purpose – This article focusses on the impact generated on GDP growth by the number of graduates in the ICT domain of Romanian counties in the period 2014–2020. Research methodology – We have collected the number of ICT graduates, the GDP per inhabitant in EUR per Romanian county, the number of ICT employees, the number of information service activities employees from the National Institute of Statistics (Romania) and Eurostat. Additionally, we consolidate the correlation matrix and regression analysis for previous statistics indicators. Findings – We have revealed a strong correlation (85,77%) between the number of ICT graduates and the GDP per inhabitant in EUR per Romanian county, and very strong correlations between the number of ICT graduates and the number of ICT employees (94,80%) and the number of employees of information service activities (97,38%). Research limitations – The data set from the National Institute of Statistics (Romania) does not contain entries for every county, due to its statistical methodology, and other socioeconomic phenomena which contribute to the development of the ICT field were just enumerated, such as natality, immigration, and emigration. Practical implications – The study revealed that investment in ICT education (number of graduates) contributes to the local prosperity of communities, counties, and, in general, at the national level as GDP growth. Romanian government can utilize the outcomes of this paper to engage additional resources, funds from Ministry of Education, to develop a long-lived learning culture of ICT skills for the entire population. Originality/Value – The article methodologically highlights the most important ICT hubs in Romania, which are correlated with the existence of an ICT educational infrastructure (high schools and universities) and the measure of impact on GDP growth for the number of highly skilled graduates in the ICT domain. The selected national and European indicators contributed to the novelty of this study.

This article examines the medium to long-term enrollment patterns of an AI-based platform designed to support children in learning robotics and participating in a national robotics competition in Romania. Drawing on registration and participation data covering students and teachers across urban and rural schools between 2020 and 2025, the study documents a consistent pattern: an initial period of high enrollment and rapid adoption followed by a steady decline over time. A key feature of the initiative is that hardware, platform access, and learning resources were provided entirely free of charge, allowing cost-related explanations for the decline to be set aside and structural and human factors to be examined directly. The paper makes two primary contributions. First, it proposes a System Dynamics framework grounded in innovation diffusion theory as a first-generation calibration model for understanding AI-enabled educational robotics adoption in a resource-constrained national context. The model is designed to be progressively tested and refined as anonymized aggregate data accumulates, and it relies exclusively on anonymized aggregated public data in accordance with GDPR requirements. Second, it advances the hypothesis that an AI-based educational platform, even one from which all financial barriers have been removed, will experience sustained enrollment decline in the absence of adequate human teacher involvement. The empirical trajectory and model outputs are consistent with this hypothesis and motivate further investigation. This represents a hypothesis-generating and framework-building paper. The framework reveals pronounced urban-rural disparities and differential outcomes by age of entry. All findings are presented as model-generated hypotheses rather than empirically demonstrated conclusions. The paper invites researchers gathering comparable data from similar initiatives in other countries to collaborate in testing and refining the model. The central conclusion is cautiously optimistic: AI may support robotics education adoption, but it is not a substitute for dedicated teachers, and without sustained investment in human capital, even a financially accessible platform is insufficient to maintain long-term enrollments.