This scientific article examines the transformative potential of Agentic AI in Science, Technology, Engineering, and Mathematics (STEM) education. It highlights how Agentic AI can enhance learning outcomes in these subjects, reduce cognitive load, and better prepare students for the demands of an AI-driven workforce. By utilising smart tools like GitHub Copilot, Agentic AI systems provide opportunities to improve STEM learning environments through personalised assistance, collaborative problem-solving, and skill development, all while automating repetitive tasks. This paper also discusses the future implications of the job market influenced by Agentic AI, emphasising the need for upskilling and the necessity for educational systems to adapt to emerging roles. It also touches on ethical aspects like fair access and AI literacy while highlighting how Agentic AI could help bridge the gap between traditional education and real-world job readiness.

This paper is focused on Ai Social Enterprise Opportunities in Romanian Ecosystem, on exploring the emerging grounds of Digital Innovation Management. Clarity is needed in order to pursue goal-oriented initiatives, our research strategy is focused on maximizing the current strengths and adapting them to emerging needs in an AI opportunity perspective.

This study examines the role of artificial intelligence (AI) in accounting and auditing through a two-stage design that combines bibliometric analysis with an exploratory comparative evaluation of AI-based solutions. The current literature shows a fragmentation between conceptual reviews and vendor-driven case descriptions, while a structured cross-cutting view of how AI technologies are actually integrated into financial workflows, and of how this integration differs by entity size and by the trustworthiness profile of the underlying systems, is still limited. The bibliometric component examines 729 peer-reviewed articles indexed in the Web of Science database (post-2015), processed using VOSviewer for keyword co-occurrence analysis. The exploratory component evaluates ten AI-based solutions against a framework that covers AI subset, functional category, target entity size, audit relevance, and disclosed architectural transparency. The bibliometric results indicate that the field is organised around six dominant terms (artificial intelligence, deep learning, machine learning, performance, classification, and model), reflecting a strong methodological convergence on supervised and deep-learning approaches. The exploratory results show that current AI offerings cluster into three functional categories (process automation, analytics and business intelligence, and predictive or audit-oriented systems), with adoption patterns that differ by company size: small and medium-sized entities (SMEs) gain the most benefits from process automation and optical character recognition, while large entities derive higher value from full-population analytics and ensemble-based anomaly detection. The study contributes a replicable methodological pipeline that links bibliometric mapping with applied tool evaluation, a comparative framework that addresses the often undisclosed AI architecture behind vendor black-box tools, and a discussion of trustworthiness limits, including the constitutive (non-error) nature of probabilistic AI failures, with implications for audit assurance and emerging regulatory frameworks such as the EU AI Act and ISO/IEC 42001.

The aim of this article is to present monitoring system management using the AGILE SCRUM Methodology and microprocessors based systems for temperature monitorization of a grain reception base for organic wheat. In this article is presented the well-detailed proof of concept for a web-based temperature monitoring system, using Arduino and temperature sensors for temperature monitorization and AGILE SCRUM Methodology for software development management. In this way, the operators from grain reception base will be able to monitor the temperature from different halls, build from different materials, remotely.

The FinTech sector has evolved into an ecosystem defined by fast technological ad-vancements, escalating competition, and dynamic regulatory frameworks. In this volatile environment, project management methodologies are crucial not only to keep pace with technological innovation but also to anticipate fluctuating user expectations while com-plying with regulatory demands. This article highlights advanced project management frameworks adapted to FinTech software development, examining their guiding principles, operational benefits and challenges. Special emphasis is placed on integrating Agentic AI systems. This emergent class of artificial intelligence is capable of autonomous decision-making within required solution frameworks. The interplay of such innovations with tools like GitHub Copilot is explored, illustrating the scope for enhanced productivity, user-centric iterations, and anticipatory compliance. The article provides a foresight into agentic systems, hyper-personalized User Experience (UX), and evolving compliance landscapes, offering a panoramic view essential for FinTech's next evolutionary step.