Within the past five years, and as Artificial Intelligence (AI) increasingly pervades the academic and educational landscape, a delicate balance has emerged between leveraging AI’s transformative potential and safeguarding individual privacy, which needs to be carefully maintained. The preservation of user privacy entails severe financial risks via penalties for the violation of directives such as General Data Protection Regulation (GDPR). This manuscript examines three neoteric approaches to data privacy protection in AI-empowered lifelong education. The first method uses Triple-Entry Accounting (TEA) together with Distributed Ledger Technology (DLT); the second method uses a transaction Merkle tree that can be used as a “proof of existence” so that the users can safeguard their personal information; and the third approach examines the advantages and disadvantages of an offline AI-tutor multimodal model that can operate without internet access. Finally, the ethical implications of deploying such technologies are critically discussed, emphasizing the necessity of achieving privacy while retaining the human factor in education.

It is a popular hypothesis for researchers worldwide that if we manage to construct a lifelike intelligence that depicts most aspects of the human brain, it will be easier for us to understand our own existence. This discussion often ends up in polemic altercations between philosophers, neuroscientists, and technologists on the definition of intelligence. It has also been a subject of interest in both academic and industrial societies, with two prominent concepts emanating at the peak of it, often as one and the same: Artificial Intelligence and Natural Intelligence. While these terms are often used interchangeably, we theorize that they represent two totally distinct and often contradictory constructs. This work aims to portray the most significant divergences between Artificial Intelligence and Natural Intelligence and find out if those can converge under the current technological advancements. We focus primarily on their accurate definitions, then their inner workings, and their potentials and limitations enumerating in the process relative sociological and ethical consequences. Finally, we show why under the current methods the probability of creating an advanced form of Artificial Intelligence is minimal.

Robotic Process Automation (RPA) is a software-based technology that uses configurable algorithmic software agents (bots) to replicate manual user activities across digital systems. It represents an evolution from earlier workflow scripting tools, and is distinguished by its ability to be used without requiring substantial IT infrastructure modifications or extensive programming knowledge. In the banking and insurance sectors, organizations face increasing pressure to adopt modern technologies that streamline operations and reduce costs while complying with strict regulatory requirements. Robotic Process Automation (RPA) has emerged as a viable and cost-effective solution, enabling automation of repetitive and rule-based tasks without requiring major changes to legacy IT systems. This paper conducts a literature review to examine the current use cases of RPA technologies in banking and insurance, analyzing how these technologies are employed to enhance corporate efficiency and performance. The review draws from recent academic publications and case studies between 2017 and 2025, identifying core implementation areas such as customer onboarding, claims processing, compliance reporting, and underwriting automation. The results highlight substantial improvements in processing speed, error reduction, and resource optimization, along with evolving metrics for measuring effectiveness. The study concludes by identifying key success factors, performance measurement approaches, and challenges in RPA implementation, offering insights for both practitioners and researchers aiming to understand the role of automation in financial services transformation.

The 4th Industrial Revolution, or 2nd digital revolution, has brought about drastic changes in the economy and society, as technology develops at a rapid pace. Digital humanities take advantage of this technological evolution to uncover new ways to approach the humanities and the social sciences. In particular, the 4IR has enabled the development of new technologies that are facilitating the integration of virtual and physical environments in different digital humanities sectors, such as arts and culture. In addition, it helped digital humanities scholars to analyse massive amounts of data and apply machine learning algorithms to identify patterns, uncover new insights and approach cultural, historical, and literary phenomena in a more comprehensive and differentiated way. In the context of 4IR, the Semantic Web plays a crucial role in enabling machines and humans to access and use the vast amount of data generated by advanced technologies, by providing a common language and structure for data exchange and integration.

The rapid expansion of distance and hybrid education has significantly increased the strategic role of Learning Management Systems (LMS) in supporting teaching, learning, and institutional digital transformation. This study presents a systematic literature review that examines recent technological innovations in LMS and their impact on learning outcomes, user adoption, and organizational readiness. The research addresses a timely question: how do emerging technologies and organizational factors impact LMS adoption and educational effectiveness? The review followed a structured literature review methodology based on predefined search criteria, inclusion and exclusion criteria, and thematic synthesis of studies published between 2020 and 2025. Publications were identified through major academic databases and analyzed to identify technological trends, organizational factors, and their influence on LMS adoption and educational effectiveness. Additionally, factors like institutional support, perceived usefulness, and user training were examined through the lens of the Extended Technology Acceptance Model (ETAM). The findings show that while new technologies enhance personalization and student engagement, their success depends heavily on organizational readiness and user perception. Perceived usefulness, quality of training, and strategic implementation emerged as critical drivers of LMS adoption. The study also identifies current gaps in empirical research and offers recommendations for future investigation. These insights can benefit educational researchers, policymakers, and technology developers alike. By aligning technological innovation with pedagogical and organizational strategies, LMS platforms can become more effective tools for supporting quality distance education.