This paper investigates the integration of ChatGPT into educational environments, focusing on its potential to enhance personalized learning and the ethical concerns it raises. Through a systematic literature review, interest analysis, and case studies, the research scrutinizes the application of ChatGPT in diverse educational contexts, evaluating its impact on teaching and learning practices. The key findings reveal that ChatGPT can significantly enrich education by offering dynamic, personalized learning experiences and real-time feedback, thereby boosting teaching efficiency and learner engagement. However, the study also highlights significant challenges, such as biases in AI algorithms that may distort educational content and the inability of AI to replicate the emotional and interpersonal dynamics of traditional teacher–student interactions. The paper acknowledges the fast-paced evolution of AI technologies, which may render some findings obsolete, underscoring the need for ongoing research to adapt educational strategies accordingly. This study provides a balanced analysis of the opportunities and challenges of ChatGPT in education, emphasizing ethical considerations and offering strategic insights for the responsible integration of AI technologies. These insights are valuable for educators, policymakers, and researchers involved in the digital transformation of education.

This study presents an analytic model to support the general public in evaluating digital currency exchange platforms. Advances in technologies have offered profitable opportunities, but the general public has difficulty accessing appropriate information on digital currency exchange platforms to facilitate their investments and trading. This study aims to provide a decision support system using analytic models that will guide the public in deciding the appropriate digital currency exchange platform for trading and investment. The overarching objective is to support the public in embracing the new era of a dependable, trustworthy, and sustainable digital society. Particularly, this study offers an analytics model that compares numerous well-known digital currency exchange platforms based on the opinions of 34 human expert members on six main criteria to identify the most suitable platform. In this study, the analytic hierarchy process approach, which is a multiple-criteria decision-making method, and Expert Choice software were used for decision support. Using pairwise comparisons of exchanges with respect to the criteria in the software, the weight of each exchange was determined, and these weights became the basis for prioritizing the exchange platform. This study provides valuable insight into how an analytics-driven expert system can support the public in selecting their digital currency exchange platform. This work is an integral part of an effort to help disruptive digital technology become widely accepted by the general public.

This study aimed to identify the critical factors and items affecting the productivity of sustainable human resources in a Railway Operation Company based on the perceptions of employees and managers in the Human Resources Department. The study was motivated by research which was applied in terms of the objectives of the study and a descriptive survey was employed as the method. The statistical population of the current study consisted of all employees and managers of the Human Resources Department of the company. Random sampling was employed to collect data and the sample size was 191 people according to Morgan’s Table. Methods including the correlation coefficient, multivariate regression, and factor analysis were employed for data analysis. The findings highlight the main factors and items affecting labor productivity in the Urban and Suburban Railway Operation Company as perceived by the Human Resources Department, which were mainly related to human resources management and could be attributed to motivation and requirements for their effective contribution to the improvement of public welfare. Organizational Attitude and Culture, Leadership Style, and Bonus and Ergonomics were extracted as factors affecting productivity or as independent variables. This study is the first study that has aimed to discuss the perceptions of the Human Resources Department active in a company. As such, the study highlights the standpoint of the main decision makers in the Urban and Suburban Railway Operation Company with regard to labour productivity in the urban and suburban sector.

In today’s fast-moving digital economy, service start-ups are reshaping industries; however, they face intense uncertainty, limited resources, and fierce competition. This study introduces an Artificial Intelligence (AI)-powered process modeling framework designed to give these ventures a competitive edge by combining big data analytics, machine learning, and Business Process Model and Notation (BPMN). While past models often overlook the dynamic, human-centered nature of service businesses, this research fills that gap by integrating AI-Driven Ideation, AI-Augmented Content, and AI-Enabled Personalization to fuel innovation, agility, and customer-centricity. Expert insights, gathered through a two-stage fuzzy Delphi method and validated using DEMATEL, reveal how AI can transform start-up processes by offering real-time feedback, predictive risk management, and smart customization. This model does more than optimize operations; it empowers start-ups to thrive in volatile, data-rich environments, improving strategic decision-making and even health and safety governance. By blending cutting-edge AI tools with process innovation, this research contributes a fresh, scalable framework for digital-age entrepreneurship. It opens exciting new pathways for start-up founders, investors, and policymakers looking to harness AI’s full potential in transforming how new ventures operate, compete, and grow.

How can organisations be forensically ready? As organisations are bound to be criticised in the digitally developing world, they must ensure that they are forensically ready. The readiness of digital forensics ensures compliance in an organisation’s legal, regulatory, and operational structure. Several digital forensic investigative methods and duties are based on specific technological designs. The present study is the first to address the core principles of digital forensic studies, namely, reconnaissance, reliability, and relevance. It reassesses the investigative duties and establishes eight separate positions and their obligations in a digital forensics’ investigation. A systematic literature review revealed a gap in the form of a missing comprehensive direction for establishing a digital forensic framework for ethical purposes. Digital forensic readiness refers to the ability of a business to collect and respond to digital evidence related to security incidents at low levels of cost and interruption to existing business operations. This study established a digital forensic framework through a systematic literature review to ensure that organisations are forensically ready to conduct an efficient forensic investigation and to cover ethical aspects. Furthermore, this study conducted a focus group evaluation through focus group discussions to provide insights into the framework. Lastly, a roadmap was provided for integrating the system seamlessly into zero-knowledge data collection technologies.

Evaluating Management Information System (MIS) effectiveness in tertiary education remains conceptually fragmented, with existing models primarily emphasizing technical performance rather than organizational interdependencies. This study develops a structural framework for assessing institutional MIS effectiveness using a sequential mixed-methods design integrating inductive construct development, expert validation, and quantitative dependency modeling. Fifteen themes and fifty-eight subthemes were identified as core dimensions of MIS capability within tertiary education institutions. Dependency and reliance analyses reveal strong systemic interconnections, indicating that institutional MIS functions operate as an integrated organizational system rather than independent domains. The framework is represented as a directed weighted graph to preserve enabling relationships, and a maximum spanning arborescence is derived to provide an interpretable implementation backbone. By modeling structural interdependencies explicitly, the proposed framework supports more informed prioritization of MIS development, strategic alignment across institutional functions, and evidence-based governance decision-making. The framework is intended primarily for evaluating organizational, governance, administrative, and institutional dimensions of MIS effectiveness within tertiary education institutions and does not explicitly assess user-level constructs such as usability, interface design, accessibility, or individual user experience. The study establishes a foundation for future empirical validation and cross-institutional benchmarking.