Articles

Exemplary Model of AI-Supported Adaptive Optimization Energy Flow Control in Smart City Microgrids: A Simulation-Based Scenarios

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The paper focuses on the possibilities for developing a model for adaptive control of electricity flows in urban microgrids using AI support into the Internet of Things networks.  The goal is the requirement for smarter, more adaptive and sustainable methods in controlling local energy systems. This is critical for distributed generation and the growing incorporation of renewable energy resources. The study is conceptual in nature and aims to develop an integrated model that combines physical energy infrastructure, IoT-based data acquisition, the analytical capabilities of artificial intelligence, and a logic for adaptive real-time decision-making. It is analyzed the theoretical foundations of adaptive management in microgrids, the design of model development of multilayered architecture, and the interaction between physical and information flows. Particular attention is given to the role of intelligent monitoring devices, forecasting and optimization algorithms, as well as the coordination between local generation, storage, consumption, and exchange with the main grid. The proposed model is analyzed through comparison with traditional, optimization-based, and AI-driven models discussed in the scientific literature, and it is argued that the integration of AI and IoT enables higher adaptability, improved load balancing, more efficient use of local energy resources, and better integration of renewable energy sources in the urban energy environment. The proposed model provides a conceptual framework for the intelligent management of electricity flows in urban microgrids, emphasizing its potential for further development and application in sustainable energy systems.

Application of Artificial Intelligence (AI) in Learning among Pre-service Teachers at Thu Dau Mot University: Current Status, Opportunities, and Challenges in the Context of Digital Transformation

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In the context of digital transformation in higher education, artificial intelligence (AI) is increasingly being utilized by pre-service teachers to support their learning. This study aims to analyze the current status, opportunities, and challenges of AI application at Thu Dau Mot University. A mixed-methods approach was employed, including a survey of 412 students and semi-structured interviews with 3 lecturers, 2 administrators, and 5 students.

The findings indicate that AI is widely used for information retrieval, learning support, and content generation, thereby enhancing learning effectiveness, fostering self-directed learning, and enabling personalized learning processes. However, several challenges remain, including over-reliance on technology, insufficient information evaluation skills, and risks related to academic integrity.

Based on these findings, the study proposes several recommendations to improve the effective integration of AI in teacher education, contributing to meeting the demands of digital transformation in higher education.

A Conceptualized Framework of Ethical and Responsible Use of Artificial Intelligence Tools in Higher Education Ecosystem

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This study presents results of a systematic literature review (SLR) of the responsible use of artificial intelligence (AI) tools in higher education, identify patterns of ethical and irresponsible use, and propose a conceptual framework for predicting ethical AI adoption. Following PRISMA guidelines, was conducted on 60 peer-reviewed studies published between 2022 and 2026, sourced from Google Scholar. Studies were mapped against four research questions addressing AI tools used, their applications, reported unethical practices, and predictive modelling approaches. Results reveal that general AI, generative AI tools, and large language models dominate higher education contexts, primarily deployed for personalized learning, academic work, and teaching. Irresponsible practices were documented in one-third of studies, including academic integrity breaches (13.33%), algorithmic bias,  and privacy violations. Critically, no existing study developed a real-time predictive model capable of monitoring ethical AI use, despite four studies demonstrating predictive modelling capabilities for other purposes. This study addresses a significant gap by proposing a novel conceptual framework that integrates AI tool deployment, user behaviour, governance measures, and predictive analytics to forecast ethical outcomes. The framework provides higher education institutions with a pathway toward data-informed, proactive governance of AI technologies.

Artificial Intelligence and Automation in Hospital Administrative Systems: A Scoping Review

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Background: Hospital administrative processes including billing, scheduling, and medical records management—are critical to health system performance but are often characterized by inefficiencies, high operational costs, and workforce burden. Artificial intelligence (AI) and automation technologies, including robotic process automation (RPA) and natural language processing (NLP), have emerged as potential solutions to streamline these processes and enhance productivity.

Objective: This scoping review aimed to synthesize existing evidence on the use of AI and automation in hospital administrative functions, focusing on efficiency gains, cost savings, implementation barriers, and ethical and regulatory considerations.

Methods: A scoping search of peer-reviewed literature was conducted across major electronic databases including PubMed, Scopus, Web of Science, and Google Scholar. Studies published between 2015 and 2025 that examined AI-based or automation-driven interventions in hospital administrative settings were included. Eligible studies addressed applications in billing, scheduling, records management, hospital information systems, or workflow optimization. Data was extracted and synthesized narratively due to heterogeneity in study designs and outcome measures.

Results: The review identified substantial evidence that AI and automation improve administrative efficiency through reduction of processing time, minimization of manual errors, and optimization of resource allocation. RPA demonstrated significant benefits in billing and claims processing, while NLP enhanced documentation accuracy and records retrieval. Several studies reported measurable cost savings and productivity improvements following implementation. However, common barriers included integration challenges with legacy systems, limited interoperability, data quality concerns, staff resistance, insufficient training, high upfront costs, and uncertain short-term return on investment. Regulatory and governance challenges, particularly data protection compliance and algorithm transparency were also frequently highlighted.

Conclusion: AI and automation technologies show considerable promise in transforming hospital administrative processes by improving efficiency and reducing operational costs. Nevertheless, successful implementation requires strong governance frameworks, workforce capacity building, financial planning, and ethical oversight. Future research should focus on longitudinal cost-effectiveness evaluations and context-specific implementation strategies, particularly in resource-limited health systems.

Artificial Intelligence, Change Leadership, and Employee Performance: Evidence from BUMN KCPs in Surakarta

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In the era of digital transformation, the adoption of Artificial Intelligence (AI) in the banking industry presents opportunities to increase efficiency as well as challenges in the form of concerns about the replacement of human roles by technology. This condition has the potential to affect employee performance and work attachment if not managed properly. This study aims to analyze the influence of Artificial Intelligence on Employee Performance and Job Attachment, as well as test the role of Change Leadership as a moderation variable in the context of state-owned banking. The quantitative approach was used by collecting data through a Google Form-based questionnaire which was distributed online and offline to 225 employees from 12 sub-branch offices of state-owned banks in the city of Surakarta. Respondents were selected using the probability cluster sampling technique. Data analysis was carried out using the Structural Equation Modeling method based on Partial Least Squares (SEM-PLS). The results of the study show that Artificial Intelligence has a positive but not significant effect on Employee Performance and Work Attachment. Then, the role of Change Leadership was found to be able to strengthen the influence of Artificial Intelligence on Employee Performance and Work Attachment, which emphasizes the importance of adaptive leadership in managing technological change. Theoretically, these findings enrich the perspective of Dynamic Capabilities theory by showing that the synergy between technology and change leadership shapes the ability of organizations to adapt in a digital environment. Practically, this research provides implications for the banking industry in optimizing the use of AI through adaptive leadership to improve employee performance and engagement in a sustainable manner.

Strategic Management in The Era of Artificial Intelligence Implications, Opportunities, and Challenges

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With the rise of artificial intelligence (AI) technologies, new perspectives are emerging to transform managerial practices, particularly in the field of strategic management. These technologies, which are the result of innovation in the IT sector, imply a redefinition of the strategic management. The latter has been considered throughout management science literature as a driver of competitiveness, development and performance of companies. Within this framework, the objective of this research is to examine, through a theoretical analysis of the literature studying the relationship between strategic management and AI, the implications required for strategic management following the arrival of AI technologies, the opportunities offered by this technology, and the challenges raised by this technological advance. This exploration of the links between AI and strategic management aims to present aspects inherent to professionals and researchers wishing to capitalize on this technological advancement to improve strategic management.

Digital Transformation of The Moroccan SSE: AI and Blockchain at the Service of Social Innovation

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This study examines the impact of artificial intelligence (AI) and blockchain on the Social Solidarity Economy (SSE) in Morocco, in a context where digitalisation represents both an opportunity and a challenge for this key sector. Using a mixed methodology combining a quantitative survey (41 SSE structures) and qualitative interviews (20 players), we analyse the adoption rates, benefits and obstacles associated with these technologies.

The results show that adoption is still limited, but promising: 28% of organisations are using AI, mainly for stock management and data analysis, while 12% are using blockchain, particularly for the traceability of local produce. These technologies are significantly improving operational efficiency (30% reduction in administrative costs), transparency (+45%) and beneficiary satisfaction (+22%). However, major obstacles remain, such as the lack of technical skills (67%), investment costs (58%) and connectivity problems in rural areas (42%).

To maximise this potential, we recommend training adapted to local realities, the creation of funds dedicated to social innovation, and the strengthening of public-private partnerships for inclusive infrastructures.

In conclusion, this research highlights that AI and blockchain can strengthen the Moroccan SSE, provided that a balanced approach is adopted, combining innovation and respect for socio-cultural specificities. It also opens up avenues for future research into hybrid models integrating technologies and traditional know-how.

The Utilization of Artificial Intelligence (AI) in English Skills as an Implementation of the Industrial Revolution 5.0

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The implementation of Artificial Intelligence (AI) in English skills, as exemplified by the Duolingo application, illustrates the transformative shift in education within the Industrial Revolution 5.0 era, where human–machine collaboration enhances personalized skill development. This study involved 30 Grade XI students at SMKN 3 Baubau and employed a descriptive qualitative approach to examine learners’ perceptions and experiences with AI-based learning tools. Data were collected through field observations, English proficiency tests, questionnaires, and interviews. The results indicate that Duolingo effectively supports self-directed blended learning, improving students’ speaking, listening, reading, and writing skills in an engaging and autonomous manner. Through AI-driven gamification, Duolingo adapts to individual learner performance by providing instant feedback, progressive difficulty levels, and interactive exercises that maintain motivation and reduce learning fatigue. These adaptive features align with the vision of Industrial Revolution 5.0, where technology collaborates with learners to optimize progress. Most participants reported significant improvements in vocabulary acquisition, pronunciation, and comprehension, attributing their advancement to daily practice and active engagement facilitated by the application. In conclusion, AI-powered platforms like Duolingo go beyond digitizing traditional learning; they transform the educational experience by fostering intrinsic motivation, sustaining engagement, and enabling personalized learning pathways, thus equipping learners for global communication demands with both efficiency and enjoyment.

Exploring the Impact of AI on English Learning of Business English Juniors at Nguyen Tat Thanh University

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This study investigates how artificial intelligence (AI) influences the English‑learning processes of third‑year Business English majors at Nguyen Tat Thanh University (NTTU). A mixed‑methods design was employed, comprising an online survey (n = 34) and semi‑structured interviews (n = 15). Quantitative data were analyzed using SPSS 26, yielding Cronbach’s α = .984, and qualitative responses underwent thematic analysis. Students reported that AI tools (e.g., ChatGPT, Grammarly) enhanced personalized learning (44.1 %), provided instant feedback (17.6 %), and increased motivation (17.6 %). Interview themes highlighted efficiency gains, richer access to specialized Business English materials, and greater communicative confidence. These findings demonstrate AI’s pedagogical value in Business English instruction and suggest that integrating AI‑powered platforms can optimize curriculum design. Future research should compare AI‑mediated and traditional teaching to assess long‑term learning outcomes.

The Impact of AI on The Learning Habits of HEI Students

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The old paradigms of education are being disrupted as a result of the growing incorporation of artificial intelligence (AI) technology into higher education institutions (HEIs). The purpose of this study is to evaluate the influence that artificial intelligence-driven tools and platforms, such as adaptive learning systems, intelligent tutoring systems, automated feedback mechanisms, and AI-based content recommendation engines, have on the behaviour of students who are enrolled in higher education institutions. The research finds major changes in students’ study behaviours, time management, and learning preferences. This is accomplished via the use of a mixed-methods approach, which combines quantitative surveys and qualitative interviews with undergraduate and postgraduate students from a wide range of academic fields. The findings indicate a trend towards learning experiences that are more personalized, autonomous, and on-demand. This transition is made possible by the ability of artificial intelligence to analyse individual learning patterns and present information that is suited to those patterns. An over dependence on artificial intelligence, a decrease in critical thinking, and digital exhaustion are some of the concerns that are brought to light by the study. The final section of the study emphasises the importance of integrating artificial intelligence in a way that is balanced, so that it may improve learning outcomes while maintaining cognitive engagement and academic integrity.