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This article examines the relationship between Artificial Intelligence (AI), the concept of sensus communis proposed by Nicholas of Cusa (1401–1464), and the formation of religious communities. Through a theoretical-philosophical analysis, the author argues that sensus communis as the integrative capacity of the human intellect that unifies sensory, rational, and intuitive dimensions offers a normative epistemological framework for critically addressing the reductionism inherent in algorithmic AI. The main finding indicates that AI constitutes only a partial simulacrum of the integrative capacity of human reason and, therefore, cannot replace the ontological-transcendental dimension of authentic formation. Accordingly, this article proposes a model of critical-integrative formation grounded in three pillars: the selective use of AI, the preservation of AI-free spaces, and hermeneutical integration. The relevance of Cusa’s thought for contemporary religious formation is articulated in three contributions: docta ignorantia as a formative habitus, coincidentia oppositorum as a paradigm of dialogue, and ontological participation as the foundation of knowledge.

This research is motivated by the increasing role of Artificial Intelligence (AI) in organizational transformation and the crucial function of leadership in ensuring its successful implementation. The primary objective of this study is to analyze the relationship between leadership, AI integration, and organizational performance, as well as to identify various challenges and supporting factors in the process. This study employs a systematic literature review (SLR) method by examining 30 relevant, reputable scientific articles from the Scopus and Google Scholar databases within the 2020–2026 timeframe through selection, evaluation, and thematic synthesis processes. The results indicate that AI integration significantly contributes to improving operational efficiency, data-driven decision-making quality, and organizational innovation. However, this success heavily depends on the role of adaptive, transformational, and digitally-oriented leadership capable of steering the technological vision. Conversely, major challenges were identified, such as employee resistance, limited digital competencies, and ethical issues surrounding data privacy. This study contributes to strengthening the conceptual understanding of leadership's role as a bridge between technology and organizational performance, while offering practical implications for management in designing effective, inclusive, and sustainable digital transformation strategies.

The advancement of digital technology has significantly transformed organizational decision-making, particularly in modern leadership contexts that demand rapid and data-driven responses. Artificial Intelligence(AI) has emerged as a strategic technology capable of enhancing accuracy, speed, and effectiveness in decision-making through comprehensive data analysis. This study aims to analyze the role of AI in supporting leadership decision-making and its implications for organizational effectiveness using a narrative literature review approach. Secondary data comprising peer-reviewed national and international journal articles were analyzed to identify patterns, themes, and interactions between AI, leadership, and decision-making processes. The findings indicate that AI functions not only as a data analysis tool but also as a strategic element that strengthens leaders’ capabilities in evidence-based decision-making, improves team coordination, and optimizes organizational processes. Thematic synthesis identified three main domains analytics and predictive capabilities, leadership strategies, and implementation challenges that form the basis for integrating AI into managerial practice. This study contributes theoretically by expanding the digital leadership and technology-based decision-making framework and practically by providing guidance for organizations to optimize AI utilization to enhance decision quality and efficiency. The research also offers directions for future empirical studies to explore AI-leadership interactions across various organizational sectors, supporting more adaptive, effective, and data-driven decision-making in the digital era.

Algorithmic technologies are widely used in contemporary marketing strategies due to the growth of the digital economy. Digital companies can evaluate consumer activity data in real time and provide highly personalized digital experiences thanks to artificial intelligence-based solutions, especially machine learning. In addition to examining how algorithmic governance and surveillance capitalism affect algorithmic personalization, this study looks into how these mechanisms affect consumer engagement, purchase intention, and perceptions of hyperreality within the digital market ecosystem. 356 active users of digital platforms, such as social media and e-commerce, were surveyed as part of this study's quantitative methodology. The links between the constructs in the suggested conceptual model were examined through data analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that the development of algorithmic personalization systems is strongly influenced by data-driven capitalism practices and algorithmic governance. Additionally, it has been demonstrated that algorithmic personalization improves customers' sense of hyperreality and increases their interaction with digital platforms. Additionally, the study shows that the most powerful factor influencing purchase intention is consumer interaction. By combining viewpoints from technology, the political economics of data, and hyperreality theory into a thorough empirical framework, these findings add to the body of knowledge on digital marketing.

This study aims to examine the impact of academic burnout on students’ mental health in the context of hybrid learning through a literature review approach. The rapid transformation of learning systems in higher education, particularly the integration of online and offline methods, has increased academic demands and adaptation challenges for students. This condition potentially triggers academic burnout, which is characterized by emotional exhaustion, depersonalization, and decreased academic achievement. This research employs a literature review method by analyzing relevant scientific articles published between 2019-2025 obtained from indexed national journals and open-access sources. The findings indicate that academic burnout is still commonly experienced by students, generally at a moderate level, and has a significant impact on mental health, including increased stress, emotional fatigue, and decreased academic performance. The study also identifies several influencing factors, including internal factors such as self-efficacy and hardiness, as well as external factors such as social support, learning environment, and academic workload. Furthermore, technological developments, particularly Artificial Intelligence, have the potential to be utilized as an early detection and prevention tool for academic burnout. This study implies the importance of developing adaptive learning strategies and psychological interventions to support students’ well-being in hybrid learning environments.

The rapid proliferation of learning analytics, business intelligence (BI), artificial intelligence (AI), and generative AI (GenAI) has significantly expanded universities’ ability to collect, integrate, analyze, and operationalize institutional data. However, despite advances in predictive analytics, dashboards, and AI-driven systems, the translation of analytical outputs into consistent and accountable institutional decision-making remains uneven. This systematic literature review synthesizes contemporary research on analytics-enabled decision-making in higher education with the aim of moving beyond dashboard-centric perspectives toward a socio-technical and computing-oriented understanding of how data are transformed into institutional actions and outcomes. Guided by the PRISMA framework, the review synthesizes evidence across four interconnected dimensions: data ecosystems and learning analytics foundations; analytics capability, BI adoption, and digital readiness; AI and advanced analytics for decision support; and human-in-the-loop (HITL) decision routines and institutional outcomes. The findings show that predictive performance and analytical sophistication alone do not guarantee decision value. Instead, effective analytics-enabled decision-making depends on interoperable data ecosystems, organizational analytics capability, governance mechanisms, explainability, and sustained human oversight. Based on these findings, this review contributes a computing-oriented decision-intelligence framework that conceptualizes analytics-enabled decision-making as an end-to-end socio-technical pipeline linking heterogeneous data acquisition, integration, feature construction, analytical modeling, explainability, human validation, governance, and feedback-based refinement. By integrating learning analytics, BI, AI, GenAI, and HITL mechanisms within a unified framework, the review clarifies how universities can move beyond dashboard-based reporting toward accountable, adaptive, and institutionally actionable decision-support infrastructures.

This study examines the role of Artificial Intelligence (AI) in enhancing student collaborative learning, with a particular emphasis on AI-driven feedback mechanisms and patterns of student interaction in developing effective collaborative skills. Unlike prior studies, this research highlights the mediating effect of AI-driven feedback on teamwork efficiency and overall learning outcomes in collaborative environments. An explanatory quantitative approach was applied using Partial Least Squares Structural Equation Modeling (PLS-SEM) to ensure robust data analysis. Data were collected from 112 university students who were actively engaged in AI-assisted collaborative learning activities, using a structured online survey instrument. The data were subsequently analyzed using SmartPLS software. The results reveal that AI significantly enhances student interaction (β = 0.534, p < 0.000) and improves problem-solving feedback (β = 0.620, p < 0.000), both of which contribute to significantly strengthening collaborative skills (β = 0.716, p < 0.000). However, the findings also indicate that AI alone does not directly improve collaboration without the support of structured pedagogical design and guidance. Therefore, universities should strategically integrate AI-driven feedback into Learning Management Systems (LMS) and strengthen digital literacy initiatives to optimize the effectiveness and sustainability of AI in collaborative learning contexts.

The development of Artificial Intelligence (AI) has transformed how business organizations manage operations, process information, and make strategic decisions, thereby creating a need for leadership patterns that are more adaptive, visionary, and responsive to digital transformation. This study aims to explain the concept of AI Leadership in the business context, identify why modern leadership must adapt to the advancement of AI, and analyze the leadership strategies that are relevant for navigating the era of AI-driven business. The study employs a Systematic Literature Review (SLR) approach with a narrative-thematic synthesis of relevant open-access scholarly literature on AI Leadership, digital leadership, human–AI collaboration, business transformation, and AI ethics. The findings show that AI Leadership represents a modern form of leadership that emphasizes not only technological understanding but also the ability of leaders to direct organizational change, foster data-driven decision making, manage collaboration between humans and technology, and implement responsible AI governance. The study also finds that the main strategies required by leaders in the AI era include strengthening digital literacy, developing adaptive managerial capabilities, enhancing human–AI collaboration, and integrating ethical principles into technology implementation. This article contributes conceptually by reinforcing AI Leadership as a leadership framework that is highly relevant to modern organizations and offers practical implications for the development of business leadership in the era of digital transformation.

The development of artificial intelligence (AI) has transformed organizational landscapes and driven fundamental changes in leadership practices and strategic management. This article aims to critically examine AI-based leadership by highlighting its opportunities, risks, and implications for strategic management. The study employs a qualitative literature-based approach using an integrative literature review strategy. The data consist of secondary scholarly literature relevant to AI, leadership, governance, innovation, and strategic management, which were analyzed through qualitative thematic analysis and conceptual content analysis. The findings show that AI-based leadership creates opportunities in the form of improved decision quality, faster strategic analysis, operational efficiency, stronger innovation, and enhanced organizational agility. However, AI integration also presents risks, including algorithmic bias, limited decision transparency, technological dependency, accountability challenges, and ethical concerns. This study confirms that AI does not fully replace human leaders; rather, it fosters a hybrid leadership model that requires technical, adaptive, transformational, and ethical capabilities. The study implies that the effectiveness of AI-based leadership depends on responsible governance, organizational cultural readiness, and balanced human–machine collaboration in supporting strategic management.

This study examines the use of generative artificial intelligence (GenAI) among theology students and its ethical and theological implications for theological education in the Society 5.0 era. Employing a light mixed-methods sequential explanatory design, the research involved theology students through surveys and semi-structured interviews. The findings indicate that GenAI is primarily utilized as a learning support tool for understanding course materials and developing academic assignments. While students demonstrate awareness of potential bias and ethical concerns, consistent transparency and critical reflection in AI usage remain limited. The theological concept of imago Dei emerges as a significant analytical lens, emphasizing human dignity and moral responsibility in academic practices. The study concludes that GenAI can constructively support theological education when integrated with faith-based ethical literacy and clear institutional guidelines.

This study aims to describe the transformation of Biology learning through the integration of Artificial Intelligence (AI) to strengthen 21st-century skills at a State Senior High School in Waingapu, East Sumba. Amidst the limitations of physical laboratory facilities, AI emerges as an innovative solution to visualize abstract biological concepts. This research employs a qualitative approach, with data collection techniques involving observation, interviews with teachers and students, and documentation studies. The results indicate that the utilization of AI platforms, such as Generative AI and virtual simulations, significantly shifts the student learning paradigm from rote memorization to active inquiry. The integration of AI has proven effective in reinforcing the 4C skills (Critical Thinking, Creativity, Collaboration, and Communication). Students have become more critical in validating information, creative in designing science projects, and more collaborative in classroom discussions. Furthermore, AI assists teachers in streamlining instructional time, allowing for deeper conceptual exploration. Despite challenges regarding local internet stability, this transformation represents a strategic step in narrowing the educational quality gap between peripheral and urban areas. In conclusion, the implementation of AI, coupled with adaptive teacher guidance, is highly effective in developing competitive students in the digital era.

The rapid development of Artificial Intelligence (AI) technology requires an improvement in digital literacy and skills among students, particularly at the vocational high school level. However, many students still lack sufficient understanding of the basic concepts and practical applications of generative AI in learning. This Community Service Program (Pengabdian kepada Masyarakat/PKM) aims to enhance students’ understanding of AI technology through generative AI training at SMK Al-Muhajirin Depok. The methods used in this program include interactive lectures, demonstrations of generative AI applications, and hands-on practice sessions. The activity was attended by 30 students and supported by the school management. The results indicate a significant improvement in students’ understanding of generative AI concepts and their ability to apply the technology in a simple learning context. In addition, students gained awareness of the ethical use of AI in an appropriate and responsible manner. Therefore, this PKM activity contributes positively to improving students’ digital literacy and readiness to face technological advancements in the modern era.

The rapid development of Artificial Intelligence (AI) has brought significant changes to the criminal justice system, particularly in criminal investigations and evidentiary processes, while simultaneously raising complex legal and ethical challenges. Objective: This study aims to analyze the legal implications of the use of AI in criminal investigations, focusing on its benefits, risks, and challenges related to the admissibility of AI-based evidence, as well as the need for regulatory frameworks that ensure fairness, transparency, and accountability. Methods: This research employs a normative qualitative approach through the analysis of legal regulations, a review of legal and technological literature, and a comparative approach across jurisdictions, complemented by case studies of AI applications in law enforcement practices. Results: The findings indicate that AI enhances investigative efficiency through data analysis, crime prediction, and digital forensics; however, it also poses risks such as algorithmic bias, human rights violations, and issues concerning the reliability and transparency of evidence. Furthermore, differences across legal systems result in the absence of uniform standards for the admissibility of AI-based evidence. Therefore, adaptive regulatory frameworks grounded in the principles of fairness, transparency, and accountability are required, along with strengthened human oversight to ensure that the use of AI aligns with the principles of justice and human rights protection.

The development of Artificial Intelligence (AI) integrated with biometric technology has opened new opportunities in leadership development, particularly in enhancing emotional regulation capabilities within high-stakes environments. This study aims to analyze the role of AI-driven biometric feedback in improving self-awareness, emotional regulation, and decision-making quality among leaders. The study employs a systematic literature review approach by examining recent reputable scientific publications related to AI, biometrics, and leadership. The findings indicate that physiological data such as heart rate variability (HRV) and galvanic skin response (GSR), when processed through AI systems, can provide real-time feedback that enhances individuals’ ability to recognize and regulate emotions adaptively. Furthermore, the integration of this technology contributes to improved accuracy and consistency in decision-making under pressure. The results also reveal that the effectiveness of implementation is influenced by both technical and non-technical factors, including data quality, algorithm accuracy, and user acceptance. This study contributes to strengthening the integration of psychological and technological approaches in modern leadership research and offers practical implications for developing data-driven leadership training programs in the digital era.

This study aims to examine the integration of Generative AI in higher education through the perspective of the SAMR framework, which consists of Substitution, Augmentation, Modification, and Redefinition. The study used a narrative literature review method by analyzing 30 relevant academic publications published between 2023 and 2025. The findings reveal that the implementation of GenAI in higher education is still predominantly situated at the substitution and augmentation levels, with primary functions focused on improving efficiency, automation, and academic assistance. In contrast, transformative applications categorized under modification and redefinition remain relatively limited and are still in the early stages of development. This study concludes that although Generative AI possesses substantial potential to transform higher education practices, its current implementation has not yet reached an optimal transformative stage. Therefore, future educational practices should emphasize the development of more transformative implementation strategies that move beyond efficiency-oriented utilization toward fostering pedagogical innovation and meaningful learning transformation.

Digital transformation has brought fundamental changes to the structure and dynamics of modern industrial economics. Technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and big data not only modify production and distribution processes but also revolutionize marketing strategies and patterns of industrial competition. This study is motivated by the need to understand how digital marketing transformation influences the development of competitive advantage through changes in digital market structure from an industrial economics perspective. In this context, digital marketing functions as a strategic instrument that integrates technology, data, and consumer behavior into market mechanisms. The analysis shows that digitalization creates a network-based market structure characterized by the concentration of economic power in major digital platforms and dominance in data control. This structure affects the intensity of competition, the direction of innovation, and patterns of industry differentiation. Digital marketing transformation enhances efficiency, expands market access, and lowers entry barriers for new players, yet it also creates competitive imbalances due to the dominance of large platforms.Through a digital Structure–Conduct–Performance (SCP) approach, the study finds that market structure acts as an intermediary variable that channels the impact of digitalization on competitive advantage. Digitalization significantly promotes industrial efficiency, innovation, and profitability. Proposed strategic solutions include strengthening digital literacy, developing adaptive regulations, and fostering cross-sector collaboration to create an inclusive, competitive, and sustainable digital industrial ecosystem

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.

The development of generative artificial intelligence has given rise to a new form of digital-based sexual violence through the spread of sexual deepfakes, non-consensual synthetic sexual representations that can attack the dignity, privacy, sexual autonomy, and sense of security of victims. This threat becomes even more serious when targeting students and educators because it not only harms individuals but also disrupts the integrity and security of educational spaces. This study aims to analyze the construction of Indonesian criminal law in ensnaring the spread of sexual deepfakes in educational environments, identify weaknesses in its regulations, and formulate a more ideal reconstruction of criminal liability. The method used is normative legal research with a qualitative descriptive approach, through a literature review of laws and regulations, scientific literature, and relevant documents related to deepfakes, electronic-based sexual violence, and legal protection in educational environments. The results of the discussion indicate that Indonesian positive laws, such as the ITE Law, the TPKS Law, the Pornography Law, the Personal Data Protection Law, and educational regulations, have essentially provided a normative basis for prosecuting such acts, but they are still partial, fragmented, and do not explicitly regulate sexual deepfakes as a separate crime. Therefore, a reconstruction of criminal liability is needed that explicitly recognizes non-consensual synthetic sexual representation as a crime, expands the forms of punishable acts, provides for greater severity in the context of educational relations, and comprehensively integrates criminal penalties with victim protection and recovery. Keywords: sexual deepfakes, criminal liability, students, educators, digital-based sexual violence.

The rapid development of artificial intelligence (AI) has brought significant changes to higher education, particularly in the way students think and learn. AI no longer functions merely as a learning support tool but also influences students’ cognitive processes, including critical thinking, problem-solving abilities, and creativity. This study aims to examine the impact of AI usage on the thinking patterns of university students. The research method employed is a literature review, analyzing various accredited national journals and relevant conference proceedings. The findings indicate that AI offers several positive impacts, such as improving learning efficiency, expanding access to information, and supporting the development of creative ideas. However, excessive reliance on AI may also lead to negative effects, including a decline in critical thinking skills and an increased tendency for students to depend on AI-generated outputs without in-depth analysis. Therefore, the role of educators is crucial in guiding students to use AI wisely and responsibly. AI should be utilized as a supportive tool in the learning process rather than a replacement for human thinking. This study is expected to contribute to the development of effective and ethical strategies for integrating AI in higher education.

The internship program of Universitas Mayjen Sungkono in Begaganlimo Village introduced a training initiative focused on the Development of an Adaptive Learning Platform Based on D-PACK as an effort to enhance the quality of digital learning in Begaganlimo elementary schools. This training was designed to assist teachers in strengthening their ability to design and utilize interactive learning media supported by artificial intelligence (AI). Through this socialization activity, teachers were not only introduced to the use of adaptive technology but also guided to apply it in teaching practices so that the learning process becomes more effective, easier to understand, and more engaging for students. In addition, the socialization activities provided a deeper understanding of how technology can be used and integrated into teaching. Teachers not only learned to operate AI-based applications or devices, but they also gained insight into the pedagogical principles within the DPACK framework, which ensures that technology is applied appropriately. As a result, learning is expected to become easier to understand, more engaging, and capable of increasing students’ participation and motivation in the classroom.