From Sacred Sanctuary to Intelligent System: A Religious-Studies Reflection on the Transformation of Temple Space under Artificial Intelligence

The integration of Artificial Intelligence (AI) and Information Systems (IS) is fundamentally reshaping the public health landscape, offering groundbreaking solutions for disease prevention, management, and surveillance. As these technologies advance, they offer unprecedented opportunities to improve health outcomes through real-time monitoring, the creation of personalized health strategies, and greater efficiency across health interventions. Despite these promising developments, the public health sector continues to face ongoing questions regarding the optimal implementation of these tools across diverse populations and complex healthcare systems. This Research Topic was established to explore how the fusion of AI and IS can transform and elevate the efficacy of public health practices, policies, and education.A primary hurdle in leveraging technology is ensuring that health systems are structurally ready for digital adoption. Two key studies in this collection address this "readiness" from different angles. Snowdon et al. contribute a crucial piece. Their work utilizes a cross-sectional analysis to assess the current capacity of digital public health systems, providing a benchmark for how state-level infrastructures can evolve to meet modern technological demands.While infrastructure is physical, "literacy" is the human component of readiness.Kumar et al. explore the barriers preventing healthcare professionals and systems from fully embracing AI. Their research emphasizes that the effectiveness of technological tools depends on the literacy of those who deploy them, identifying specific adoption challenges within the public healthcare sector.One of the most powerful applications of AI in public health is its ability to predict future crises. Mingyu Du explores this topic by utilizing information systems to process time-series data. This study demonstrates how proactive forecasting can provide public health officials with the lead time necessary to outbreaks before they escalate.In addition to infectious diseases, chronic conditions represent a significant global health burden that requires continuous monitoring. Haiyan Xie introduces a specialized technical approach. This study demonstrates how sophisticated neural network architectures, such as capsule networks, can improve the accuracy of predictions for long-term health issues in both clinical and community settings. Complementing this specific model is the systematic review by Liu and Wang. Their review synthesizes current research on combining Internet of Things (IoT) devices and machine learning to provide a comprehensive framework for patient monitoring.Tailoring AI and IS to specific life stages is a recurring theme in this collection.Maternal health, for instance, benefits significantly from data-driven management. At the other end of the life span, the aging population requires intelligent systems for better health management. Wenjie Li et al. address this issue. By leveraging hypergraph convolution, a complex AI technique, they developed a platform capable of addressing the multifaceted needs of the elderly, helping bridge the gap in geriatric care.Monitoring and influencing health behaviors is another area where AI excels. In educational environments, Lu and Ruijuan explore the use of monitoring systems. This research highlights how AI can recognize physical actions to monitor student health and activity levels within schools.Cui and Yin address the psychological barriers to physical activity. Their use of explainable AI is particularly noteworthy, as it helps identify why students who intend to exercise often fail to do so, providing actionable insights into behavioral market. This study raises important questions about equity, examining whether robust governance and higher education can shield vulnerable populations from the potential negative economic impacts of AI.The 14 articles presented in this Research Topic demonstrate the vast potential of Information Systems and Artificial Intelligence in modernizing public health. From digital maturity assessments in Missouri to secure blockchain transactions for IoT, these studies provide a roadmap for a more data-driven health sector. However, the editor notes that significant challenges remain, particularly regarding data privacy and security, as well as the equitable distribution of these benefits.The findings in this special issue underscore that the successful implementation of AI in public health requires more than advanced algorithms; it also necessitates robust infrastructure, high levels of eHealth literacy, and a commitment to security. By addressing these themes, this collection contributes to a deeper understanding of how these powerful tools can be leveraged to foster a healthier and more resilient society

This report on the Future of Work Data driven offers a structural and best-practices prescriptive overview of digitally-enabled, data-driven management and decision-making. It reframes the core objectives of management performance in the era of technological disruption and provides an agile operational framework for adopting data-driven management, adaptable to businesses of any size or industry and scalable across organizations. The report also includes a wealth of curated data and insights from the field of digital transformation, identifying common obstacles to success, outlining key strategic opportunities for a successful shift to data-driven management, quantifying the risks of resisting this shift, and forecasting the future of technology-assisted decision-making. As the future of work evolves, the integration of advanced data analytics into management systems is reshaping how organizations measure, manage, and drive employee performance. "Data-Driven Performance Management" represents a paradigm shift from traditional, subjective evaluation methods to more objective, real-time, and evidence-based approaches. This paper explores the role of data analytic s, artificial intelligence (AI), and machine learning in enhancing performance management processes, focusing on personalized feedback, predictive insights, and continuous development. By leveraging real-time data, organizations can better align individual and team performance with strategic goals, drive employee engagement, and foster a culture of continuous improvement. Furthermore, the paper examines the ethical considerations, challenges, and potential pitfalls of using data in performance management, such as privacy concerns, algorithmic biases, and the risk of over-reliance on data-driven decision-making.

The article is dedicated to the integration of artificial intelligence (AI) into the activities of the National Police of Ukraine. It emphasizes that the use of AI can significantly transform information processing, automate processes, and enhance police efficiency, particularly in monitoring the Internet and social networks to prevent and solve crimes. However, one of the key challenges in implementing AI in law enforcement is the lack of proper legal regulation. The use of AI-based systems, such as facial recognition technologies or crime prediction tools, may lead to violations of citizens’ privacy and even discrimination if these systems are employed without appropriate oversight and transparency. The need to protect personal data in the context of increasing AI usage is especially pressing, as Ukraine must comply with international standards such as the General Data Protection Regulation (GDPR) to ensure adequate information protection. In addition, overcoming technical limitations related to infrastructure, unstable power supply, and insufficient resources is essential for the effective use of AI. The article proposes the introduction of legal acts to regulate AI use and ensure its controlled application. The creation of an independent oversight mechanism, as well as the implementation of modern data protection technologies such as encryption, will help minimize the risks of unauthorized access to personal information. Thus, the combination of legislative regulation, technical modernization, and process transparency will contribute to enhancing police efficiency and protecting human rights in the context of digital transformation.

This article links three rarely considered dimensions related to the implementation of artificial intelligence (AI)-based technologies in the form of predictive policing and discusses them in relation to liberal democratic societies. The three dimensions are the theoretical embedding and the workings of AI within anomic conditions (1), potential normative disorders emerging from them in the form of thinking errors and discriminatory practices (2) as well as the consequences of these disorders on the psychosocial, and emotional level (3). Against this background, AI-induced anomie is conceptualized as a field of tension that refers to a systematic deterioration of democratic norms that are supposed to create ‘normative orders’, but which, when implemented through AI-supported measures, can reproduce existing discriminations, and establish new kinds of discriminatory relations. In future, these AI-based measures have the potential to lead to opposing normative disorders by emerging in the form of false social norms to an equally false Second Nature. They deprive persons involved of the possibility of individual appropriation of social norms and the specific emotional development associated with it.

In an era marked by rapid technological disruption, dynamic market conditions, and shifting workforce expectations, organizations are increasingly recognizing the strategic value of data-driven talent management. This paper explores the integration of Artificial Intelligence (AI) and advanced analytics into workforce strategy as a pivotal approach to achieving workforce optimization. While traditional talent strategies have relied heavily on intuition, experience, and retrospective performance reviews, the emergence of AI has introduced a proactive, predictive, and prescriptive dimension to managing human capital. The study delves into how data-driven frameworks can be leveraged to attract, retain, develop, and deploy talent more effectively in alignment with organizational goals. Drawing from contemporary industry practices, empirical data, and cross-sector case studies, this research demonstrates how AI tools such as machine learning algorithms, natural language processing, and sentiment analysis can unearth patterns from vast, unstructured datasets to enhance decision-making. Applications discussed include AI-driven candidate screening, predictive attrition models, employee engagement forecasting, skill gap identification, and real-time performance analytics. Moreover, the paper critically evaluates the ethical, legal, and organizational implications of AI integration in human resources. While the use of algorithms and predictive tools presents tremendous opportunities to reduce bias and improve transparency, it also raises questions about data privacy, algorithmic fairness, and the erosion of human oversight in workforce decisions. Through a balanced approach, the research underscores the importance of implementing responsible AI frameworks that align with regulatory norms and uphold employee trust. Findings suggest that organizations embracing AI and analytics in talent management experience greater agility, improved workforce planning accuracy, and increased return on human capital investments. However, the transformation is not solely technological; it demands a cultural shift within HR functions, necessitating new skillsets, interdisciplinary collaboration, and executive buy-in. The paper concludes by offering a strategic model for implementing data-driven talent strategies that combine AI capability with human insight, emphasizing a phased, scalable, and ethically grounded approach. This research contributes to the growing body of knowledge on strategic workforce management and offers actionable insights for HR leaders, policymakers, and business strategists seeking to future-proof their organizations through intelligent talent practices. In doing so, it positions AI and analytics not as replacements for human judgment but as vital instruments in enhancing the strategic role of HR in building resilient, data-literate, and performance-optimized workforces.

The next-generation of Robotics and Artificial Intelligence (AI) sets a new horizon of inquiry into the obvious and hidden significances of the secular techno-society. By incorporating the perspectives of STS (Science, Technology and Society) into the sociology of religion, this article attempts to explore the social-religious significance found in the emerging relationships between the human and Robotics and AI. By referring to several cases of Robotics from Japan and the US, this article examines three versions of the relationship, namely, the physical, the social and the psychological. Several concrete cases are referred to for each aspect: the robot suit, the robotic arm, and BMI (Brain Machine Interface) for the physical relationship; PARO, a seal-like healing robot, OriHime, and Pepper for the social relationship; and a conversation with artificial ‘personality’ such as Bima48 for the psychological relationship. For each of this kind of relation, the involvement on Sociology of Religion is discussed.

The integration of artificial intelligence and algorithmic systems into social media platforms has fundamentally reshaped the information landscape during 2023-2024. This comprehensive study examines the transformation of the information space across major platforms: Facebook, X (formerly Twitter), TikTok, and YouTube, analyzing both positive and negative dimensions of AI-driven algorithmic curation. Through systematic analysis of platform policies, algorithmic mechanisms, and content moderation frameworks, this research identifies significant opportunities for information democratization and targeted harm prevention alongside concerning risks of filter bubbles, algorithmic bias, and manipulated discourse. The study demonstrates that AI algorithms, while enabling unprecedented scalability in content moderation achieving accuracy rates of 85-96%, simultaneously generate echo chambers that reduce exposure to diverse viewpoints. The research reveals critical disparities in algorithmic treatment across demographic groups and geographic regions, with particular challenges in non-Western language content moderation. A comprehensive framework for ethical algorithmic governance is proposed, emphasizing transparency requirements, bias auditing mechanisms, and participatory design approaches. This paper concludes that the future of information integrity depends not on algorithmic advancement alone but on institutional commitments to democratic accountability and cross-stakeholder collaboration in platform governance.

The Najaf Sea is undergoing significant environmental changes due to the climate change and urban expansion, resulting in decreasing water levels that affect the surrounding areas. The present study employs two technologies, Remote Sensing (RS) and Artificial Intelligence (AI), to monitor the environmental changes as part of the sustainability assessments. It aims to observe and analyze the changes between 2018 and 2025, tracking the variations in the water bodies and evaluating the impacts of the climate and human activity on sustainable resource management. Additionally, this supports rational decision-making regarding the environmental conservation, sustainable development, and resource management in the region. The Normalized Difference Water Index (NDWI), Support Vector Machine (SVM), Geographic Information Systems (GIS), and Sentinel-2 images were utilized to analyze the changes in the water bodies and their associated environmental impacts. Through change detection, AI models deliver highly accurate predictions of sustainable water resources. This paper highlights the critical role of advanced technologies in monitoring and forecasting the changes to water resources. The findings promote ecological protection and help local communities adapt to the environmental shifts through data-driven management. This research provides essential information to help experts develop resilient strategies for environmental conservation and climate change mitigation. As demonstrated, these technologies enhance monitoring in sensitive areas, such as the Najaf Sea. The study fosters ecological balance, protects the environment, and encourages development through technological advancements for the benefit of the local population.

Purpose: The purpose of this publication is to analyse the potential of AI implementation in people analytics. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases connecting with researched topic. Findings: The main findings of the study indicate that the integration of artificial intelligence into People Analytics significantly extends the analytical capacity of organizational behavior research by enabling dynamic, multi-level, and behaviorally grounded analysis of employee activity, interaction, and decision-making. The paper demonstrates that AI-driven People Analytics shifts the focus from static, descriptive HR indicators toward predictive and pattern oriented explanations rooted in real-time, multi-source data, allowing classical organizational behavior theories to be operationalized at unprecedented scale and temporal resolution. Empirical applications reviewed in the study show that AI supports more accurate identification of engagement dynamics, turnover risk configurations, team interaction structures, leadership behaviors, well-being trajectories, and skill development processes, while also revealing latent patterns that remain invisible to traditional methods. At the same time, the findings emphasize that the scientific and practical value of AI-based analytics depends on strong theoretical framing, contextual interpretation, and ethical governance, without which algorithmic insights risk reductionism and loss of explanatory depth in organizational research. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage analysed scientific problem. Keywords: People Analytics; Artificial Intelligence; organizational behavior; human resource analytics; machine learning; employee engagement; workforce decision-making; predictive analytics; data-driven management. Category of the paper: literature review.

The article "Embracing Real AI: A Call to Action for Medical Physicists in Healthcare" urges medical physicists to prepare for the integration of artificial intelligence (AI) into healthcare practices, emphasizing their pivotal role in adapting to technological advancements. The authors advocate for embracing AI through advocacy, broadening perspectives, and enhancing coordination and communication. They propose an ABC strategy focusing on increasing educational initiatives, fostering interdisciplinary collaboration, and creating team collaboration to facilitate AI integration. The commentary highlights AI's potential in enhancing diagnostics, personalizing medicine, and automating routine tasks while addressing challenges such as data sharing and the role of federated learning. The article calls for medical physicists to lead in embracing AI, emphasizing continuous learning and collaboration to leverage its potential for improving healthcare and patient care. Medical physicists have consistently demonstrated strong interest in developing proficiency in the adoption of new technological advancements. The roots of the profession come from the radiation sciences, including radiation protection, radiation therapy, diagnostic imaging, and nuclear medicine.1 As science and technology continued to evolve, medical physicists' roles have extended into other non-radiation domains, such as non-ionizing-radiation-based imaging (ultrasound and magnetic resonance), molecular imaging, computer aided diagnosis (CAD), information technologies, and data science.2 In addition, medical physicists gradually have adopted increasingly more active roles in ensuring the professional education of other radiology/radiation oncology team members, maintaining high quality standards via quality assurance (QA) methods. They also play a major role in advising the hospital management on medical devices and software acquisition. The continuing expansion of these roles and responsibilities has put medical physicists on the forefront of embracing emerging technologies, making the profession one of the most technical and versatile in healthcare settings. Currently, as our field grows in importance, we medical physicists seek to continue to engage in significant ways to for increased contributions and roles in human health. This commentary/opinion urges medical physicists to prepare for their expanding roles in the field of AI and its implementation and oversight in clinical practice. Medical physicists must embrace "Real AI" to help integrate AI into healthcare practices. Conceptually we advocate for a strategy that involves Real AI through advocacy, broadening, and enhancing coordination/communication (an ABC strategy). In our current and future work medical physicists will use AI to automate routine tasks, allowing medical physicists to focus on more complex tasks. Furthermore, Medical Physics will use AI to enhance efficiency, safety, diagnostic and therapeutic applications, and for personalized medicine. However, as we have done in the past with other complex concepts (such as radiation), medical physicists need to be prepared for the potential risks and ethical dilemmas associated with AI, such as bias and lack of transparency. It will be important that Medical Physicists prepare for the rapidly changing AI landscape, and continue learning, gain hands-on experience, and collaborate with other AI experts in the healthcare environment. This paper aligns with the already approved guidance document developed by the AAPM in conjunction with International Atomic Energy Agency (IAEA)3 that discusses how medical physicists can ensure the effective implementation and management of AI systems. It is crucial for the Clinical Quality Management Program (CQMP) personnel to receive regular training and updates on relevant guidelines and legislation. Clear communication channels should be established with IT experts, vendors, and other stakeholders for smooth coordination.4 Comprehensive documentation should be developed to ensure compliance with contractual obligations and guidelines. The clinical team should be involved in acceptance testing and discussions, depending on the clinical purpose of the AI system.4 Protocols for data collection and curation should be established, along with the development of standardized validation datasets for performance evaluation.4 A system for monitoring updates to AI systems and models should be implemented, with the CQMP leading new acceptance/commissioning rounds for any updates. Lastly, mechanisms for continuous evaluation and improvement of the CQMP processes should be established, which could involve regular audits, feedback mechanisms from end-users, and incorporating lessons learned from previous rounds.4 Nowadays, major healthcare systems in the US consider their data as immensely valuable assets that require rigorous protection to ensure Health Insurance Portability and Accountability Act (HIPAA) compliance, as well as intellectual property considerations. It can be very difficult for researchers to share clinical data with vendors for development purposes without a significant return being specified to the institution, such as joint intellectual property or substantial grant funding. Instead, these healthcare systems encourage their researchers to commercialize their findings independently, allowing the institution to retain full rights to intellectual property. That said, the realization of federated learning would be a significant advancement. To achieve this, a powerful pre-trained model that would be adaptable to operation on different scales and in various clinical scenarios is necessary. It is plausible that local adaptation may not require substantial computing power or AI expertise. This concept is particularly intriguing and could be beneficial to smaller centers and clinics in underserved areas. However, the primary challenge is the cost. As we become more reliant on AI systems like OpenAI's ChatGPT or Google Gemini, we often overlook the fact that these conveniences come with a hefty price tag, costing billions of dollars to develop and maintain.5 As medical physicists we and other healthcare professionals can anticipate that AI will significantly transform healthcare, improving efficiency, accuracy, and the level of detail that can be extracted from imaging, and methods of therapy. These technological advancements are expected to bring immense value to the field, offering a new horizon in diagnostic and therapeutic capabilities. Yet, we also must recognize that it also introduces potential significant risks and ethical dilemmas. One of the primary concerns is the possibility of bias in AI, which can stem from the training data, the algorithms, or their application, leading to potentially detrimental effects on patient care. As medical physicists, we should acknowledge that the complexity and lack of transparency in AI decision-making processes present obstacles in terms of accountability and rectifying errors and requires greater oversight and responsibility. The integration of AI also has great capacity in redefining the role of medical physicists, impacting education and employment within the field. Addressing these issues necessitates the creation of ethical standards for AI in healthcare, emphasizing transparency, responsibility, and equity, with contributions from diverse stakeholders, including patients, medical professionals, and ethicists.6 Such measures are crucial to ensure the responsible utilization of AI in healthcare, and ultimately serve the best interests of patients and society. We anticipate that continued guidance from our professional societies will be helpful as our collective communities develop methods and approaches that help us learn, adopt, and employ AI responsibly. Advocacy: increase educational initiative, public awareness, and recommending processes at all levels of the clinical workforce, as well as patient engagement. Broadening Perspectives: encourage Interdisciplinary Collaborations that allow medical physicists to work with professionals from other disciplines such as computer science, data science, and biomedical engineering, to gain insights into different perspectives on AI applications in healthcare. This enables medical physicists to provide continuing education and connect the community with research opportunities. Improving Coordination and Communication through creating team collaboration: enhance communication with healthcare professionals, administrators, and patients by clearly defining and articulating the role of medical physicists in AI applications. Promote the sharing of knowledge, as exemplified by creating data repositories through contributions, to further creating the foundation of our understanding and application of AI in the field. We consider the concept of Real AI in our context to be aimed at providing and/or qualifying a ready AI product that has undergone a rigorous QA process, that is free of false additives and biases, with data carefully curated to represent the demographics and be attuned to the needs of the clinic, sourced with proper ingredients, and abiding by laws and regulations that can ensure the product serves the common health needs of patients and benefits the public's interest. What AI 'is' and what it 'is not' is a complex topic that warrants further exploration and understanding, but one vital for comprehension of what utility AI can fulfill in the clinical process, what its advantages and limitations are, and how it can be curated to perform in the clinical scenarios relevant to a particular radiology/radiation oncology practice. Multiple data-analysis algorithms have been created over the course of years, and not all of them qualify as AI.7 What distinction(s) lie in what constitutes AI? One possible interpretation is that AI is a system that can adapt to new data, or a system that generates insights driven by data. AI systems are designed to "learn" and adapt to new data and be stable over the course of introducing data perturbations or employ model adaptation mechanisms. AI systems can adjust the underlying data-processing mechanisms based on the input they receive, which allows them to improve their performance and make more accurate predictions or decisions over time. This is often achieved through techniques such as machine learning, where algorithms are trained on a dataset and then used to make predictions or decisions without being explicitly programed to perform the task.8 Understanding how such datasets are selected, what data needs to be fed into AI model to achieve desired results, and how to prevent common pitfalls and ethical conundrums associated with the use of AI models requires additional training that might yet be lacking in the traditional training of the radiology/radiation oncology adjacent specialists. The scope of involvement of each member of the team when it comes to AI integration into the clinic continues to be determined as the field rapidly evolves. When it comes to the role of medical physicists in conjunction with AI, an open discussion of the exact responsibilities is still ongoing, and feedback is encouraged from all the members of the community. So, what can medical physicists do? They can use AI to enhance quality improvement and safety by analyzing medical data to identify trends, patterns, and outliers.9 This can lead to the identification of areas for improvement or potential safety hazards and help them enter the realm of Responsible AI. AI can also improve diagnostic and therapeutic techniques by enhancing the quality of medical imaging and automating image interpretation.10 Furthermore, AI can help in integrating diagnostics, personalized medicine, and theragnostics by analyzing large datasets to tailor treatment plans to individual patients.11 This can lead to more effective and personalized care. AI can also automate routine tasks in medical physics, such as treatment planning and QA processes, leading to increased efficiency.12 Lastly, AI techniques like machine learning and deep learning can be leveraged for research and development to analyze complex datasets, discover patterns, and develop innovative techniques for disease detection, treatment, and monitoring.13 Whether it involves developing AI-driven solutions like automated segmentation, dose calculations, addressing intricate problems in the clinic, or potentially even contributing to open-source AI initiatives, such activities will empower medical physicists to enhance their skills and make tangible contributions to the advancement of healthcare. Embracing AI not only fosters a sense of accomplishment but also opens doors to the world of `automation' and scaling that will pervade all technologies of the future. The AHAIBC committee is at the center of bringing the medical physicist forward by developing curriculum concepts, bootcamps, and engendering engagement for our society. Integration of AI into the realm of medical physics education is critical, especially considering the potential significance of incorrect AI usage or misapplication. The physicist is responsible for installing and commissioning the AI software, ensuring the modeling is not biased, performing continuing QA on the hospital data and processes, and establishing efficient resource management. Embracing education in AI offers new benefits for medical physicists as it is already revolutionizing various industries and professional practices and we need to be equally prepared. One way to engage and prepare healthcare professionals for the upcoming AI wave is to start with the roots of quality safety and assurance. To do this, we should enable a comprehensive QA program that encompasses all clinical operations related to medical fields including radiology, nuclear medicine, and radiation oncology. Ensuring the safe operation of hardware, software, clinical operation processes and machinery is of utmost importance and one of the most crucial responsibilities of a medical physicist. A Real AI approach can be highly beneficial in achieving the goal of safe clinical implementation. Understanding the potential and limitations of AI serves as a cornerstone for fostering engagement not only within our profession but with other healthcare providers. Continuous learning and participation in hands-on experience are essential components for navigating the complexities of AI applications within healthcare. Collaboration, networking, and exploring AI's purpose and impact are equally vital in this journey. Additionally, some physicists may choose personal projects, embracing challenges in small groups, and actively contributing to AI-focused teams to amplify the motivation and expertise of our field. Insights through personal and collaborative opportunities ultimately provide for and encourage professional growth and innovation within our medical physics field. Some medical physicists may be able to attend specialty meetings and conferences dedicated to AI which further enriches their knowledge base and provides them avenues for fruitful collaboration. There are successful educational programs such as the Radiological Society of North America Artificial Intelligence (RSNA AI)-certificate program.14 Interdisciplinary cooperation and inter-institutional collaboration for AI experts is of paramount importance for integrating AI into medical physicists' practice on a larger scale, and mechanisms enabling this collaboration should be provided to the community. In summary, the authors believe that being prepared for and embracing the changes that AI is already bringing at the current time will benefit our community, healthcare, patient care, and society at large immediately and for the future. We are at a critical juncture, which can be considered a fourth industrial revolution, where AI and automation are applied more broadly. Medical physicists have a pivotal role to play in this revolution. We need to position ourselves at the forefront of 'Real AI' and lead the charge in this exciting new era. It is time for action, and we can take the first steps with potentially just a few ABCs. All authors contributed their efforts in writing and editing this call for action. ChatGPT search engine has been utilized to provide additional background to the subject of matter for illustrative purposes. The authors appreciate members of the Ad. The authors declare no conflicts of interest. The content for this call for action has been edited with the help of large language models ChatGPT and Google NotebookLM.

This trend report is based on an analysis of articles and reviews contain ed in 13 representative sociological journals (approximately 800 titles). General conclusions: the U.S.A. has the undisputed leadership in research, followed by France and the Netherlands. There tends to be a differentia tion and specialisation in studies; generally speaking as much attention is paid to the interior structure of the religious institutions as to the latter's relationships with other social institutions and groups. In the U.S.A. more attention is paid to external relations and they carry out work which relatively is closer to 'real' sociology of religion; in Europe, more emphasis is laid on examining the internal structures and hence more 'social-ecclesiastical' research is carried out. An extensive review of the available literature shows the following items which are worthy of attention: the very interesting discussion of the basic theories behind the sociology of religion and of the socially dif ferentiating effects of membership of a Church in the U.S.A.; the change of emphasis from the question of recruitment to the priesthood to that of the position and role of the clergy at the present time; the two current lines in the sociology of ecclesiastical communities; and some very good studies concerning mixed marriages and the relationship between Church bodies and social stratification in the U.S.A.; the religions of minority groups, jews and negroes; and questions of interecclesiastical tensions and prejudice. Sections where work still needs to be done include: the sociology of the ecclesiastical sytem of education and instruction, the liturgy, and the hierarchy. Moreover interconfessional relations, the relationship of denominations to social institutions — instruction and education, political structures, mass-communication — should receive more attention. The review ends with a division of the historico-sociological studies according to theme, and the formulation of some general proposals.

In pre-communist times, the Temple of Universal Rescue (Guangji Si) functioned as both a nationally prominent Buddhist temple and a center of local community activity. Following the rise to power of China’s communist party, however, the temple was closed as a site of popular worship. While its prominent status spared the temple from destruction during the most oppressive years of the Maoist regime, it did not reopen to the public again until the early 1990s. Since that time, the temple has become a lively civic space frequented by a growing number of lay Buddhist converts. However, it no longer functions as a center for community activity for the neighborhood surrounding it; because the number of temples in the surrounding area remains scant compared to imperial times, its worshippers come from all over the Beijing area. Over the last ten years, many of the old neighborhoods whose residents once frequented the temple have been demolished to make room for a new subway station—a project that erodes what remains of the temple’s traditional role in the surrounding neighborhood while making it more accessible to its present population of geographically dispersed worshippers. At the same time, the population of temple-goers has changed from syncretic worshippers of both local and national gods within a circuit of neighborhood temples in the pre-communist period to a more self-consciously sectarian community of lay Buddhists. While the orientation of pre-communist worshippers was located specifically in the local place, temple worshippers in the post-Mao period are attracted to the non-locative vision of Buddhism and, concomitantly, see little significance to particular temples as place-defined spaces. In this way, transformations of space at a single temple site in Beijing highlight complex changes in urban Chinese religiosity from locally based religiosity to translocal and yet exclusivist religious orders.

The integration of artificial intelligence into the system of higher education represents a turning point in the process of learning and teaching. The development of artificial intelligence has opened the way to personalized training, automation of administrative tasks and the introduction of innovative training methods. The purpose of the study was to analyze the practical aspects of using artificial intelligence in higher education institutions of Ukraine. It was determined that artificial intelligence is an organized set of information technologies, which makes it possible to perform complex complex tasks. There are three main categories of artificial intelligence: narrow-spectrum artificial intelligence, or Artificial Narrow Intelligence, general artificial intelligence, or Artificial General Intelligence, and artificial superintelligence, or Artificial Super Intelligence. The main educational services provided by artificial intelligence in institutions of higher education are the development and conduct of lectures, seminars and practical classes; teacher counseling; creation of educational programs and electronic courses; development of tasks and simulation of their solution; conducting various educational events; evaluation of the works of education seekers. Some examples of the use of artificial intelligence, in particular chatbots, in the higher education of Ukraine are analyzed and their potential for improving the educational process and forming professional skills is emphasized. An example of the use of GPT-3.5 in the Luhansk Educational and Scientific Institute for teaching foreign languages is presented. Such applications based on artificial intelligence as Thinkster and Duolingo and the main aspects of their use by students of higher education are characterized. Recommendations are provided for the successful implementation of artificial intelligence technologies in higher education.

The integration of artificial intelligence into the system of higher education represents a turning point in the process of learning and teaching. The development of artificial intelligence has opened the way to personalized training, automation of administrative tasks and the introduction of innovative training methods. The purpose of the study was to analyze the practical aspects of using artificial intelligence in higher education institutions of Ukraine. It was determined that artificial intelligence is an organized set of information technologies, which makes it possible to perform complex complex tasks. There are three main categories of artificial intelligence: narrow-spectrum artificial intelligence, or Artificial Narrow Intelligence, general artificial intelligence, or Artificial General Intelligence, and artificial superintelligence, or Artificial Super Intelligence. The main educational services provided by artificial intelligence in institutions of higher education are the development and conduct of lectures, seminars and practical classes; teacher counseling; creation of educational programs and electronic courses; development of tasks and simulation of their solution; conducting various educational events; evaluation of the works of education seekers. Some examples of the use of artificial intelligence, in particular chatbots, in the higher education of Ukraine are analyzed and their potential for improving the educational process and forming professional skills is emphasized. An example of the use of GPT-3.5 in the Luhansk Educational and Scientific Institute for teaching foreign languages is presented. Such applications based on artificial intelligence as Thinkster and Duolingo and the main aspects of their use by students of higher education are characterized. Recommendations are provided for the successful implementation of artificial intelligence technologies in higher education.

The integration of artificial intelligence (AI) into mental health care is not merely a technological shift but a societal response to perceived challenges within human-centric care delivery. This mixed-methods study critically examines this transition by analyzing high-engagement public discourse on X (formerly Twitter) from January 1 to September 1, 2025 ( N = 496 posts, English/Spanish). The findings reveal a central paradox: while public discourse shows profound frustration with traditional providers—citing prohibitive costs and inefficacy (61%−65% negative sentiment)—its embrace of AI is deeply ambivalent. Users value AI primarily for its non-human qualities of accessibility, anonymity, and scalability (53%−58% positive sentiment), yet simultaneously critique it for its failure to replicate the quintessentially human trait of empathy. Spanish-language discourse further illuminates this, positioning AI's anonymity as a direct countermeasure to cultural stigma. Interpreting these findings through a critical application of the Unified Theory of Acceptance and Use of Technology (UTAUT), this paper argues that AI is not being adopted as a therapeutic equivalent, but as a pragmatic, if imperfect, tool to navigate the deficiencies in the “facilitating conditions” of traditional care. This dynamic, however, presents a significant risk: that AI becomes a technological patch for deep-seated systemic problems, potentially delaying fundamental healthcare reform. This study offers a novel, critical perspective on AI's role, urging a shift from designing AI that mimics empathy to creating hybrid systems that leverage AI's strengths ethically and transparently, without absolving the human system of its duty to care.