ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ ДЛЯ ОЦІНКИ ESG-ПОКАЗНИКІВ У HR-ПРАКТИКАХ

Artificial Intelligence (AI) is changing Human Resource (HR) practices in the digital age. From hiring to employee engagement and performance evaluation, AI tools and platforms help HR departments become more strategic and efficient. This paper examines how AI is reshaping traditional HR models by improving personalization, trust, and operational efficiency. Through surveys and a literature review, the study explores the role of AI- powered influencers in HR, evaluates ethical concerns regarding AI use in HR, and identifies factors that determine AI credibility and influence. The findings suggest that AI’s effectiveness in HR largely relies on perceived authenticity, transparency, and employee relatability. The research concludes with practical tips for integrating AI into HR strategies responsibly.

Traumatic brain injury (TBI) is a leading cause of disability and death globally, presenting significant challenges for diagnosis, prognosis, and treatment. As healthcare technology advances, artificial intelligence (AI) has emerged as a promising tool in enhancing TBI rehabilitation outcomes. This literature review explores the current and potential applications of AI in TBI management, focusing on AI's role in diagnostic tools, neuroimaging, prognostic modeling, and rehabilitation programs. AI-driven algorithms have demonstrated high accuracy in predicting mortality, functional outcomes, and personalized rehabilitation strategies based on patient data. AI models have been developed to predict in-hospital mortality of TBI patients up to an accuracy of 95.6%. Furthermore, AI enhances neuroimaging by detecting subtle abnormalities that may be missed by human radiologists, expediting diagnosis and treatment decisions. Despite these advances, ethical considerations, including biases in AI algorithms and data generalizability, pose challenges that must be addressed to optimize AI's implementation in clinical settings. This review highlights key clinical trials and future research directions, emphasizing AI's transformative potential in improving patient care, rehabilitation, and long-term outcomes for TBI patients.

This study aims to explore the integration of artificial intelligence (AI) in human resource (HR) practices, focusing on its role in talent acquisition. By conducting a comprehensive literature review using a qualitative, library research method, this paper examines the current trends, challenges, and opportunities associated with AI adoption in HR, particularly recruitment. The findings highlight that AI has the potential to significantly improve recruitment efficiency, reduce biases, and enhance decision-making processes in HR. However, the study also reveals key ethical concerns related to algorithmic biases and transparency, which can negatively impact fairness in hiring practices. Furthermore, the research emphasizes the importance of upskilling HR professionals to effectively manage AI tools and integrate them into broader organizational strategies. The paper concludes by discussing the need for transparent, ethical AI systems and suggests that further research is necessary to evaluate the long-term effects of AI on HR practices and organizational culture. This study contributes to the ongoing discourse on AI in HR by providing insights into the implications of AI-driven recruitment and offering recommendations for future research in the field.

The paper deals with the use of Artificial Intelligence (AI) tools in formal education. The first part of the paper presents a theoretical framework, defining key concepts and examining the current state of AI use in education in Slovakia and other countries. This analysis provides a deeper insight into current trends and challenges in the implementation of AI in educational processes. The second part of the article focuses on a systematic search for AI tools that have the potential to be used in the educational process. The given tools are subjected to a content analysis to assess their applicability and benefits in the educational context. The results of this paper have the potential to contribute significantly to a better understanding of the benefits of AI in the context of formal education. It can also be used as a basis for developing effective strategies for implementing AI in educational processes and enriching research using advanced technologies in pedagogy.

This study explores the potential applications of artificial intelligence (AI) in the context of special education. The discussion focuses on those areas of special needs education in which the implementation of AI is already supported by empirical evidence and practical experience. Accordingly, the study examines the potential uses of AI in the following domains: 1) personalized learning and adaptive educational methods; 2) the development of communication and social skills; 3) assistive technologies and accessibility supported by AI; 4) the role of AI in early diagnosis and intervention; and 5) AI-based support for teachers and assistance in designing and implementing individual development plans (IEPs).

This chapter explores the practical applications of artificial intelligence (AI) tools in enhancing business efficiency, demonstrating how AI-driven solutions can streamline operations, optimize decision-making, and foster innovation across various industries. It provides a structured guide to understanding AI-powered business applications, including machine learning algorithms, predictive analytics, automation tools, and AI-driven customer engagement systems. Through real-world case studies, the chapter highlights how businesses of all sizes—from small startups to large corporations—can leverage AI to reduce costs, improve productivity, and gain competitive advantages. The discussion includes scalable AI solutions, debunking the myth that AI is only accessible to large enterprises. The chapter also examines sector-specific AI applications, showcasing AI’s impact in finance, healthcare, marketing, retail, manufacturing, and logistics. Additionally, this chapter addresses the ethical and societal implications of AI adoption, including privacy concerns, algorithmic bias, workforce transformation, and regulatory considerations. It provides a practical roadmap for AI implementation, guiding business owners in identifying the right AI tools, overcoming challenges, and ensuring responsible AI integration. By the end of this chapter, readers will have a clear understanding of the AI landscape, including how to strategically implement AI tools to maximize business efficiency while maintaining ethical integrity.

Artificial intelligence (AI) is rapidly transforming clinical practice, necessitating that neurology educators prepare trainees for this shift. This review outlines how AI tools are currently being trialed for integration into clinical care and demonstrates potential applications within neurology education. We highlight practical models for curriculum development from other specialties and underscore the need for structured, competency-driven approaches. Ultimately, integrating AI education into neurology training is essential to equip future clinicians for a healthcare system increasingly shaped by AI.

The author of the article considers the main directions of application of artificial intelligence in financial management. The article reviews the main scientific sources and positions of practitioners on the strategic prospects of applying artificial intelligence tools in financial management. The research is based on the methodology of systematic review and synthesis of scientific sources and industry reports, which is the most justified for this area of ​​research. The article analyzes key indicators of the artificial intelligence market in the financial sector according to data from leading analytical companies, which demonstrates the impressive growth dynamics of the global artificial intelligence market by 2030-2034. The author systematizes the main challenges faced by the implementation of artificial intelligence in financial management, such as: ethical and regulatory lags, scaling problems, environmental impact and data bias. Based on the analysis of market trends, the evolutionary path of using artificial intelligence tools has been determined, which indicates the growth of market maturity, which is moving from the stage of "technology for the sake of technology" to the stage of "solutions for the sake of business tasks". The author identified strategic vectors for the implementation of artificial intelligence technology in financial management, among which three main ones were conceptually identified: increasing operational efficiency, risk management and cybersecurity, personalization and customer experience. Specific areas of application in which the implementation of artificial intelligence has the most significant results in financial management have also been systematized: fraud detection, creditworthiness assessment, forecasting needs, democratization of financial services, development of virtual assistants, etc. The article pays attention to comparing the main models of generative artificial intelligence and their possibilities of use as tools in financial management, in particular, advantages, limitations and the most optimal type of use.

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

In today’s evolving world of work, Artificial Intelligence (AI) has emerged not merely as a technological tool but as a catalyst for rethinking how jobs are structured and performed (Bankins et al., 2024; Parker & Grote, 2022). Across diverse job families and organizational contexts, tools like generative AI (GAI) go beyond mere instruments for data analysis or structuring and can adopt increasingly far-reaching roles through rapid progress in generating new graphic or textual content or algorithmic decision making (Ferràs- Hernández, 2018; Walsh et al. 2019). Central to this symposium is the recognition that this changing nature of work due to digitalization, propelled by AI and related tools, calls for a fresh look at established theories of job design and strategies for human resource management. Traditionally, research has illuminated how fostering characteristics like autonomy, skill and task variety, or adequate demands affect employee motivation, satisfaction, and well-being and thereby further organizational outcomes (Oldham & Fried, 2016; Parker et al., 2017). Yet the introduction of AI – particularly generative AI – adds complexity: Who performs which tasks? How are decisions made, and by whom? Which skills remain uniquely human, and which may be augmented or even entirely adopted by AI? Such questions demand a more nuanced and strategic approach to designing work in the future that balances efficiency and innovation with psychological and ethical considerations. The four presentations in this symposium aim to contribute to theory building on how AI affects work in terms of work design, the interplay of employees and AI use, and work outcomes. In particular, they highlight potential risks or unique challenges when working with AI (e.g., in terms of less enriched work, lowered autonomy levels, threats to job identity or risks due to disclosure of use) and path the way for interventions to achieve benefits of such technologies at work. In detail, the empirical studies in this symposium illuminate these issues at three interconnected levels: 1. Task-Level Insights: Two contributions focus on the more granular task characteristics (e.g., autonomy, skill variety) in modern, AI-enhanced work contexts. They demonstrate how AI use influences the allocation of specific tasks when designing work roles and how the perception of task characteristics changes when AI is used in a decision-making task. 2. Job- and Identity-Level: One paper zooms out from individual tasks to jobs as a whole and explores how AI-driven change might disrupt employees’ job identity. This lens demonstrates how task changes cascade into bigger questions of role definition and identity. 3. Organizational Context: Finally, one presentation adopts the broadest perspective and investigates managerial and organizational factors that might shape employees’ willingness to disclose and ethically integrate AI tools. Taken together, these presentations point toward a future in which AI’s transformative potential hinges on thoughtful coordination across tasks, roles, and organizational systems. By combining established frameworks with new insights drawn from (quantitative and qualitative) surveys and experiments conducted in Germany, Austria, the United States, the United Kingdom, and Australia, these contributions illustrate how AI can enrich, rather than diminish, work and thereby strengthen human-centered organizations. By focusing on variables at different levels, the studies path the way for applications in practice, emphasizing the importance of individual approaches (e.g., training for job design, supporting self-efficacy), or on the organizational level (e.g., aligning HR practices, develop clear and supportive organizational policies and guidelines for the use of AI at work). Investigating the Influence of Work Design Perspective and AI Integration on Enriched Job Design Author: Tanja Bipp; Heidelberg University Author: Thea Ebert; Aalen University Collaborative Intelligence: Integrating Generative AI in Knowledge Work and its Impact on Job Design Author: Sabina Hodzic; University of Graz Author: Simon Grob; University of Graz Author: Bettina Kubicek; University of Graz Job Identity as Starting Point for SHRM in AI-Driven Organizational Change Author: Sophie Berretta; Ruhr University Bochum Author: Annette Kluge; Ruhr University Bochum Silent Adoption: Understanding the Drivers of Non-Disclosure of Generative AI Tools by Employees Author: Fangfang Zhang; Curtin University - Perth

Purpose: This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.Methods: A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.Results: The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.Conclusion: The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools.

Artificial Intelligence (AI) has emerged as a transformative technology, playing a critical role in advancing sustainable development. This paper examines the intersection of AI tools and sustainable development, focusing on key areas such as environmental protection, resource management, and societal well-being. By leveraging AI for climate change mitigation, biodiversity conservation, and waste management, we can achieve significant environmental benefits. In resource management, AI enhances water resource management, agricultural practices, and sustainable urban planning. Additionally, AI contributes to societal well-being through improved healthcare, personalized education, and effective disaster response. Despite its potential, the implementation of AI in sustainable development faces challenges, including data privacy concerns, algorithmic bias, and the need for adequate infrastructure and expertise. Addressing these challenges is crucial for maximizing AI’s positive impact on sustainability. This paper will cover three main areas where AI impacts sustainable development: environmental protection, resource management, and societal well-being. Each section delves into specific applications, providing case studies and examples to illustrate AI's role. This paper provides a comprehensive overview of current AI applications in sustainable development, discusses their potential impacts, and explores the ethical and practical considerations involved. By doing so, it aims to contribute to the ongoing discourse on how AI can be harnessed to create a more sustainable and equitable future.

This paper discusses the integration of artificial intelligence (AI) tools in education, delineating their potential to transform pedagogical practices alongside the challenges they present. Generative AI models like ChatGPT, had a disruptive impact on teaching and learning, due to their ability to create text, images, and sound, revolutionizing educational content creation and modification. However, nowadays the educational community is polarized, with some embracing AI for its accessibility and efficiency thus advocating it as an indispensable tool, while others cautioning against risks to academic integrity and intellectual development. This document is designed to raise awareness about AI tools and provide some examples of how they can be used to improve education and learning. From an educator’s perspective, AI is an asset for curriculum development, course material preparation, instructional design and student assessment, while reducing bias and workload. For students, AI tools offer personalized learning experiences, timely feedback, and support in various academic activities. The Turkish Biochemical Society (TBS) Academy recommends educators to embrace and utilize AI tools to enhance educational processes, and engage in peer learning for better adaptation while maintaining a critical perspective on their utility and limitations. The transfer of AI knowledge and methods to the teaching experiences should complement and not replace the educator’s creativity and critical thinking. The paper advocates for an informed embrace of AI, AI fluency among educators and students, ethical application of AI in academic settings, and continuous engagement with the evolving AI technologies, ensuring that AI tools are used to augment critical thinking and contribute positively to education and society.

Artificial intelligence (AI) is increasingly utilized in interventional cardiology (IC) and holds the potential to revolutionize the field. We conducted a global, web-based, anonymous survey of IC fellows and attendings to assess the knowledge and perceptions of interventional cardiologists regarding AI use in IC. A total of 521 interventional cardiologists participated in the survey. The median age range of participants was 36 to 45 years, most (51.5%) practice in the United States, and 7.5% were women. Most (84.7%) could explain well or somehow knew what AI is about, and 63.7% were optimistic/very optimistic about AI in IC. However, 73.5% believed that physicians know too little about AI to use it on patients and most (46.1%) agreed that training will be necessary. Only 22.1% were currently implementing AI in their personal clinical practice, while 60.6% estimated implementation of AI in their practice during the next 5 years. Most agreed that AI will increase diagnostic efficiency, diagnostic accuracy, treatment selection, and healthcare expenditure, and decrease medical errors. The most tried AI-powered tools were image analysis (57.3%), ECG analysis (61.7%), and AI-powered algorithms (45.9%). Interventional cardiologists practicing in academic hospitals were more likely to have AI tools currently implemented in their clinical practice and to use them, women had a higher likelihood of expressing concerns regarding AI, and younger interventional cardiologists were more optimistic about AI integration in IC. Our survey suggests a positive attitude of interventional cardiologists regarding AI implementation in the field of IC.

The paper presents the results of the research study conducted on students at the Faculty of Humanities and Social Sciences in Zagreb (FHSS), Croatia, at the end of 2024, focusing on their experiences with artificial intelligence (AI) tools, and its use in academic activities, students' trust in AI, and the future use of AI by students. While over 81 % of respondents reported using AI tools for study purposes, more than 89% did not receive any education about AI and expressed a need for such education. Generative AI tools for text creation are the most frequently used type of AI tools, but students still require additional knowledge to use them effectively. AI tools are also utilized for seeking advice, finding information, being available 24/7, and assisting in writing student papers, though not yet on a massive scale. Teachers remain the most trusted source for information and for recommending AI tools. Despite known shortcomings of AI tools such as content errors, potential costs and ethical or privacy concerns, students expressed their intention to continue using AI tools in the future.