HUMAN RESOURCE MANAGEMENT AND ARTIFICIAL INTELLIGENCE: TRANSFORMATIVE EFFECTS AND FUTURE PROSPECTS

This study explores the transformative role of Artificial Intelligence (AI) in Human Resource Management (HRM), focusing on theoretical modeling of its functional impacts, ethical constraints, and strategic implications. The research aims to identify key opportunities and challenges associated with AI integration into HR practices. Using a conceptual framework and mathematical formalism, this study simulates AI integration across recruitment, performance appraisal, and employee engagement. A constraint-aware optimization model is employed to assess ethical considerations, while scenario-based evaluations illustrate domain-specific applications without relying on empirical datasets. AI is projected to reduce recruitment workload by up to 55%, increase personalization in learning and development by 30–50%, and improve predictive succession planning accuracy by 25–30%. Ethical utility modeling demonstrates a trade-off between AI performance and transparency, emphasizing the need for embedded governance frameworks. The study provides a strategic blueprint for responsible AI adoption in HRM, guiding organizations in balancing efficiency with ethical compliance. This research contributes a novel AI-HRM Integration Framework and formalized models to the field, addressing existing gaps in empirical and ethical considerations.

Examining the impact of artificial intelligence on employee performance in the digital era: An analysis and future research direction

Artificial Intelligence (AI) is revolutionizing Human Resource (HR) management by enhancing talent acquisition, employee engagement, performance evaluation, and workforce planning. This study explores AI-powered talent management strategies, analysing their effectiveness, advantages, and challenges. By examining AI applications in HR, this paper highlights how organizations leverage AI-driven insights to improve efficiency, reduce biases, and enhance decision-making processes. Furthermore, the study delves into ethical concerns and the future of AI in talent management. Keywords: Artificial Intelligence, Talent Management, Human Resource Management, AI-Driven Recruitment, Employee Engagement, Performance Evaluation, Workforce Planning, Ethical AI

Can technology become a strategic partner in human resource management? Traditionally, the HR function was perceived primarily as an administrative process relying on manual operations. However, the digital era, as in many other domains, has transformed the nature of HR management. The growing pace of global change has compelled organizations to adapt to technological innovation. The new era has fundamentally reshaped organizational approaches to work processes, creating an environment in which digital tools play a central role. Technological development has brought forth the need for process optimization. Consequently, HR management practices have gradually shifted from paper-based administrative operations toward a digital ecosystem. The introduction of Human Resource Information Systems (HRIS) has automated numerous HR functions, increasing efficiency and facilitating data-driven decision-making. Over time, the logical continuation of HRIS evolution became the integration of Artificial Intelligence (AI), which further enhanced and diversified HR processes. The synergy of these technologies has elevated HR management to a new analytical and data-driven dimension. With the growing economic and organizational influence of AI, its integration has become a transformative force across industries. The primary aim of this article is to explore the strategic significance of automation and artificial intelligence in human resource management. It examines the development of HRIS systems, their functional capabilities, and algorithmic integrations that ensure operational efficiency and data-based decision-making. Additionally, the paper presents international case studies illustrating the practical applications of HRIS and AI technologies in global organizations. Keywords: Artificial Intelligence (AI), Human Resource Information System (HRIS), automation

The integration of Artificial Intelligence (AI) and digital connectivity can revolutionize material and human resources management in secondary schools. The study focused on human and material resources management in education in Nigeria and its impact on students’ academic performance in secondary schools, in Ebonyi State, Nigeria. Subjects for the study were two hundred (200) students and hundred (100) teachers randomly selected from five secondary schools in Afikpo, Ohozara, and Ezza South Local Government Areas of Ebonyi State. Questionnaire was used to collect relevant data for the study. Pearson Product Moment Correlation was used to analyses the formulated hypotheses. The findings of the study revealed that there was significant positive relationship between human resources management and students’ academic performance (r = 0.814; p < 0.05). Also, significant relationship exists between material resources management and students’ academic performance (r = 0.629; p < 0.05). The implication is that effective material resource management may bring about improvement in student’s academic performance. While it was recommended that effective practice of both human and material resources is to be well implemented for effective performance of students at any level of educational system in Nigeria.

PurposeDrawing on the Technology-Organization-Environment (TOE) framework, this study investigates how Public Administrations (PAs) can enhance organizational resilience through the integration of Artificial Intelligence (AI) in Human Resource Management (HRM) practices.Design/methodology/approachAdopting a qualitative and inductive research design, the study combines the analysis of policy and strategy documents with data from four exploratory focus groups and 43 semi-structured interviews with public managers.FindingsThe study identifies four mechanisms through which AI enhances resilience in PAs. First, the integration of AI into HRM improves recruitment, training, and performance management. Second, the development of key managerial competencies—including strategic thinking, change management, and emotional intelligence—supports effective AI adoption. Third, overcoming cultural and structural barriers, such as resistance to change and digital skill gaps, enables transformation. Fourth, the use of AI-driven tools strengthens crisis response, operational efficiency, and employee engagement. Together, these mechanisms foster adaptability and long-term resilience, highlighting the strategic role of AI-enabled HRM in the public sector.Practical implicationsThe findings offer practical recommendations for PA leaders, emphasizing the importance of AI-enabled HRM systems, continuous managerial development, and strategic actions to overcome implementation barriers. These insights provide a comprehensive roadmap for enhancing adaptability, efficiency, and resilience in public sector organizations—encompassing smart investments, leadership development, a supportive work culture, ethical governance, and infrastructure modernization.Originality/valueThis study advances the literature on AI adoption in PA by offering a nuanced, empirically grounded perspective on how AI-driven HRM practices and managerial competencies interact to shape organizational resilience.

In an era where artificial intelligence (AI) is revolutionizing business paradigms, this study delves into the intricacies of AI-driven business models, offering a nuanced understanding of their emergence, evolution, and impact on traditional business strategies. This scholarly inquiry aims to dissect the role of AI in reshaping business models, highlighting the interplay between technological innovation and business strategy. The study meticulously examines the integration of AI into various business facets by employing a systematic and thematic analysis of a diverse range of literature, including academic journals, industry reports, and case studies. This methodological approach facilitates a comprehensive understanding of AI's role in business innovation, addressing both the opportunities and challenges it presents. The findings reveal that AI-driven business models are characterized by enhanced operational efficiency, data-driven decision-making, and customer-centric approaches. These models signify a transformative shift from conventional business strategies, demanding a reevaluation of leadership roles and ethical considerations in the digital age. The study identifies key challenges in AI implementation, such as technical complexities and ethical dilemmas, while uncovering AI's vast opportunities for business growth and competitive advantage. Conclusively, the study recommends a balanced approach to AI integration, emphasizing the need for ethical AI practices, continuous adaptation, and a synergy between AI capabilities and human insights. It advocates for business leaders to embrace AI not just as a technological tool, but as a catalyst for sustainable and innovative business growth. This scholarly work contributes significantly to the discourse on AI in business, providing a foundational framework for future research and practical application in AI-driven business innovation. Keywords: Artificial Intelligence, Business Models, Digital Transformation, AI Integration, Leadership in AI, Ethical AI Practices.

The integration of artificial intelligence (AI) into human resource management (HRM) is a pivotal factor in the transformation of conventional HR practices. The increasing complexity of HR processes, in conjunction with the mounting necessity for personalisation, efficiency and adaptability, underscores the importance of leveraging AI-based solutions within contemporary organisations. The objective of this research is to explore the role of AI in HRM, analyse its impact on adaptive management approaches, and identify key factors that impact the successful implementation of AI in HR processes. The study uses a mixed approach, combining literature review, empirical data collection based on interviews with companies of different sizes, and correlation analysis. The analysis focuses on the adaptability of AI-based HR systems, their impact on employee engagement, productivity and decision-making processes. Special attention is paid to ethical issues such as algorithmic bias and transparency, as well as organisational barriers that may prevent the implementation of AI. The findings of the research demonstrate that the implementation of AI technology has the potential to enhance the efficiency of HR management practices. This enhancement is achieved through the optimisation of recruitment processes, the creation of personalised learning pathways, the facilitation of real-time performance evaluation, and the cultivation of a culture that fosters proactive career development. Adaptive AI-powered HR systems enable organisations to respond expeditiously to market changes, optimise talent management, and minimise operational risks. The study demonstrates that, while SMEs exhibit greater flexibility in implementing AI solutions, large corporations encounter structural and managerial challenges that necessitate strategic adjustments to facilitate effective integration of AI. The practical significance of the study lies in its recommendations for organisations seeking to implement adaptive AI-based HR models. It provides insights on how to optimise the use of AI for talent management, improve HR efficiency and address ethical considerations.

This research reflects on the evolving Artificial intelligence (AI) technology in the creative advertising industry. With emphasis on the concerns in the advertising space, where AI is underway in threatening the novelty of originality in creative ideation harnessed by human creativity. AI technologies such as OpenAI GPT for audio production and StyleGAN for photo realistic generation is starting to replace human creation for creative work. The technology has convincing factors in benefit where the automation reduces time with speed and a more economical for production costs. The creativity in the advertising industry stands vulnerable with forthcoming AI technology’s impact and in contrast to the approach towards the support for inevitable innovation. Synthesised results of advertising materials, dominated by generative adversarial networks is garnering popularity, with the convincing content currently accepted by marketers. This thesis has three focus areas: keeping the balance with creativity with AI integration into creative workflows, debating the novelty of original creative mastery versus AI technology which is dependent on data to develop creative outputs and exploration of how AI collaborated with creative professionals enables efficacy of campaigns. As AI continues to be a threat to the novelty of originality, this will need further understanding on societal and economic implications. This research serves to evaluate further AI’s role in parameters for enhancing and transforming the creativity in the advertising paradigm. Ultimately, to assess the acceptance or resistance for the future.

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.

Objective : To analyze foreign experience in the use of artificial intelligence (AI) in oncology, as well as examine current advances in AI and its impact on clinical practice. Material and methods. A systematic review of foreign literature was carried out, current AI programs and technologies in oncology were analyzed. The total number of identified records via the PubMed/MEDLINE search was 7680. After publication selection in accordance with the PRISMA guidelines, 32 studies that met all criteria were randomly included in the review and formed the basis for the analysis. Results . AI demonstrates high efficiency in cancer diagnostics, including early detection of tumors, analysis of medical images and pathological data. A literature review on the use of AI models in oncology revealed exponential growth from 2010 to 2022, confirming the active development of the field. Diagnostic and treatment reports generated by the AI technologies indicated a comparable level of accuracy to that of experienced oncologists; they also revealed the ability to improve clinical outcomes. Furthermore, the introduction of AI stimulates the development of personalized treatment, increased patient adherence to therapy and optimization of the work of medical organizations. AI’s impact was revealed on physicians (reduced diagnostic and treatment errors), patients (personalized support), and hospitals (smart management systems). Conclusion . AI is becoming an integral part of modern oncology, offering new opportunities to improve diagnostics, treatment, outcome prediction, and patient support. A study of the dynamics of publications and AI models indicates that the use of AI in clinical oncology is rapidly developing, opening up new prospects for the creation of smart hospitals, simplification of medical data exchange, development of personalized medicine, and improvement of the quality of patient care. The integration of AI with emerging technologies such as wearable devices and multimodal analysis promises to revolutionize oncology practice.

Artificial intelligence (AI) technology is gradually integrating into the entire process of green supply chain management (GSCM), providing a systematic solution for enterprises to improve productivity and performance. This paper focuses on Chinese manufacturing enterprises, aiming to explore the multi-factor synergistic mechanism influencing differences in GSCM levels from a temporal perspective under the drive of AI. Based on 2019–2023 panel data of enterprises, this paper innovatively integrates the random forest algorithm with dynamic qualitative comparative analysis (QCA) to reveal the configurational effects of technological, organizational, and environmental factors in enterprises’ GSCM practices. The findings demonstrate that no single factor is a necessary condition for enterprises to implement GSCM; configurational analysis identifies two driving models: “AI technology innovation-driven (Configuration 1 and Configuration 2)” and “strategic resource-driven (Configuration 3)”; Configuration 1 combines research and development (R&D) investment and green awareness among executives with the enabling role of government subsidies; Configuration 2 couples R&D Investment with strong funding capacity, again facilitated by the presence of government subsidies; Configuration 3 combines AI technology adoption and green awareness among executives, supported by the necessary funding capacity and government subsidies. Additionally, inter-group analysis reveals no significant temporal effect among configurations but shows phased evolutionary characteristics. This paper has thoroughly explored the complex paths for enhancing GSCM of manufactory enterprises under the influence of AI. It is recommended that the government refine and strengthen targeted subsidy policies to better support the adoption and integration of AI in advancing GSCM within the manufacturing sector. Concurrently, manufacturers must align technology, organizational structure, and external factors, specifically through core AI technology improvements, enhanced executive green awareness, and the mobilization of government and external funding. These advancements have led to high-level GSCM within enterprises, allowing them to achieve high-quality and sustainable development.

Technical Education (TE) serves as a critical component for fostering economic development by equipping individuals with the necessary skills to thrive in various industries. Meanwhile the rapid advancement of technology, particularly in the realm of Artificial Intelligence (AI), has created a pressing need for educational systems worldwide to adapt and evolve. As the global economy increasingly relies on AI technologies, it is imperative for TE programmes to align their curricula with these advancements to equip students with relevant skills but there seems to be scarcity of studies that dwell on integration of AI in TE and there is the need to assess the role played by artificial intelligent in repositioning the programme. Therefore, this paper focused on the assessment of the role of Artificial Intelligence in reposition of Technical Education (TE) for economic development in Nigeria. To achieve the broad aim, this study identifies existing challenges, and highlights the transformative potential of integrating AI into TE. The population of the study was 300 NCE students of Kano Federal University of Education. A total enumeration sampling technique was applied because the population is of manageable size. A validated 15 items questionnaire, designed by the researcher on four point scale ranging from strongly agree (4) – Strongly Disagree (1) was used for data collection. Reliability of the instrument was ensured using split-half reliability technique which yielded a cumulative coefficient of 0.79. Data collected were analyses using mean and standard deviation to answer the research questions. The findings revealed that there is need to integrate AI into the current state of TE; the major challenges and barriers that hinder AI integration in TE is unavailability of uniform content to serve the AI application and that introduction of AI into the curriculum of TE will contribute to the advancement of Nigerian economic development. There is an urgent need for substantial investment in AI-related infrastructure within TE institutions to providing access to AI tools and technologies and TE programmes should be updated to include AI-related courses and concepts.

Purpose of the study: The integration of artificial intelligence (AI) into human resource management (HRM) has transformed organizational approaches to talent acquisition, employee development, and decision-making. This study aims to bridge the gap in understanding the implications and challenges of AI adoption in HRM. It explores the current state of AI implementation, identifies automated HRM tasks, and examines obstacles to effective integration. While existing literature emphasizes the benefits of AI in HRM, critical challenges remain underexplored, necessitating further investigation. Need for the study: With rapid technological advancements, organizations increasingly adopt AI in HRM practices. However, there is a lack of comprehensive understanding of the implications and challenges associated with this integration. This study focuses on assessing the extent of AI adoption, identifying automated HRM tasks, and addressing the challenges faced during implementation. Existing research predominantly highlights AI’s benefits, often neglecting its critical concerns, emphasizing the need for further exploration. Methodology: A quantitative research approach was adopted, collecting data through structured questionnaires distributed to 102 working professionals across various organizations in Bangalore using convenience sampling. Findings: The findings emphasize AI’s critical role in reshaping traditional HR practices, enhancing efficiency, and supporting strategic decision-making processes. Practical implications: The study findings give an insight into the opportunities and challenges an organization faced while integrating AI into its HRM practices.

This study aims to systematically analyze the educational issues and challenges arising from the rapid diffusion of artificial intelligence (AI) technologies in secondary education and to derive implications for their future application. To this end, the study synthesizes findings from international and domestic research literature, policy documents, and empirical studies to examine the impacts of AI adoption across multiple dimensions of secondary education, including curricular structure, instructional practices, performance-based assessment, student guidance and counseling, teachers’ professionalism and identity, teachers’ work structure, educational inequality, school facilities and infrastructure, and educational governance. The analysis indicates that AI can serve as an effective cognitive trigger during the exploratory and expansion phases of learning by supporting information access and idea generation. However, when AI intervenes in core stages such as concept formation, value judgment, and meaning construction, it poses a significant risk of weakening students’ cognitive agency and undermining teachers’ educational judgment. In particular, in the domain of performance-based assessment, AI use tends to reinforce outcome-oriented learning, thereby distorting the fundamental purpose of process-oriented evaluation. In student guidance and counseling, the findings further demonstrate that AI technologies cannot replace relational, empathetic, and trust-based educational interventions. Moreover, while AI holds the potential to enhance teachers’ professional capacity by supporting instructional design and learning diagnosis, the analysis reveals a parallel risk: under technology-driven operational models, teachers may be reduced from educational decision-makers to system operators. Based on these findings, this study argues for a paradigm shift in AI integration—from technology-centered implementation to education-centered judgment. It emphasizes the principle that AI should function as a tool for triggering learning processes, while educational judgment and responsibility must remain firmly with teachers. This principle should be institutionally embedded across curriculum design, assessment systems, school operations, and educational governance.