Editorial Volume 51 Issue 1

Introduction. One of the trends of global importance is the artificial intelligence (AI) and its innovations. One of such innovations has become emotional artificial intelligence (emotional AI/AI), it is called a revolutionary technology that can identify human emotions, process them in a timely manner and react “properly”. Experts consider emotional AI as an instrument that provides emotionally-oriented human–machine communication. The article discusses the specifics of emotional AI, achievements, potential opportunities, development prospects.Methodology and sources. The methodology of philosophical, socio-psychological, comparative and interdisciplinary approaches is used. The sources used in the article are: special literature of domestic and foreign authors (B. Goertzel, D. Goleman, R. Picard,D.I. Dubrovsky, E.M. Proidakov) scientific research, publications and websites devoted to emotional artificial intelligence, and its features (Aliya Green Emotional artificial intelligence: changing the human world for the better).Results and discussion. The relevance of the topic of emotional AI determined the need to refer to the concept of ”emotional intelligence” (EI) as the basic basis of emotional artificial intelligence, which allowed us to show the essential characteristics of human emotional intelligence, its difference from AI. Emotional artificial intelligence is an innovation of modern AI, its main actors are anthropomorphic robots, text, voice chatbots and video bots, which are already actively demonstrating to the public their knowledge and skills in the field of psychology of emotions, which are being improved within the framework of the current AI.Conclusion. Currently, there is a gradual process of teaching emotional AI to interact with a person, and although these achievements are not great yet, EII is gradually developing in accordance with the challenges of new realities within the specifics of modern applied AI. However, in the digital age, human–machine and machine-to-human communication is an interconnected process that should be aimed at building both utilitarian–useful and partnership relations in the practices of their interaction, which meets the requirements of the era and leads to further progress of AI – to the creation of a new, common AI – “human AI the level” which is supposed to greatly expand the capabilities of a person and society as a whole.

In the current dynamic economic environment effective leadership needs a good understanding of artificial intelligence (AI) capabilities along with emotional intelligence (EI). This paper is analyzing the importance of EI and AI in leadership, how AI can improve leadership, and utilize AI and EI to make strategies for improving decision-making processes for leaders and executives and achieve excellence in leadership. After reviewing and analyzing available literature, this paper trying to find out the use of AI and EI in leadership, current state of practice, trying to find out main opportunities, challenges related with AI, and suggest a practical recommendation for utilizing EI and AI integration in leadership. This study investigates the integration of Emotional Intelligence (EI) and Artificial Intelligence (AI) as complementary tools to enhance leadership decision-making, effectiveness, and organizational performance. The research emphasizes the role of EI in understanding and managing human emotions to foster empathy and interpersonal connections, alongside the capacity of AI to analyze data and provide predictive insights for informed decision-making. Using a multidisciplinary approach, the study develops a framework to align these competencies, addressing critical leadership challenges in the modern workplace, such as adaptability, innovation, and team cohesion. The methodology involves a comprehensive review of existing literature, case studies, and theoretical analysis to explore practical strategies for integration. Key findings reveal the potential of combining EI and AI to foster organizational growth, enhance productivity, and improve team dynamics. The study also discusses the challenges of merging these approaches, such as ethical considerations, bias in AI algorithms, and the complexity of balancing emotional and technical intelligence. By providing actionable recommendations for practitioners and researchers, this work contributes to advancing leadership practices and highlights opportunities for further exploration in the rapidly evolving field of AI-driven human-centric leadership.

This chapter focuses on the theorisation of higher education research and the innovation landscape in South Africa (HESA). Furthermore, a theorisation of Egyptian Higher Education Research and Innovation Landscape) (EgHERIL) is included because of the country’s proximity to Europe and the Middle East. Then the Kenyan landscape is theorised as part of Sub-Saharan Africa. At the beginning of this chapter, a concise historical survey is presented concerning how they (Kenya and Egypt) create sustainable learning environments, starting with their solutions to their respective challenges. The chapter is interested in analysing those conducive contextual factors by amplifying them and highlighting the threats to the evolving solutions to circumvent them. The focus then shifts to gathering evidence, or lack thereof, that some of the mentioned solutions work.

This study analyzes the relationship between emotional intelligence (EI) profiles and the use of artificial intelligence (AI) among university students, considering its use as an academic, informational, and emotional support resource. It also explores whether there are statistically significant differences between the identified EI profiles and the purposes for which AI is used. Finally, it examines the association between EI and AI use. A total of 352 students from the University of Alicante participated (184 women, 168 men; mean age = 21.4, SD = 2.3). EI was assessed using the TMMS-24 scale (Attention, Clarity, and Emotional Repair). To evaluate AI use, a 12-item ad hoc questionnaire was developed and validated, comprising three dimensions: educational support, informational support, and emotional support. Cluster analysis identified three EI profiles: (1) high and balanced EI, with high scores across all three dimensions; (2) regulatory EI, characterized by moderate attention and high emotional understanding and regulation; (3) repair-deficit EI, showing difficulties in emotional regulation despite moderate perception and understanding. ANCOVA analyses assessed differences between profiles, showing that students with high and balanced EI perceived greater usefulness of AI for educational and informational support, as well as greater emotional support benefits, compared to other profiles. Finally, positive correlations were found between EI and AI use across all three types of support. These findings suggest that EI influences AI use in differentiated ways, highlighting its role as a facilitator of learning, information management, and emotional well-being in higher education.

Artificial intelligence (AI), commonly defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation,” can be classified into analytical, human-inspired, and humanized AI depending upon its application of cognitive, emotional, and social intelligence. AI’s foundations took place in the 1950s. A sequence of vicissitudes of funding, interest in, and support for AI followed subsequently. In 2015 AlphaGo, Google’s AI-driven system, won against the human grandmaster in the highly complex board game Go. This is considered one of the most significant milestones in the development of AI and marks the starting of a new period, enabling several AI innovations in a variety of sectors and industries. Higher education, the fashion industry, and the arts serve as illustrations of areas wherein ample innovation based on AI occurs. Using these domains, various angles of innovation in AI can be presented and decrypted. AI innovation in higher education, for example, indicates that at some point, AI-powered robots might take over the role of human teachers. For the moment, however, AI in academia is solely used to support human beings, not to replace them. The apparel industry, specifically fast fashion—one of the planet’s biggest polluters—shows how innovation in AI can help the sector move toward sustainability and eco-responsibility through, among other ways, improved forecasting, increased customer satisfaction, and more efficient supply chain management. An analysis of AI-driven novelty in the arts, notably in museums, shows that developing highly innovative, AI-based solutions might be a necessity for the survival of a strongly declining cultural sector. These examples all show the role AI already plays in these sectors and its likely importance in their respective futures. While AI applications imply many improvements for academia, the apparel industry, and the arts, it should come as no surprise that it also has several drawbacks. Enforcing laws and regulations concerning AI is critical in order to avoid its adverse effects. Ethics and the ethical behavior of managers and leaders in various sectors and industries is likewise crucial. Education will play an additional significant role in helping AI positively influence economies and societies worldwide. Finally, international entente (i.e., the cooperation of the world’s biggest economies and nations) must take place to ensure AI’s benefit to humanity and civilization. Therefore, these challenges and areas (i.e., enforcement, ethics, education, and entente) can be summarized as the four summons of AI.

This study investigates the role of Artificial Intelligence (AI) in enhancing Social-Emotional Competence (SEC) among higher education teachers. SEC, which includes emotional regulation, relationship management, and stress coping, is essential for effective classroom management and student engagement. However, many higher education institutions struggle to equip teachers with sufficient SEC, resulting in increased stress and diminished teaching effectiveness. AI has emerged as a transformative tool, offering real-time feedback, simulations, and data-driven insights to support teachers’ emotional and social development. The principal objectives of the study were: (1) to explore how AI-based tools can enhance social-emotional competence among higher education teachers, (2) to assess the effectiveness of AI in improving teachers’ emotional intelligence and classroom management, and (3) to identify the challenges and limitations associated with implementing AI-driven solutions for SEC development. A mixed-methods research design was employed, combining quantitative surveys and qualitative interviews. A sample of 100 higher education teachers from five universities was selected using stratified random sampling. A structured questionnaire measured emotional intelligence, classroom management, and stress levels before and after AI-based training. Semi-structured interviews gathered teachers’ perceptions of AI’s impact, while classroom observations evaluated behavioral changes. Data analysis included paired t-tests and thematic analysis. Results showed that AI-based training programs significantly improved teachers’ emotional intelligence and classroom management. Pre- and post-training surveys indicated a 15% increase in emotional intelligence and a 20% improvement in stress management. Teachers reported increased self-awareness, better emotional regulation, and improved student-teacher relationships. Classroom observations confirmed that AI-supported teachers managed student behavior more effectively and responded with greater empathy and patience. However, challenges such as technological limitations, resistance to change, and lack of technical support were identified as barriers to effective implementation. The study concludes that AI holds significant potential to enhance teachers’ SEC by providing real-time feedback and data-driven insights. Addressing infrastructure challenges and improving professional development programs are necessary to maximize the effectiveness of AI-driven SEC interventions. The findings underscore the need for integrating AI-based emotional intelligence training into higher education frameworks to improve teacher effectiveness and student engagement. Further research should explore the long-term impact and scalability of AI-based SEC interventions.

Integrating Artificial Intelligence (AI) and E-learning platforms has become increasingly prevalent in the rapidly evolving landscape of higher Education. However, amidst this technological advancement, the role of Emotional Intelligence (EI) and its impact on the efficacy of AI-driven educational tools still needs to be explored. This pilot study seeks to elucidate the intricate relationship between Emotional Intelligence, Artificial Intelligence, and E-Learning in Higher Education. Drawing upon a multidisciplinary approach, this study investigates the correlation between students' Emotional Intelligence competencies and their engagement with AI-driven E-Learning platforms. The findings of this pilot study are expected to shed light on several critical aspects. Firstly, it aims to uncover how Emotional Intelligence influences students' receptivity to AI-infused E-Learning environments, potentially elucidating strategies for optimizing user experience and learning outcomes. Moreover, by exploring the reciprocal influence between Emotional Intelligence and AI algorithms, this research endeavors to contribute to the refinement of AI technologies, fostering greater personalization and adaptability in educational settings. Furthermore, this study endeavors to address the ethical implications inherent in the intersection of Emotional Intelligence, Artificial Intelligence, and E-Learning. By elucidating the potential risks and benefits associated with integrating these technologies, it seeks to inform policymakers, educators, and AI developers alike, facilitating the responsible deployment of AI-driven educational tools. Therefore, its innovative methodology and comprehensive approach aspire to pave the way for future research endeavors, ultimately enriching the educational landscape with insights prioritizing technological advancement and human well-being.

This article focuses on the attitude to innovations and the stress of innovations in higher education. Numerous researchers identify different types of learning (teaching) focused on more or less innovativeness, including more or less procedural, methodological, etc. creation. Thus, the traditional, reproductive and “theoretically oriented” type of education is associated with the retransmission, reproduction of social experience, the second, “practically oriented” – with a creative search based on the existing experience and thus with its enrichment. However, in reality it is impossible to find such forms and formats of teaching and upbringing, in which the practice of teaching was reduced only to the organization of pure reproduction of knowledge and skills, it is even more unrealistic to imagine teaching on an exclusively creative, practical, research, meta-subject basis beyond the reproductive retransmission of the most important knowledge and skills left by the predecessors to the living generations. We are usually talking about the quality and focus of education, its moral, ideological, social, psychological and other conditions and results. The authors discuss the concept of the stress of innovations. The stress of innovations in educational institutions is the stress that occurs in the process and as a result of the introduction of innovations in education. The innovations cause a situation that gives rise to stress and post-stress disorders in students and teachers (didactogenies in the forms of pediogeny, mathetogeny, eductogeny). The main goal of the study is to comprehend the types of people’s attitudes towards innovations in education in the context of the concept of innovation stress. The main method of this research was a theoretical analysis of the problematics of people’s attitudes to innovation in education in the context of the concept of innovation stress. All productive and effective innovations in education are connected by one idea – the creation of conditions under which the development of a person as a person, partner and student/professional is inevitable, and not just stated or impossible. When developing an integrative model for the prevention and overcoming of the stresses of innovation for students and teachers (in case of mathetogeny, pediogeny and eductogeny) it is important to set and solve the tasks of prevention and correction of stress in the context of the development of subjects of education in different contexts: in the context of educational, professional, personal and interactive development. Prevention and correction of stress in innovative education (and in the stress from innovation) is associated with the prevention and correction of pediogenies (harm caused by the wrong, destructive and pathological attitude of teachers to students), mathetogenies (harm caused by the wrong, destructive and pathological attitude of students to teachers), and eductogenies (harm associated with the deformation of organizational conditions and forms of training and education). One of the components of the work is psychological (psychotherapeutic) assistance to the subjects of education (in the form of one-time consultations and trainings, coaching and systematic support). Such assistance, even in the form of one-time consultations, should nevertheless be aimed at systematic, integrative prevention and correction of stresses in education, including the stresses of innovation.

This article discusses modern approaches and technologies that help improve the quality of education and increase student motivation. The study focuses on the benefits and challenges of introducing innovations such as interactive learning platforms, massive open online courses (MOOCs), augmented reality (AR), and other digital tools. Modern education is undergoing significant transformations under the influence of the latest technologies and innovative approaches to learning. Innovations in education not only change traditional teaching methods but also open up new opportunities to improve the efficiency and accessibility of learning. The use of technologies such as virtual and augmented reality, artificial intelligence, and big data makes it possible to significantly improve the quality of the educational process, making it more personalised and interactive. The benefits of the latest approaches include increased student motivation and engagement, improved learning outcomes, and easier access to education. However, innovation also faces challenges, such as technical and financial constraints, the need to train teachers and students to use new technologies, and ethical and social issues. Practical examples such as Khan Academy and the experience of Finland illustrate the successful application of innovative approaches in the educational process. The article examines the implementation of innovations in higher education, in particular, the latest approaches and teaching technologies. Using examples of successful projects and the experience of individual educational institutions, the study shows that innovations contribute to improving the quality of education, increasing student motivation, and improving their preparation for the modern labour market. The benefits and challenges of implementing innovations are discussed, as well as recommendations for the successful implementation of new approaches in educational institutions. The findings of the study emphasise the importance of a clear strategy, support from the administration, and active participation of teachers and students in the implementation of new technologies. Further research could focus on the long-term effects of innovations, assessing their impact on different age groups and disciplines, and developing effective teacher training methods. The paper argues that innovations in higher education contribute to improving the quality of education, motivating students, and preparing them for the modern labour market.

Managing innovation in education is key in facing the era of globalization and constant technological development. Innovation plays an important role in creating a dynamic and relevant learning environment. However, managing innovation in education is not easy, because it involves a deep understanding of various aspects, including student needs, resources, scientific developments, and social and political factors. The innovation management process includes the stages of identification, development, implementation and evaluation of innovation. The aim is to improve the quality of education, student competitiveness, efficiency and inclusiveness. However, in the real world, the introduction of innovations often encounters resistance to change, resource limitations, technological problems, and administrative barriers. In the digital era, the use of artificial intelligence (AI)-based technology in education has changed the paradigm. AI technology enables personalization of learning, efficiency, and collection of data for improvement. However, challenges include infrastructure, staff training, data privacy, and cultural change. A participatory approach, involving all stakeholders in decision-making and implementation of innovation, is an effective method in overcoming challenges and maximizing the benefits of AI technology in higher education. Both case studies underscore the importance of managing innovation and AI technology in education to improve the quality of learning, student engagement and development of relevant skills. But challenges such as infrastructure, training and privacy issues need to be addressed tactfully according to the context of each educational institution.

Purpose The purpose of this article is to deepen understanding of how emotional intelligence (EI) and artificial intelligence (AI) affect organizational behavior from a phenomenological perspective. Through philosophical lenses – particularly Descartes, Husserl and Merleau-Ponty – it highlights the contrasts and similarities between these forms of intelligence. The study aims to explore how AI and EI shape human experience and meaning-making in organizations, providing insights into how AI integration can foster more human-centered organizational practices. Design/methodology/approach This study employs a phenomenological approach to explore the philosophical underpinnings of EI and AI. By examining Descartes’ Cartesian dualism and Husserl’s phenomenology, the study analyzes the alignment and divergence between AI and these philosophical perspectives. The methodology integrates literature review and conceptual analysis to link philosophical insights with their organizational behavior implications, offering a framework that critically examines AI’s impact on human experience and organizational dynamics. Findings The findings highlight that emotional intelligence, rooted in the body-mind interaction, offers a human-centered view of experience, distinct from artificial intelligence. However, combining AI with EI can enhance organizational behavior by promoting more empathetic approaches. While AI can mimic cognitive functions, it lacks the embodied emotional experiences essential for human interaction. This insight emphasizes the need for AI systems to support, rather than disrupt, organizational meaning-making processes. Originality/value This article offers an original interdisciplinary perspective, merging phenomenological philosophy with organizational behavior. By examining emotional and artificial intelligence through Descartes, Husserl and Merleau-Ponty, the study presents fresh insights into AI design that prioritizes human-centered development. It contributes to AI ethics and organizational behavior literature by emphasizing the role of emotional intelligence in guiding AI integration within organizational contexts.

Emotional intelligence (EI) includes social skills, motivation, self-awareness, empathy, and emotional regulation. These qualities are essential for successful personal and professional lives, strong relationships, and complex social interactions. The field of computer science known as artificial intelligence (AI) creates algorithms and systems that carry out operations like observation, reasoning, learning, and decision-making that normally calls for human intelligence. The replication of human intelligence in computers that are built to comprehend, learn, and make decisions is known as AI. It can improve overall efficiency, fortify the decision-making process, and aid in the more efficient delivery of services. AI has advanced significantly over the last few decades; machines can now comprehend language, analyze voice and visuals, and even defeat chess grandmasters. In order to help therapists adapt their treatments to their patients' emotional needs, AI systems can analyze speech patterns and facial expressions during therapy. A number of exciting research avenues could connect AI with EI. Developing AI systems that can identify emotions and act accordingly is a key area of research. This calls for advancements in robot motion control to provide personified reactions that show comprehension and affection, as well as in natural language processing (NLP) to produce emotionally complex text and voice. Researchers must examine the moral ramifications of AI-driven emotional manipulation and the possibility of bias in emotional detection algorithms in order to develop and implement these technologies in an ethical manner. In several areas, AI may enhance EI. AI will be able to identify and react to emotions in sympathetic and organic ways as AI and emotional intelligence research progresses, facilitating more acceptable and natural human-machine interactions. In order to ensure that AI can comprehend and react to human emotions in an ethical manner, protecting individuality and privacy while avoiding prejudices and possible exploitation, it is imperative that emotional intelligence be taken into account in ethical frameworks for AI. The purpose of this analytical work is to assess the relationship between AI and EI, as well as to describe the potential future effects of human-machine contact and consequences.

The evolution of artificial intelligence (AI) and robotics in education has transitioned from automation toward emotionally responsive learning systems through artificial emotional intelligence (AEI). While AI-driven robotics has enhanced instructional automation, AEI introduces an affective dimension by recognizing and responding to human emotions. This study examines the role of AEI-powered robotics in fostering student engagement, cognitive development, and social-emotional learning (SEL) across early childhood, K-12, and higher education. Constructivist and experiential learning theories provide a foundation for integrating emotionally intelligent robotics into interdisciplinary and transdisciplinary STEAM education. Findings indicate that AEI enhances motivation, problem-solving, and collaboration by creating adaptive learning environments that respond to student affective states. However, challenges such as data privacy, inaccuracies in emotion recognition, and access to robotics must be addressed to ensure ethical implementation. The study advocates for further interdisciplinary research, professional growth, and infrastructure investment to optimize AEI-powered robotics in education. The study also emphasizes prioritizing emotionally intelligent interactions for AEI-driven robotics that represents a shift toward human-centered, AI applications for supporting personalized learning and holistic student development. Future directions include refining affective computing models and fostering ethical AI and AEI frameworks to ensure responsible and effective implementation in early childhood through higher educational settings.

To incorporate leadership in the governance of the Employees, one must have a thorough awareness of the various advantages of EI in HEI. Following the development of artificial intelligence came the emergence of emotional artificial intelligence, being aware that increasing the presence of emotions in AI would raise the likelihood of parallels between humans and machines. It will also be able to comprehend humans and be more likely to identify the root cause and consequences of an issue. Many of the gadgets in our bedrooms and kitchens are artificially intelligent to assist us with everyday activities, but they lack the emotional intelligence to adjust to our needs. An artificial intelligence that satisfies a person's needs needs to be capable of adjusting to their mental state. At the MIT laboratory, several technologies are being created. A total of 309 publications on the relevance of emotional intelligence in leadership were found in the scientific databases Scopus and Emotional Intelligence Important in Leadership, out of which 105 were chosen for further study. The Bibliometric tool was used to process the data; it included details on yearly production, journal analysis, author analysis, document analysis, keyword analysis, etc. Managers and policymakers in organizations in general and Higher Educational Institutions in specific can get some valuable inputs from the study's findings that will help promote artificial intelligence with emotional intelligence in their respective organizations that will ensure their growth, stability, and prosperity.

Emerald Studies in Higher Education, Innovation and Technology seeks to provide a multifaceted and interdisciplinary approach to these interconnected topics and invites proposals from all scholars working in these fields. The underlying purpose of this series is to demonstrate how innovations in education, educational technology and teaching can advance research and practice and help us respond to socio-economic changes and challenges. The series has a broad scope, covering many topics, including but not limited to learning analytics, open and distributed learning, technology enhanced learning, digital pedagogies, data mining, virtual and augmented realities, cloud computing, social media, educational robotics, flipped classrooms, active learning, innovation networks and many more.