Homeostasis as a foundation for adaptive and emotional artificial intelligence

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.

With the rapid development of generative artificial intelligence (AI) and its deep integration into nursing education, nursing students' AI literacy (AS) has become a critical competency for their professional development. However, the patterns of associations among emotional intelligence (EI), AI self-efficacy (AILS), and AS in relation to comprehensive AS remain unclear. Based on the NUR.S.E.S. framework and using network analysis methods, this study systematically mapped the complex relational network among EI, AILS, and AS among undergraduate nursing students. It identified nodes with high centrality and bridging strength within this network, offering preliminary insights that may inform future educational interventions. A cross-sectional survey design was used, with 982 undergraduate nursing students from a university conveniently sampled in September 2025 as research participants. Assessments were conducted using the EI Scale, the AILS Scale, and the AS Scale. Using R (version 4.5.1; R Core Team), we constructed a Gaussian graph model, calculated centrality metrics such as node and bridge strength, and assessed network stability using the bootstrap method. Network analysis showed that emotion regulation (strength centrality=1.355) and evaluative ability (strength centrality=1.323) showed the highest strength centrality, indicating their prominent positions within the network. Emotional perception (bridge strength=0.427) and comfort with AI (bridge strength=0.242) are the most critical bridge nodes, appearing to connect EI with AI technology systems. Simultaneously, the network architecture suggests that AILS may play a bridging role, effectively linking EI (particularly emotional perception as a bridging factor) with higher levels of AS. Cultivating AS among undergraduate nursing students is a system that deeply integrates emotional, cognitive, and technical confidence. EI was closely associated with AS, and AILS appeared to occupy a bridging position in the network. Educational interventions might consider enhancing emotional perception and comfort with AI, pending validation through longitudinal or experimental designs.

The workplace has experienced extensive digital transformation, in part due to artificial intelligence's commercial availability. Though still an emerging technology, emotional artificial intelligence (EAI) is increasingly incorporated into enterprise systems to augment and automate organizational decisions and to monitor and manage workers. EAI use is often celebrated for its potential to improve workers' wellbeing and performance as well as address organizational problems such as bias and safety. Workers subject to EAI in the workplace are data subjects whose data make EAI possible and who are most impacted by it. However, we lack empirical knowledge about data subjects' perspectives on EAI, including in the workplace. To this end, using a relational ethics lens, we qualitatively analyzed 395 U.S. adults' open-ended survey (partly representative) responses regarding the perceived benefits and risks they associate with being subjected to EAI in the workplace. While participants acknowledged potential benefits of being subject to EAI (e.g., employers using EAI to aid their wellbeing, enhance their work environment, reduce bias), a myriad of potential risks overshadowed perceptions of potential benefits. Participants expressed concerns regarding the potential for EAI use to harm their wellbeing, work environment and employment status, and create and amplify bias and stigma against them, especially the most marginalized (e.g., along dimensions of race, gender, mental health status, disability). Distrustful of EAI and its potential risks, participants anticipated conforming to (e.g., partaking in emotional labor) or refusing (e.g., quitting a job) EAI implementation in practice. We argue that EAI may magnify, rather than alleviate, existing challenges data subjects face in the workplace and suggest that some EAI-inflicted harms would persist even if concerns of EAI's accuracy and bias are addressed.

Emotion (or affective) artificial intelligence (AI) is a hot topic within the greater field of AI, in both, academic as well as practitioner circles. One of the industries with great potential for AI implementation is education. While emotion AI is commonly referred to as a field of growing interest, research in the specific context of education is still in its early stages and publications are few. This paper aims to discuss this emerging field of research on emotion AI in the context of education. The current body of literature can be grouped into three clusters: 1) concept and model development; 2) intelligent tutoring systems; 3) students' state of mind. The review concludes that emotion-based improvements of learning systems surely hold a lot of promise yet still suffers one major shortcoming: that of appropriate responses to the detected emotions.

Artificial Intelligence (AI), a transformative innovation in the past two decades, is reshaping industries and societies. Emotion Recognition System (ERS), a subset of AI, enables machines and robots to discern human emotions. As more AI solutions incorporate ERS, it has led to the establishment of Emotion AI as a promising development enhancing human-computer interaction (HCI) which is a key feature of Industrial Revolution 5.0. Many researchers suggested that Emotion AI will lead to many potential innovative solutions that can be applied in various sectors. Through Emotion AI we would gain the ability to have better awareness, empathy and emotional intelligence, leading to better engagement. Thus, sustainability practitioners utilising Emotion AI can affect better engagement for their initiatives and realise the desired impact. Since ERS engineers, practitioners and developers are expanding the used of ERS and emotion AI to be part of every individuals lives, therefore, there is a need to understand the perspective of the potential users in adopting ERS and being ready for ERS. This research provides insights into individuals' readiness to integrate ERS into their lives. Specifically, this study examines Malaysian youths' readiness and adoption of ERS. With a sample of 177 respondents using PLS-SEM, the study identifies Attitude, Subjective Norms, Perceived Behavioral Control, Facilitating Conditions and Awareness as determinants of ERS adoption. Furthermore, Technology Aptitude moderates the relationship between the determinants and ERS adoption intention. The findings can help future researchers develop more accurate and impactful ERS technologies that can affect better achievement of sustainability goals.

The article explores the potential of Emotional Artificial Intelligence in higher teacher education. In the context of the digital transformation of education, Artificial Intelligence technologies play a crucial role in personalizing learning, enhancing teacher-student interaction, and adapting the educational process based on students’ emotional states. The study examines the fundamental principles of Emotional Artificial Intelligence, emotion recognition methods (computer vision, voice analysis, biometric technologies), and their application in educational contexts. The advantages of implementing this technology in pedagogical universities are analyzed, particularly its ability to improve feedback between students and teachers, foster a more supportive emotional learning environment, and increase student motivation. Special attention is given to the challenges of integrating Emotional Artificial Intelligence into the educational process, such as ethical concerns regarding data privacy, technological limitations, and potential resistance from educators and students. The study identifies future research directions, including improving emotion recognition algorithms, developing ethical standards for the use of emotional data, and integrating Emotional Artificial Intelligence into digital learning platforms. The findings highlight the significant potential of Emotional Artificial Intelligence in pedagogical education to create an adaptive and emotionally responsive learning environment.

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

Chapter Five - Emotional AI: Computationally intelligent devices for education

Emotional artificial intelligence (AI), i.e., affective computing technologies, is rapidly reshaping the education of young minds worldwide. In Japan, government and commercial stakeholders are promulgating emotional AI not only as a neoliberal, cost-saving benefit but also as a heuristic that can improve the learning experience at home and in the classroom. Nevertheless, critics warn of a myriad of risks and harms posed by the technology such as privacy violation, unresolved deeper cultural and systemic issues, machinic parentalism as well as the danger of imposing attitudinal conformity. This study brings together the Technological Acceptance Model and Moral Foundation Theory to examine the cultural construal of risks and rewards regarding the application of emotional AI technologies. It explores Japanese citizens’ perceptions of emotional AI in education and children's toys via analysis of a final sample of 2000 Japanese respondents with five age groups (20s–60s) and two sexes equally represented. The linear regression models for determinants of attitude toward emotional AI in education and in toys account for 44 % and 38 % variation in the data, respectively. The analyses reveal a significant negative correlation between attitudes toward emotional AI in both schools and toys and concerns about privacy violations or the dystopian nature of constantly monitoring of children and students’ emotions with AI (Education: βDystopianConcern = − .094***; Toys: βPrivacyConcern = − .199***). However, worries about autonomy and bias show mixed results, which hints at certain cultural nuances of values in a Japanese context and how new the technologies are. Concurring with the empirical literature on the Moral Foundation Theory, the chi-square (Χ2) test shows Japanese female respondents express more fear regarding the potential harms of emotional AI technologies for the youth's privacy, autonomy, data misuse, and fairness (p < 0.001). The policy implications of these results and insights on the impacts of emotional AI for the future of human-machine interaction are also provided.

The content of the concepts of artificial intelligence and emotional artificial intelligence is analyzed in the article. By comparing the characteristics of natural and artificial intelligence (analysis of information, ability to reflect, capability to learn, ability to self-learning, understanding language, recognition of emotions), their main distinctive features are defined: possibilities of functioning and motivational aspirations. The basic approaches to the study of emotional artificial intelligence are distinguished: analytical (the approach is based on the ability of machines to analyze human emotions) and synthetic (this approach emphasizes the ability of machines to synthesize a variety of emotional abilities). The definition of emotional artificial intelligence are given – intellectual systems capable of recognizing human emotions, interpreting them and responding adequately to them. It has been shown that, in practical terms, emotional artificial intelligence is the most promising in the social and medical fields.

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.

The rise of Artificial Intelligence (AI) in healthcare has led to significant advancements in geriatric nursing, transforming both clinical outcomes and care delivery. Yet, as AI plays an increasing role in patient care, there is growing recognition of the need to balance technological innovation with compassionate, human-centred care. This chapter explores how emotional intelligence (EI) and AI can complement one another to improve the physical and mental health of older adults. The chapter examines the critical role of emotional intelligence in geriatric nursing and discusses how AI can support, rather than replace, the empathetic and emotionally aware care provided by nurses. Through case studies, practical applications, and theoretical analysis, this chapter illustrates how integrating EI and AI can enhance care outcomes while maintaining the human touch essential to geriatric nursing. Ethical considerations, such as maintaining dignity and autonomy, and the future of geriatric nursing in an AI-driven world are also explored.

Emotion Artificial Intelligence (AI) is transforming the capabilities of personal assistant by enabling real-time adaptation to user emotions, behaviours, and contextual needs. This paper explores the potential of emotion-adaptive eco-feedback in personal assistant, particularly within home environments, to foster well-being, energy efficiency, and personalised user experiences. Currently, there is limited research on how users perceive emotion-adaptive eco-feedback and how emotion AI can be adopted in the eco-feedback within personal assistant in real-world settings. To address this, we employed a co-design method — Matchmaking for AI — to facilitate collaboration between real users and researchers. We built a living lab with 11 participants in Germany for half a year and conducted two experimental sessions: a pre-interview to understand user behaviours, requirements, and expectations on eco-feedback, and a co-design session using matchmaking for AI after half a year based on their appliance energy consumption data collected by our smart plugs using our open. DASH platform. The co-design sessions collaboratively brainstorm ideas for potential emotion AI adoptions and identify what their needs should be addressed by emotion AI technology. Through a co-design session, we generated eight design ideas that integrate emotion AI into eco-feedback. These concepts include emotion-adaptive eco-feedback framing, emotion-timed interaction and delivery and emotion-aware environment and social adaption. Our work explores the potential of using Emotion AI in eco-feedback within personal assistant and also provides new insights into AI co-design methodologies.

With growing commercial, regulatory and scholarly interest in use of Artificial Intelligence (AI) to profile and interact with human emotion ("emotional AI"), attention is turning to its capacity for manipulating people, relating to factors impacting on a person's decisions and behavior. Given prior social disquiet about AI and profiling technologies, surprisingly little is known on people's views on the benefits and harms of emotional AI technologies, especially their capacity for manipulation. This matters because regulators of AI (such as in the European Union and the UK) wish to stimulate AI innovation, minimize harms and build public trust in these systems, but to do so they should understand the public's expectations. Addressing this, we ascertain UK adults' perspectives on the potential of emotional AI technologies for manipulating people through a two-stage study. Stage One (the qualitative phase) uses design fiction principles to generate adequate understanding and informed discussion in 10 focus groups with diverse participants (n = 46) on how emotional AI technologies may be used in a range of mundane, everyday settings. The focus groups primarily flagged concerns about manipulation in two settings: emotion profiling in social media (involving deepfakes, false information and conspiracy theories), and emotion profiling in child oriented "emotoys" (where the toy responds to the child's facial and verbal expressions). In both these settings, participants express concerns that emotion profiling covertly exploits users' cognitive or affective weaknesses and vulnerabilities; additionally, in the social media setting, participants express concerns that emotion profiling damages people's capacity for rational thought and action. To explore these insights at a larger scale, Stage Two (the quantitative phase), conducts a UK-wide, demographically representative national survey (n = 2,068) on attitudes toward emotional AI. Taking care to avoid leading and dystopian framings of emotional AI, we find that large majorities express concern about the potential for being manipulated through social media and emotoys. In addition to signaling need for civic protections and practical means of ensuring trust in emerging technologies, the research also leads us to provide a policy-friendly subdivision of what is meant by manipulation through emotional AI and related technologies.