Editorial: Affective brain-computer interface in emotion artificial intelligence and medical engineering

This piece of creative writing explores the possible impact of emotional artificial intelligence (EAI), a variety of technologies which have the common aim of inferring human emotion from outward expressions such as facial expressions, vocal patterns, text, and physiological data. It is difficult to determine exactly how EAI might affect people when thinking in the abstract. We therefore took a collaborative approach, combining the imagination and writing skills of a Sci-Fi writer, with the knowledge base of an academic studying EAI, in order to create a story which makes the potential consequences of EAI real in a tangible way. Some readers may have prior knowledge of EAI, and for you we hope this piece can offer new insights or different perspectives to consider. However, we suspect many readers will not have heard of EAI and be considering it here for the first time. For you, we hope to provide a story which you can truly immerse yourself in, which can act as a prompt for you to begin to consider your views on this technology. EAI may be at a relatively early stage of development, but we do not anticipate it being long before it is a widespread technology. Our prompt as writers was to create an imagined surveillance future. Our invitation to you as readers is to not only imagine it, but ponder it, live in it, question it, and, ultimately, shape it.

Artificial intelligence (AI) has transformed corporate training and leadership development, particularly through Emotion AI, which enhances emotional intelligence (EQ) and leadership growth. This chapter examines AI-driven tools, such as machine learning and natural language processing (NLP), in creating personalized training programs. Emotion AI, which analyzes emotions via facial recognition, voice tone, and behavior, supports leaders in improving communication, empathy, and decision-making. By integrating AI into leadership development, organizations enhance collaboration, resilience, and conflict resolution. The chapter also explores AI-powered adaptive learning, real-time feedback, and simulations that engage employees in immersive learning environments. While AI offers significant benefits, ethical concerns such as privacy and biases are addressed. The chapter concludes with insights into the future of AI in leadership, emphasizing a balanced approach to technology and human intuition.

The adoption of AI-driven chatbots has substantially influenced language teaching pedagogies and transformed the educational landscape in terms of providing affective support to EFL students to a great extent. Accordingly, research on emotional AI in EFL education is characterized by the deployment of AI applications to provide emotional support for learners and evaluate the emotional impacts of AI through their affective consequences. This study explores the studies conducted about the role of AI-enhanced social-emotional learning frameworks in promoting engagement and emotional well-being in EFL contexts. In other words, the principal objective of this research is to analyze the effects of integrating Artificial Intelligence (AI) tools into English language pedagogy in terms of facilitating student engagement and fostering their emotional well-being. The scholarly studies underscore the efficacy of AI-enhanced social-emotional learning in heightening student engagement and emotional well-being by tailoring learning to learners’ own profiles (Zong& Yang, 2025). A key function of these AI systems is the cultivation of measurable improvements in emotional regulation, cognitive engagement, and higher academic performance. In addition, AI-powered corrective and motivational elements significantly strengthened EFL learners' self-reflective practices, with increased confidence in expressing original ideas, and enjoyment in productive language skills (He, Abbasi & He, 2025). In this regard, emotional AI applications in EFL settings have been utilized in EFL contexts, where they have shown considerable efficacy on the behavioral, cognitive, and affective dimensions of student engagement. The most important and functional affordances of AI tools in English language pedagogy include the delivery of instructional guidance, creating customized content, and offering the capacity for emotion analysis (Liu et al., 2024). This paper will conclude by analyzing the potential factors that shape the adoption and effectiveness of emotional AI technologies in EFL educational contexts.

Businesses today use Artificial Intelligence (AI) to enhance customer interactions. Emotion AI, a branch of AI, recognizes human emotions through text, voice, and facial expressions, enabling personalized and engaging experiences. While this increases customer satisfaction, it also raises privacy and security concerns. To address these challenges, we propose emoAIsec, a framework that integrates Emotion AI with strong data protection measures. It employs encryption, privacy-focused analytics, and legal compliance to ensure ethical AI-powered customer interactions. emoAIsec consists of three key modules: Emotion Recognition, Customer Experience Optimization, and Data Security. Emotion Recognition analyzes emotions using deep learning with high accuracy, outperforming existing models in real-time emotion detection. Customer Experience Optimization dynamically adjusts responses, enhancing customer satisfaction and reducing churn. Data Security ensures safe handling of emotional data through federated learning and differential privacy, setting a new benchmark for secure AI-driven interactions. Our research highlights emoAIsec unique contributions, including an innovative graph-based approach for multimodal emotion fusion and real-time processing capabilities. These advancements make it a reliable and ethical solution for optimizing customer experience while maintaining data integrity and security.

The rapid growth of artificial intelligence (AI) use in higher education raises concerns about how students’ emotional states and the quality of their interactions with AI shape both affective engagement and ethical awareness in academic contexts. This study aims to examine the effects of emotional well-being, AI credibility, and AI interaction quality on students’ ethical awareness, with affective engagement positioned as a mediating mechanism. A quantitative cross-sectional survey was administered to higher education students who use AI tools for academic activities, and the proposed relationships were tested using PLS-based structural modeling with bootstrapping procedures. The findings indicate that emotional well-being (β = 0.549, p < 0.001) and AI interaction quality (β = 0.420, p < 0.001) significantly enhance affective engagement, whereas AI credibility shows no significant effect (β = –0.045, p = 0.342). Affective engagement has a significant positive influence on ethical awareness (β = 0.597, p < 0.001) and significantly mediates the effects of emotional well-being and interaction quality on ethical awareness, while no indirect effect is observed for AI credibility. Overall, these results imply that ethical awareness in student AI use is fostered more strongly through emotionally supportive experiences and high-quality human–AI interactions than through credibility perceptions alone, underscoring the need for human-centered AI integration and ethics-oriented guidance in higher education

FBN-TCN: Temporal convolutional neural network based on spatial domain fusion brain networks for affective brain–computer interfaces

Affective brain–computer interfaces (aBCIs) are an emerging technology that decodes brain signals—primarily electroencephalography (EEG)—to monitor and regulate emotional states in real time. By detecting and responding to users’ emotional processes, aBCIs hold significant promise for transformative applications in health care, adaptive education, and immersive entertainment. This tutorial introduces the foundational concepts of aBCIs, outlines their key methodologies, and highlights recent advances, as well as ongoing challenges. Our objective is to provide researchers, engineers, and practitioners with a structured roadmap for developing robust, generalizable, and user-adaptive aBCI systems.

Recently, intense research and development have been conducted in the research area of Artificial Intelligence, to find various techniques to implement this and create more advanced versions of the technology. Emotion Artificial Intelligence or opinion mining happens to be one such technique that provides advancement to Artificial Intelligence. By definition, opinion mining refers to the collection of different opinions or reviews of people with respect to a particular subject. Classification and categorization of these reviews in a consolidated manner of broad categories are known as summarization of these opinions. The representation of this information thus obtained in the forms of models, charts, and graphs is known as visualization of this summary. In the past couple of years, online platforms have increased rapidly. These platforms advanced themselves to not just providing items that can be purchased but also other activities that can be done with the help of the internet such as reservations, financial transactions. The services also include purchasing/booking various kinds of tickets, for traveling or for entertainment, or ordering food using these online portals. In the following work, the various restaurants which deliver food via online portals and their services have been discussed with respect to the feedbacks or opinions received by customers. In this work, the various reviews left by the customers have been summarized and presented and categorized manner classifying them as positive, negative. These have also been visualized for a better understanding and help in comparison to two or more restaurants. The main goal of this work is to ease the difficulty faced by people to decide which place to go and will also increase the efficiency as the time consumed will be reduced as compared to visiting each website and reading the reviews.

Mental health professionals working for children with special needs have noted that the overall subjective well-being of the caregivers of these children also requires to be assessed and addressed. The different factors of well-being can be addressed through different parameters of artificial intelligence like digital therapeutics, human-computer interaction, and algorithms to design ethics of automation. Therefore, the chapter aims to investigate the impact of the subjective well-being of the caregiving mothers of special children on their level of coping, stress, perceived social support, and hope and the role of machine learning and artificial intelligence. An algorithm based on these domains which can induce human-computer interaction may help in enhancing the well-being of caregivers as well as direct them for proper training and rearing of children with special needs. Therefore, intervention techniques with emotional artificial intelligence should also be designed to focus on elevating the overall subjective well-being of the caregiver of a child with special needs.

Homeostasis, a fundamental biological mechanism, enables living organisms to maintain internal balance despite changing environmental conditions. Inspired by these adaptive processes, research into artificial intelligence (AI) seeks to develop systems capable of dynamic adaptation, introspection, and empathetic interactions with users. This article explores the potential of implementing homeostatic mechanisms in AI as a foundation for emotional intelligence and self-regulation. Key questions include the distinction between simulation and actual experience, the role of machine introspection, and the emergence of qualitative states akin to phenomenal experiences. Drawing on Antonio Damasio’s theory and classical concepts from cybernetics, the article investigates how homeostatic principles might inspire the development of AI, paving the way for more flexible and context-aware technologies.

Background: One of the most crucial steps in providing the child’s oral health care and treatment needs is modifying his/her behavior, mood and emotion in any primary care/dental home or any speciality centre. This is very important parameters for any dental professionals and paediatric dentist. Through this study we investigated the knowledge, awareness of Emotional Artificial Intelligence (AI) as tool for modifying their behaviour prior to any treatment requirement. Materials and Methods: The study was conducted among dental professionals of located in different regions of eastern India. We used a consecutive sampling method to collect our sample. Results: Out of the 120 participating dental professionals, 30% professionals believed that emotional AI could monitor a child’s acceptance to treatment. Despite a low perception level of emotional AI by the dentist, 71% agreed on the beneficial effect of Emotional Artificial Intelligence (AI) in an emergency or uncooperative child if such technology exist. Conclusion: Regression model showed that certain key influencers can increase the quality of healthcare by 3 times which could favors Emotional Artificial Intelligence as an adjunct to Pediatric dentists in managing children.

In the digital age, individuals increasingly express their thoughts and emotions through social media, resulting in a continuous stream of user-generated text data. This abundance of textual information offers valuable insight into users' mental and emotional states. Leveraging this data, a Cognitive Behavioral Therapy (CBT) model has been developed to classify emotions and support mental health interventions. CBT, a well-established, evidence-based approach for addressing psychological issues, benefits from the integration of advanced artificial intelligence (AI) and deep learning techniques to enhance its effectiveness. The proposed CBT framework utilizes Natural Language Understanding (NLU) models such as BERT and RoBERTa to analyze user-generated text and categorize it into specific emotional states based on intent. Datasets sourced from platforms like Kaggle and Reddit serve as the foundation for training the model in emotion detection tasks. Beyond classification, the system incorporates XLNet to generate contextually appropriate and supportive responses aimed at promoting emotional well-being and guiding users toward a healthier mental state. This dual capability enables the system to function as both a diagnostic and therapeutic tool. Users may find interactions with AI-driven systems more approachable and non-judgmental, offering a comfortable alternative to traditional psychotherapy while still delivering meaningful support and guidance.

Our knowledge about the facial expression of emotion may well be entering an age of scientific revolution. Conceptual models for facial behavior and emotion phenomena appear to be undergoing a paradigm shift brought on at least in part by advances made in facial recognition technology and automated facial expression analysis. And the use of technological labor by corporate, government, and institutional agents for extracting data capital from both the static morphology of the face and dynamic movement of the emotions is accelerating. Through a brief survey, the author seeks to introduce what he terms biometric art, a form of new media art on the cutting-edge between this advanced science and technology about the human face. In the last ten years, an increasing number of media artists in countries across the globe have been creating such biometric artworks. And today, awards, exhibitions, and festivals are starting to be dedicated to this new art form. The author explores the making of this biometric art as a critical practice in which artists investigate the roles played by science and technology in society, experimenting, for example, with Basic Emotions Theory, emotion artificial intelligence, and the Facial Action Coding System. Taking a comprehensive view of art, science, and technology, the author surveys the history of design for biometric art that uses facial recognition and emotion recognition, the individuals who create such art and the institutions that support it, as well as how this biometric art is made and what it is about. By so doing, the author contributes to the history, practice, and theory for the facial expression of emotion, sketching an interdisciplinary area of inquiry for further and future research, with relevance to academicians and creatives alike who question how we think about what we feel.

Generative artificial intelligence (AI) is forecasted to have more of an impact on some occupations and industries than others.We examine the impact that AI will have on musicians and singers.This analysis provides an interesting example as musicians typically engage in physical activities when playing, performing, and use creativity when writing songs.We contribute to the existing literature by linking AI to both an occupational analysis and delving into the emotional aspects AI might not be able to provide for musicians.We explore the pros and cons of using AI for the creation of AI generated music.Further, this paper explores music creation to focus on production and performance.Additional consideration is provided regarding the legal and ethical considerations of AI for musicians.

Despite the growing promise of generative artificial intelligence (AI) to revolutionize workplace productivity, many workers remain hesitant to adopt this technology. Why do individuals decide not to use a tool that could greatly enhance their performance? This study addresses this question by examining the overlooked role of emotional preferences in decision-making, providing a novel perspective for scholars and leaders interested in adopting generative AI. By integrating the Emotion Imbued Choice Model with Pekrun et al.’s taxonomy of achievement emotions, this research explores how preferences for emotional outcomes, such as pride, anxiety, or relief, influence the likelihood of using generative AI. Using a sample of 88 U.S.-based participants and a behavioral vignette design, the findings reveal that task enjoyment moderates the relationship between emotional preferences and AI adoption. Specifically, individuals are less likely to use generative AI for tasks they enjoy when they prefer to avoid negative outcome emotions, such as anxiety or hopelessness. Conversely, a preference for positive retrospective emotions, such as pride or relief, increases AI adoption in less enjoyable tasks. These results suggest that emotional preferences in combination with task-specific contexts shape the perceived utility of generative AI and ultimately the decision to use it or not at work. The study contributes to emotional decision-making theories by introducing emotional preferences as a key factor in technology adoption. Practical implications highlight the need for organizations to address emotional barriers, such as creative displacement anxiety, and to design interventions that align with workers’ emotional and task-related preferences. The findings offer actionable insights for organizations aiming to integrate generative AI effectively while enhancing employee satisfaction and productivity. Future research directions include exploring team-based dynamics, longitudinal emotional shifts, and cross-cultural variations in emotional preferences toward AI adoption.