Search Results (1,181)

Although many studies have examined which teaching strategies are effective in achieving inclusive education, less attention has been paid to the role of teachers’ emotional competence. This study aimed to systematically review the literature on the relationship between teachers’ emotional competence and inclusive education through the following research questions: (1) What aspects of teachers’ emotional competence have been studied in relation to inclusive education? (2) How does teachers’ emotional competence influence different aspects of inclusive education? Five electronic databases were searched for all peer-reviewed empirical studies published from 2010 to February 2025. Studies were selected if they focused on K-12 teachers’ emotional competence in relation to inclusive education and were based on empirical designs. The CASP (Critical Appraisal Skills Programme) checklist was used to assess the quality of included studies. Eighteen studies were included. They drew on partially overlapping definitions of emotional competence (i.e., emotional intelligence, emotional awareness, empathy, and emotion regulation) and considered multiple indicators of inclusion that focused on student (engagement, motivation, emotional self-regulation, emotional development, and academic outcomes) and contextual variables (classroom management, teacher–student relationships, and classroom climate). Outcomes differed across various disabilities and special educational needs (SENs). The role of emotional competence should be considered both in improving teachers’ skills in professional practice and in providing adequate and comprehensive training for future teachers. These findings highlight the need to integrate emotional competence training into teacher education programs and inform education policy aimed at fostering more inclusive learning environments. Full article

This study analyzes 194,151 tweets from the 2021 German federal election using sentiment analysis and statistical techniques to examine social media’s role in shaping group emotions, voters’ emotional expression and derogatory speech toward candidates, and the relationship between sentiment intensity and tweet spread. The findings show that negative emotions dominated social media discussions. Additionally, voter perceptions towards candidates on social media also follow a pattern of negativity, often characterized by derogatory speech. This takes four main forms: intelligence-based attacks, animal metaphors, character insults, and gender-based discrimination, with female candidates disproportionately affected. Moreover, the study finds that negative emotions exhibit significantly greater diffusion and reach compared to positive and neutral sentiments on social media. This study further examines election fairness and political dialog openness through the lens of equity, inclusion, diversity, and access (IDEA). These findings emphasize individual and collective emotional dynamics in the social media environment, highlighting the need for governance strategies that promote equity, inclusivity, and diversity in digital political discussions. Full article

The multimodal data collection that includes physiological and psychological data, combined with data processing using artificial intelligence technology, has become a research trend in human–computer interaction. In the stage of new product design, it is necessary to consider user experience for the evaluation and prediction of new products. The paper presents a human–computer interaction study on new product design with user participation. This research adopts a combination of design neurocognition and genetic algorithms in design optimization to evaluate the usability of engineering control interfaces using eye-tracking and facial expression data. Eye-tracking and neural network technology are used to predict the appearance of humanoid robots. The paper explored the evaluation and prediction of new product design using multimodal physiological and psychological data. The research results indicate that artificial intelligence technologies represented by neural networks can fully exploit biometric data represented by eye-tracking and facial expression, improving the effectiveness of new product evaluation and prediction accuracy. The research results provide a solution based on the combination of design neurocognition and artificial intelligence technology for the evaluation and prediction of new product design in the future. Full article

Emotional states play a crucial role in shaping decision-making and social interactions, with sentiment analysis becoming an essential technology in human–computer emotional engagement, garnering increasing interest in artificial intelligence research. In EEG-based emotion analysis, the main challenges are feature extraction and classifier design, making the extraction of spatiotemporal information from EEG signals vital for effective emotion classification. Current methods largely depend on machine learning with manual feature extraction, while deep learning offers the advantage of automatic feature extraction and classification. Nonetheless, many deep learning approaches still necessitate manual preprocessing, which hampers accuracy and convenience. This paper introduces a novel deep learning technique that integrates multi-scale convolution and bidirectional long short-term memory networks with an attention mechanism for automatic EEG feature extraction and classification. By using raw EEG data, the method applies multi-scale convolutional neural networks and bidirectional long short-term memory networks to extract and merge features, selects key features via an attention mechanism, and classifies emotional EEG signals through a fully connected layer. The proposed model was evaluated on the SEED dataset for emotion classification. Experimental results demonstrate that this method effectively classifies EEG-based emotions, achieving classification accuracies of 99.44% for the three-class task and 99.85% for the four-class task in single validation, with average 10-fold-cross-validation accuracies of 99.49% and 99.70%, respectively. These findings suggest that the MSBiLSTM-Attention model is a powerful approach for emotion recognition. Full article

Empathetic and coherent responses are critical in automated chatbot-facilitated psychotherapy. This study addresses the challenge of enhancing the emotional and contextual understanding of large language models (LLMs) in psychiatric applications. We introduce Emotion-Aware Embedding Fusion, a novel framework integrating hierarchical fusion and attention mechanisms to prioritize semantic and emotional features in therapy transcripts. Our approach combines multiple emotion lexicons, including NRC Emotion Lexicon, VADER, WordNet, and SentiWordNet, with state-of-the-art LLMs such as Flan-T5, Llama 2, DeepSeek-R1, and ChatGPT 4. Therapy session transcripts, comprising over 2000 samples, are segmented into hierarchical levels (word, sentence, and session) using neural networks, while hierarchical fusion combines these features with pooling techniques to refine emotional representations. Attention mechanisms, including multi-head self-attention and cross-attention, further prioritize emotional and contextual features, enabling the temporal modeling of emotional shifts across sessions. The processed embeddings, computed using BERT, GPT-3, and RoBERTa, are stored in the Facebook AI similarity search vector database, which enables efficient similarity search and clustering across dense vector spaces. Upon user queries, relevant segments are retrieved and provided as context to LLMs, enhancing their ability to generate empathetic and contextually relevant responses. The proposed framework is evaluated across multiple practical use cases to demonstrate real-world applicability, including AI-driven therapy chatbots. The system can be integrated into existing mental health platforms to generate personalized responses based on retrieved therapy session data. The experimental results show that our framework enhances empathy, coherence, informativeness, and fluency, surpassing baseline models while improving LLMs’ emotional intelligence and contextual adaptability for psychotherapy. Full article

With advances in digital technology, including deep learning and big data analytics, new methods have been developed for autism diagnosis and intervention. Emotion recognition and the detection of autism in children are prominent subjects in autism research. Typically using single-modal data to analyze the emotional states of children with autism, previous research has found that the accuracy of recognition algorithms must be improved. Our study creates datasets on the facial and speech emotions of children with autism in their natural states. A convolutional vision transformer-based emotion recognition model is constructed for the two distinct datasets. The findings indicate that the model achieves accuracies of 79.12% and 83.47% for facial expression recognition and Mel spectrogram recognition, respectively. Consequently, we propose a multimodal data fusion strategy for emotion recognition and construct a feature fusion model based on an attention mechanism, which attains a recognition accuracy of 90.73%. Ultimately, by using gradient-weighted class activation mapping, a prediction heat map is produced to visualize facial expressions and speech features under four emotional states. This study offers a technical direction for the use of intelligent perception technology in the realm of special education and enriches the theory of emotional intelligence perception of children with autism. Full article

Technology-assisted emotion analysis opens new possibilities for the early identification of risk behaviors that may impact the well-being of university students, contributing to the creation of healthier, safer, and more proactive educational environments. This pilot study aimed to design and develop a technological prototype capable of analyzing students’ emotional states and anticipating potential risk situations. A mixed-methods approach was adopted, employing qualitative methods in the ideation, design, and prototyping phases and quantitative methods for laboratory validation to assess the system’s accuracy. Additionally, mapping and meta-analysis techniques were applied and integrated into the chatbot’s responses. As a result, an educational technological innovation was developed, featuring a chatbot structured with a rule-based dialogue tree, complemented by an ontology for knowledge organization and a pre-trained artificial intelligence (AI) model, enhancing the accuracy and contextualization of user interactions. This solution has the potential to benefit the educational community and is also relevant to legislative stakeholders interested in education and student well-being, institutional leaders, academic and well-being coordinators, school counselors, teachers, and students. Full article

Built on the Conservation of Resources (COR), Multiple Intelligence (MI), and Social Exchange (SET) theories, this study investigates how cultural intelligence, emotional intelligence, and perceived organizational support influence newcomers’ task performance and error reduction. The research also explores the mediating effects of emotional exhaustion and the moderating effects of cognitive diversity. Data were collected from 476 participants in organizations employing newcomers, using census, stratified, and simple random sampling techniques. Structural Equation Modeling (SEM) was employed to test the research hypotheses. The results reveal that higher levels of cultural and emotional intelligence are negatively associated with emotional exhaustion, while an increase in perceived organizational support reduces emotional exhaustion. Emotional exhaustion was found to be linked to higher error rates and lower task performance. The mediation analyses showed that emotional exhaustion mediated the relationship between cultural intelligence, emotional intelligence, and perceived organizational support and both task performance and error reduction. Furthermore, cognitive diversity moderated the relationships between cultural intelligence and emotional exhaustion, as well as between emotional intelligence and emotional exhaustion. These findings underscore the critical roles of cultural and emotional intelligence, along with organizational support, in mitigating emotional exhaustion, reducing errors, and enhancing task performance, while emphasizing the importance of cognitive diversity in shaping organizational outcomes. Full article

Many interconnected factors have been implicated in the prediction of whether a given individual occupies a managerial role. These include an assortment of demographic variables such as age and gender as well as trait emotional intelligence (trait EI) and cognitive ability. In order to disentangle their respective effects on formal leadership position, the present study compares a traditional linear approach in the form of a logistic regression with the results of a set of supervised machine learning (SML) algorithms. In addition to merely extending beyond linear effects, a series of techniques were incorporated so as to practically apply ML approaches and interpret their results, including feature importance and interactions. The results demonstrated the superior predictive strength of trait EI over cognitive ability, especially of its sociability factor, and supported the predictive utility of the random forest (RF) algorithm in this context. We thereby hope to contribute and support a developing trend of acknowledging the genuine complexity of real-world contexts such as leadership and provide direction for future investigations, including more sophisticated ML approaches. Full article

As the automotive industry advances rapidly, user needs are in a constant state of evolution. Driven by advancements in big data, artificial intelligence, and natural language processing, mining user requirements from user-generated content (UGC) on social media has become an effective way to understand these dynamic needs. While existing technologies have progressed in topic identification and sentiment analysis, single-method approaches often face limitations. This study proposes a novel method for user requirement mining based on BERTopic and RoBERTa, combining the strengths of topic modeling and sentiment analysis to provide a more comprehensive analysis of user needs. To validate this approach, UGC data from four major Chinese media platforms were collected. BERTopic was applied for topic extraction and RoBERTa for sentiment analysis, facilitating a linked analysis of user emotions and identified topics. The findings categorize user requirements into four main areas—performance, comfort and experience, price sensitivity, and safety—while also reflecting the increasing relevance of advanced features, such as sensors, powertrain performance, and other technologies. This method enhances user requirement identification by integrating sentiment analysis with topic modeling, offering actionable insights for automotive manufacturers in product optimization and marketing strategies and presenting a scalable approach adaptable across various industries. Full article

This study focuses on the impact of technological changes, particularly the development of generative artificial intelligence, on government–citizen interactions in the context of government services. From a psychological perspective with an emphasis on technological governance theory and emotional contagion theory, it examines public perceptions of the simulated emotions of governmental chatbots and investigates the moderating role of age. Data were collected through a multi-stage stratified purposive sampling method, yielding 194 valid responses from an original distribution of 300 experimental questionnaires between 24 September and 13 October 2023. The findings reveal that public expectations significantly enhance the simulated emotional perception of chatbots, with this effect being stronger among older individuals. Age shows significant main and interaction effects, indicating that different age groups perceive the simulated emotional capabilities of chatbots differently. This study highlights the transformative impact of generative artificial intelligence on government–citizen interactions and the importance of integrating AI technology into government services. It calls for governments to pay attention to public perceptions of the simulated emotions of governmental chatbots to enhance public experience. Full article

The aim of this work is to introduce a computer science solution to manage emotions and affections and connect them to the causes as in humans. The scientific foundation of this work lies in the ability to model the affective and emotional states of an individual or artificial intelligence (AI). Then, in this study, we go a step further by exploring how to extend this capability by linking it to the underlying causes—specifically, by establishing a connection between emotions, affective states, and neurotransmitter activities. The methods used in this study pertain to decision support systems based on complexity theory. Specifically, for the training of the platform to study the link between emotions/affections and neurotransmitters, an electroencephalogram (EEG) acquisition module is integrated into the platform. As a result, this solution provides the bedrock for next-generation AI, i.e., artificial rational–emotive decision-makers. In addition, this research studies the connection of EEG data with neurotransmitters’ activity, opening pathways to applications such as emotional monitoring, mental health, and brain–computer interfaces, adding to cognitively and emotionally enriched AI. The main result of this study is a platform able to manage artificial neurotransmitters such as adrenaline, GABA, dopamine, serotonin, oxytocin, endorphins, and the hormone cortisol for emulating and motivating emotive and affective states. In conclusion, this study highlights the following: (i) the possibility of conducting indirect measurements of emotional states based on EEG data, (ii) the development of a framework capable of generating a wide spectrum of emotional states by modulating neurotransmitter levels within a defined discrete range, and (iii) the ability to establish a connection between neurotransmitters (causes) and emotional states (effects). Full article

This paper examines the transformative potential of generative artificial intelligence (AI) and neuroaesthetic methodologies in archaeology, museum collections and art history. It introduces the concept of the AI multiverse, which allows archaeologists and social scientists to construct multiple plausible reconstructions of ancient environments and cultural practices, addressing the inherent uncertainties in archaeological data. Generative AI tools create simulations and visualizations that redefine traditional archaeological frameworks by incorporating multivocal and dynamic interpretations. The study also integrates visual thinking strategies (VTSs), eye tracking and saliency map analyses to investigate how structured observation enhances cognitive and emotional engagement with visual artifacts. A case study involving the painting My Mother, She Fell From the Sky highlights the impact of VTS on guiding viewers’ gaze and improving interpretive depth, as evidenced by heatmaps and saliency distribution. Full article

Limitations in some emotional characteristics that are conceptualized in the definition of emotional intelligence can be seen among people with autism spectrum disorder. The main objective of this study is the analysis of the effectiveness of interventions directed to enhance emotional recognition and emotional regulation among this specific population. A systematic review was carried out in databases such as Psycinfo, WoS, SCOPUS, and PubMed, identifying a total of 572 articles, of which 29 met the inclusion criteria. The total sample included 1061 participants, mainly children aged between 4 and 13 years. The analyzed interventions focused on improving emotional recognition, with significant results in the identification of emotions such as happiness, sadness, and anger, although some showed limitations in the duration of these effects. The most used programs included training in facial recognition, virtual reality, and the use of new technologies such as robots. These showed improvements in both emotional recognition and social skills. Other types of interventions such as music therapy or the use of drama techniques were also implemented. However, a gender bias and lack of consistency between results from different cultures were observed. The conclusions indicate that, although the interventions reviewed seem effective, more research is needed to maximize their impact on the ASD population. Full article

This study was conducted to determine the relationship between emotional intelligence (EI), breastfeeding self-efficacy, and maternal expectations of women who did and did not receive education and counseling during pregnancy. An observational cross-sectional study was conducted in a state hospital with 146 pregnant women (intervention group, n = 72; control group, n = 74). The intervention group had five stages, while the control group received standard pregnancy care. Data is collected by the “Personal Information Form”, “Rotterdam EI Scale”, “Prenatal Breastfeeding Self-Efficacy Scale”, and “Prenatal Maternal Expectations Scale”. When the emotional intelligence scores increased in the intervention group, breastfeeding self-efficacy and antenatal motherhood expectations also increased in the intervention group. In addition, the intervention group’s EI, EI self-evaluation sub-dimension, prenatal motherhood expectations, unrealistic negative motherhood expectations mean, and breastfeeding self-efficacy scale were higher than those of the control group. The regression analysis revealed that the “self-evaluation” sub-dimension of the EI in the intervention group is correlated with regulate others and their own emotions, EI, breastfeeding self-efficacy, and prenatal motherhood expectations. This study shows that pregnant women who attended antenatal classes during the prenatal period had higher EI, breastfeeding self-efficacy, and prenatal maternal expectations than those who were pregnant and did not receive education. Full article