This study aims to dissect the current state of emotion recognition and response mechanisms in artificial intelligence (AI) systems, exploring the progress made, challenges faced, and implicit operations of integrating emotional intelligence into AI. This study utilized a comprehensive review approach to investigate the integration of emotional intelligence (EI) into artificial intelligence (AI) systems, concentrating on emotion recognition and response mechanisms. The review process entailed formulating research questions, systematically searching academic databases such as PubMed, Scopus, and Web of Science, critically evaluating relevant literature, synthesizing the data, and presenting the findings in a comprehensive format. The study highlights the advancements in emotion recognition models, including the use of deep literacy ways and multimodal data emulsion. It discusses the challenges in emotion recognition, similar to variability in mortal expressions and the need for real-time processing. The integration of contextual information and individual traits is emphasized as enhancing the understanding of mortal feelings. The study also addresses ethical enterprises, similar as sequestration and impulses in training data. The integration of emotional intelligence into AI systems presents openings to revise mortal-computer relations. Emotion recognition and response mechanisms have made significant progress, but challenges remain. Unborn exploration directions include enhancing the robustness and interpretability of emotion recognition models, exploring cross-cultural and environment-apprehensive emotion understanding, and addressing long-term emotion shadowing and adaption. By further exploring emotional intelligence in AI systems, further compassionate and responsive machines can be developed, enabling deeper emotional connections with humans.

Background: The rising prevalence of mental disorders, coupled with limited access to mental health services, underscores the urgent need for innovative solutions. Artificial Intelligence (AI) offers transformative potential in managing mental health conditions through multimodal data analysis. Objective: This study explores emerging applications of AI in early detection, personalized treatment, and the prevention of symptom escalation in mental disorders. Methods: A narrative review was conducted using comprehensive searches of PubMed, Scopus, and IEEE Xplore databases (2015– 2025). Selected sources included studies on natural language processing (NLP), deep learning, and the analysis of multimodal data (eg, voice, text, and biosensor inputs). A qualitative synthesis was employed to identify key patterns, challenges, and innovations. Findings: AI enhances early detection through concepts such as a “psychological digital signature” and reports high performance in some studies (reported accuracies vary widely, eg, up to ~91% in selected cohorts). However, many high-accuracy reports derive from single-site or limited datasets with variable external validation; therefore, these figures should be interpreted cautiously. We discuss study-specific limitations (sample size, validation methods, and population diversity) in the Methods and Critical Appraisal sections. Conclusion: AI provides a patient-centered, preventive framework for reimagining mental health care. However, its effective integration requires robust ethical standards and digital infrastructure. Ethical considerations are critically linked to clinical implementation, particularly regarding privacy, fairness, and transparency in AI-assisted decision-making.

Background: Coronavirus disease 2019 (COVID-19), now a global pandemic, is a new, highly contagious, and preventable disease that has caused many deaths across the world. Correct understanding of the risks and following health instructions are among the most important self-care parameters. Aims: To assess people's perception of the risks and their adherence to recommended preventive behaviours regarding COVID-19 infection. Method: This descriptive–analytical study was conducted with 1861 people residing in Ardabil province in 2020. The data were collected electronically and included four elements: demographic details; health belief model (HBM) constructs (perceived sensitivity, perceived severity and perceived benefits); beliefs about the effectiveness of disease prevention strategies; and complying with health behaviours. The data was analysed using SPSS-21 software. Results: Significant differences were found in the mean scores for beliefs about the effectiveness of preventative measures, the constructs of the health belief model, and compliance with preventive behaviours relating to the participants’ gender, age, marital status and level of education. Beliefs and intention to stay at home, collectively predicted 54.7% of the variance in preventive behaviours. Conclusions: Although a majority of participants had positive attitudes towards the effectiveness of preventive measures and adhered to them, some people who were not adherent with these healthy behaviours could be key participants in the next wave of the disease.

This study aimed to understand the medical sciences students’ critical thinking (CT) and their learning styles and investigate the relationship between academic performance and the both mentioned variables. A descriptive analytical study was conducted on 469 medical sciences students in Ardabil University of Medical Sciences using the Kolb's learning styles and California's CT skills test (Form-B). Cumulative grade point average was considered as academic performance. The data were analyzed with SPSS 18 and using Chi Square test, Independent t-test, one-way ANOVA and Pearson correlation test with a significant level of p<0.05 . The mean of academic performance was 15.9±1.7 . The popular learning style among students was assimilative style. Results showed significant relationship between academic performance and learning style. Significant relationship between academic performances of students with the total CT skills was not found, but significant relationship was seen between academic performance and deductive skill only. Learning styles changed in different semesters among medicine and dentistry students from an abstract-reflexive style to a concrete-active one. Using appropriate, active and collaborative teaching methods can help to improve learning and students' academic performance.