The study investigates the relationship between perceived emotional intelligence, burnout, work engagement, and job satisfaction in 238 Italian school teachers. The mean age was 50 years, ranged from 26 to 66 (SD = 9.16). The research protocol included a demographics data sheet, the Wong and Law Emotional Intelligence Scale (WLEIS; Wong & Law, 2002), the Copenhagen Burnout Inventory (CBI; Kristensen, Borritz, Villadsen, & Christensen, 2005), the Utrecht Work Engagement Scale (UWES; Schaufeli, Bakker, & Salanova, 2006), and the Organizational Satisfaction Scale (QSO; Cortese, 2001). Several international studies already demonstrated an association among these variables. Our results showed that perceived emotional intelligence positively correlates with work engagement and job satisfaction, and negatively correlates with burnout. Hierarchical regression analyses also point out that, among all the perceived emotional intelligence subdimensions, the use of emotion is the best predictor of the study variables, even when controlling for gender differences. These results suggest that emotional intelligence may have a protective role in preventing negative working experiences of teachers.

Teachers’ psychological well-being is a crucial aspect that influences learning in a classroom climate. The aim of the study was to investigate teachers’ emotional intelligence, burnout, work engagement, and self-efficacy in times of remote teaching during COVID-19 lockdown. A sample of 65 teachers (Mage = 50.49), from early childhood through lower secondary education, were recruited during a period of school closure to answer self-report questionnaires and other measures assessing study variables. Results showed that during the COVID-19 pandemic, teachers reported higher levels of burnout and lower levels of self-esteem due to multiple challenges related to remote teaching and the growing sense of insecurity regarding health safety in the school environment. However, the negative effects of COVID-19 on teachers’ self-efficacy, work engagement, and burnout varied according to their own levels of emotional intelligence. These results demonstrate that emotional intelligence may support teachers in facing these challenges.

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This paper aims to discuss the possible role of inner speech in influencing trust in human–automation interaction. Inner speech is an everyday covert inner monolog or dialog with oneself, which is essential for human psychological life and functioning as it is linked to self-regulation and self-awareness. Recently, in the field of machine consciousness, computational models using different forms of robot speech have been developed that make it possible to implement inner speech in robots. As is discussed, robot inner speech could be a new feature affecting human trust by increasing robot transparency and anthropomorphism.

Meta-emotional intelligence is a recently developed multidimensional construct that, starting from the original ability model of emotional intelligence, focuses on the cognitive aspects of emotional abilities and on the metacognitive and meta-emotional processes that influence our emotional life. Thus, meta-emotional intelligence is the combination of emotional abilities and meta-emotional dimensions, such as the beliefs about emotions, the self-concept about one's emotional abilities, and the self-evaluation of performance. This article aims to illustrate the theoretical and methodological background of this construct and to describe the IE-ACCME test, an original multi-method tool that has been developed to measure the different variables that compose meta-emotional intelligence. Applications of this construct will be discussed, as well as future directions.