Introduction: Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field.Aim: This systematic literature review aims to explore how artificial intelligence (AI) is used to evaluate emotions within educational settings. We provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings.Method: The review involved a search across the following academic databases: Pubmed, Web of Science, PsycINFO and Scopus. Forty-one articles were selected that meet the established inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment within educational environments.Results: The findings reveal a variety of AI-driven approaches that were developed to capture and analyze students’ emotional states during learning activities. The findings are summarized in four fundamental topics: (1) emotion recognition in education, (2) technology integration and learning outcomes, (3) special education and assistive technology, (4) affective computing. Among the key AI techniques employed are machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review identified emerging factors that, while important, require further investigation to understand their relationships and implications fully. These elements could significantly enhance the use of AI in assessing emotions within educational settings. Specifically, we are referring to: (1) federated learning, (2) convolutional neural network (CNN), (3) recurrent neural network (RNN), (4) facial expression databases, and (5) ethics in the development of intelligent systems.Conclusion: This systematic literature review showcases the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts.

The recent pandemic has influenced teachers’ mental health and well-being. The present work follows the Job-Demands-Control model, analyzing changes in the demands, control, and social support during COVID-19, and how they influence job satisfaction, emotional exhaustion, depersonalization, and personal accomplishment among school teachers. The sample comprised 172 school teachers. The instruments applied were The Maslach Burnout Inventory (MBI), The Job Satisfaction Teacher Index (Organization for Economic Cooperation and Development (OECD), 2014), and The Demand Control Support Questionnaire (DCSQ). Job satisfaction was predicted exclusively by social support, revealing the importance of social interactions at work. Emotional exhaustion and depersonalization were predicted by job demands, showing that an increase in job demands (i.e., COVID) affects individuals’ feelings of burnout. The results show that it would be interesting to design strategies that guarantee job control in the teaching context. This would open multiple pathways to implement healthier methodological processes for teachers and the consequent research to support these processes.

The objective of this research was to analyze the impact of an intervention program performed by a specialist in physical education (PE) to contribute to the development of motor competence (MC) in pre-school children with motor development problems. The sample consisted of 28 children (12 from the intervention group and 16 from the control group) aged between 4.1 and 5.9 years (mean = 4.71 ± 0.54) who were in the fifth and sixth grades of pre-school education in two schools from Lugo, Spain. The Movement Assessment Battery for Children-2 (MABC-2) was used for data collection. The data revealed that, in the pre- and post-test intervention groups, there are statistically significant differences in manual dexterity (p < 0.001; d = 2.63), aiming and catching (p < 0.002; d = 1.13), balance (p < 0.001; d = 1.68), total test score (p < 0.001; d = 3.30) and total percentile score (p < 0.001; d = 1.88). Between the control and intervention post-test groups, significant differences were found in manual dexterity (p = 0.015; η² = 0.22), aiming and catching (p = 0.003; η² = 0.32), balance (p = 0.050; η² = 0.15), total test score (p < 0.001; η² = 0.47) and total percentile score (p < 0.001; η² = 0.48). Based on the results obtained, a specific MC program implemented by a PE specialist contributed to the improvement of manual dexterity, aiming and catching and balance, as well as a better percentile in the general MC of pre-school children diagnosed with motor skill problems.

The scientific evidence supports that physical inactivity in childhood is a reality throughout the world which generates important consequences in the global development of children. Young people with Autism Spectrum Disorder (ASD), due to the characteristics of the disorder they suffer, constitute a group at risk. Therefore, assessing the levels of physical activity (PA) in this group is fundamental for subsequent decision making and implementation of PA promotion programmes. Consequently, the aim of this systematic review was to identify, summarise and analyse the main instruments used to assess the levels of PA (in terms of time and/or intensity) in primary school children diagnosed with ASD. Scientific articles in English and Spanish published in five databases were reviewed: PsycINFO, WOS, SPORTDiscus, Scopus and PubMed, following the guidelines of the PRISMA statement. Out of the 605 articles identified, 12 met the previously established inclusion criteria. The instruments used by the studies analysed were divided into two main groups: accelerometers and questionnaires. Both showed different strengths and limitations but agreed on the low levels registered of PA in children with ASD. For this reason, it is considered necessary that further research be carried out in this field, as well as the development and implementation of sports programmes adjusted and adapted to the needs and characteristics of the ASD group.

Body image (BI) is a trending topic of study since health problems derived from a negative perception of the body are increasing and affecting people of all ages, with an increasing incidence among children from the age of eight. The objective of this study was to evaluate the current perception of the body against the desired body and the degree of body satisfaction of Galician primary education students. A total of 355 students (167 boys (47%)) between 9 and 12 years old participated (mean = 10.53; SD = 0.84). Sociodemographic data (sex, age, height, and weight) were collected, and the Figure Rating Scale was used. There are statistically significant differences between boys and girls in the current perceived figure (p = 0.003) and in the desired figure (p < 0.001). Depending on age, the differences were in current (p = 0.010) and desired (p = 0.021) body perception. In conclusion, boys perceive themselves as having a larger figure than girls do, but this perception is far from reality according to the body mass index. For the desired figure, both boys and girls want to be slimmer, but girls want a slimmer figure. Regarding age, the current perceived figure size increases with age as it increases in those students dissatisfied with their body.

This study explores psychosocial differences in mental health indicators and learning patterns between male and female students to promote inclusion and equality in university classrooms, focusing on STEM and non-STEM fields. Using a holistic approach, five learning dimensions comprising a diversity-in-learning (DinL) construct were analyzed: Coping with Difficulties, Effort, Autonomy, Understanding/Career Interest, and Social Context. Psychosocial predictors, including paternal and maternal parenting patterns, were also examined. Data were collected through a questionnaire completed by 2443 students from 83 undergraduate and postgraduate programs. Results indicated that male students reported higher levels of autonomy, while female students reported experiencing more mental health difficulties related to learning. Gender differences in learning dimensions were more pronounced in non-STEM fields. Parental influences also differed, with fathers and mothers shaping male and female students’ learning patterns in ways that reflect traditional gender roles. The findings highlight the lasting effects of gender roles on learning habits and psychological challenges in higher education. The study underscores the importance of addressing gender patterns in parenting and education to support more women in pursuing STEM fields, ultimately fostering greater equality and inclusion in academia.