Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review.

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.

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson's disease behavioral analysis.

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis.

Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence.

The prevalence of sleep disorders, characterized by issues with quality, timing, and sleep duration is increasing globally. Among modifiable risk factors, diet quality has been suggested to influence sleep features. The Mediterranean diet is considered a landmark dietary pattern in terms of quality and effects on human health. However, dietary habits characterized by this cultural heritage should also be considered in the context of overall lifestyle behaviors, including sleep habits. This study aimed to systematically revise the literature relating to adherence to the Mediterranean diet and sleep features in observational studies. The systematic review comprised 23 reports describing the relation between adherence to the Mediterranean diet and different sleep features, including sleep quality, sleep duration, daytime sleepiness, and insomnia symptoms. The majority of the included studies were conducted in the Mediterranean basin and reported a significant association between a higher adherence to the Mediterranean diet and a lower likelihood of having poor sleep quality, inadequate sleep duration, excessive daytime sleepiness or symptoms of insomnia. Interestingly, additional studies conducted outside the Mediterranean basin showed a relationship between the adoption of a Mediterranean-type diet and sleep quality, suggesting that biological mechanisms sustaining such an association may exist. In conclusion, current evidence suggests a relationship between adhering to the Mediterranean diet and overall sleep quality and different sleep parameters. The plausible bidirectional association should be further investigated to understand whether the promotion of a healthy diet could be used as a tool to improve sleep quality.

Translation and cross-cultural adaptation to Brazilian Portuguese of two brief screening tools for at-risk psychosis youth: the Prodromal Questionnaire (PQ-16) and the PRIME-Screen.

INTRODUCTION: Prodromal characteristics of psychosis have been described for more than a century. Over the last three decades, a variety of studies have proposed methods to prospectively identify individuals (and youth in particular) who are at high risk of developing a psychotic disorder. These studies have validated various screening instruments and made them available in several languages. Here, we describe the translation into Brazilian Portuguese and cross-cultural adaptation of two such screening tools - the Prodromal Questionnaire-16 (PQ-16) and the Prevention through Risk Identification, Management, and Education (PRIME)-Screen. METHOD: Two bilingual native speakers of Brazilian Portuguese translated the questionnaires from English. A native English speaker then performed back-translations into English. These back-translated versions were submitted to the original authors. They provided feedback and later approved the final versions. RESULTS: After translation and cross-cultural adaptation, no items needed to be changed in the adapted PQ-16 and four items were revised in the PRIME-Screen. After the peer-review process, we included two suggestions in the PQ-16 to facilitate use of the tool in our cultural and social contexts. The PRIME-Screen did not need further changes. CONCLUSION: These new instruments can help screen Brazilian Portuguese-speaking patients who are at risk of psychosis in primary care.

Translation and cross-cultural adaptation to Brazilian Portuguese of two brief screening tools for youth at risk of psychosis: the Prodromal Questionnaire (PQ-16) and the PRIME-Screen

Abstract Introduction Prodromal characteristics of psychosis have been described for more than a century. Over the last three decades, a variety of studies have proposed methods to prospectively identify individuals (and youth in particular) who are at high risk of developing a psychotic disorder. These studies have validated various screening instruments and made them available in several languages. Here, we describe the translation into Brazilian Portuguese and cross-cultural adaptation of two such screening tools - the Prodromal Questionnaire-16 (PQ-16) and the Prevention through Risk Identification, Management, and Education (PRIME)-Screen. Method Two bilingual native speakers of Brazilian Portuguese translated the questionnaires from English. A native English speaker then performed back-translations into English. These back-translated versions were submitted to the original authors. They provided feedback and later approved the final versions. Results After translation and cross-cultural adaptation, no items needed to be changed in the adapted PQ-16 and four items were revised in the PRIME-Screen. After the peer-review process, we included two suggestions in the PQ-16 to facilitate use of the tool in our cultural and social contexts. The PRIME-Screen did not need further changes. Conclusion These new instruments can help screen Brazilian Portuguese-speaking patients who are at risk of psychosis in primary care.

Barriers to care and psychopathology among Bolivian migrants living in São Paulo, Brazil.

The objective of this study was to investigate barriers to appropriate mental health care in a sample of Bolivian migrants living in São Paulo and to examine the association between barriers of care and the presence of symptoms of non-psychotic psychiatric disorders in this population. Considering that treatment usually reduces symptoms, it could be hypothesized that individuals reporting more barriers to care also will report more symptoms. The sample comprised 104 individuals born in Bolivia, with Bolivian nationality and living in São Paulo for at least 30 days prior to enrolling in the study, between 18 and 80 years of age and able to read and write in Spanish or Portuguese. The symptoms of mental disorders were assessed using the Self-Reporting Questionnaire (SRQ-20) and barriers to appropriate mental health care were evaluated using the Barriers to Assessing Care Evaluation (BACE). A multiple linear regression analysis was performed to determine the predictive effect of the BACE total score (independent variable) in the SRQ-20 score (dependent variable), including in the model, and the variables that were significantly correlated with the BACE total score or SRQ-20. Our results indicate that more than a half of the sample of Bolivian migrants living in Sao Paulo, Brazil, especially females, presented significant non-psychotic psychopathology. Individuals reporting more barriers to care, especially instrumental and attitudinal barriers, also have a higher risk of psychiatric symptoms, independently of sex, age and family income. Our results suggest that actions to increase availability of mental health services, especially culturally sensitive services, could reduce barriers to care and improve mental health among migrants.