Deploying a robotic positive psychology coach to improve college students' psychological well-being

Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence

Research in the field of Artificial Intelligence applied to emotions in the educational context has experienced significant growth in recent years. However, despite the field's profound implications for the educational community, the social impact of this scientific production on digital social media remains unclear. To address this question, the present research has been proposed, aiming to analyze the social impact of scientific production on the use of Artificial Intelligence for emotions in the educational context. For this purpose, a sample of 243 scientific publications indexed in Scopus and Web of Science has been selected, from which a second sample of 6,094 social impact records has been extracted from Altmetric, Crossref, and PlumX databases. A dual analysis has been conducted using specially designed software: on one hand, the scientific sample has been analyzed from a bibliometric perspective, and on the other hand, the social impact records have been studied. Comparative analysis based on the two dimensions, scientific and social, has focused on the evolution of scientific production with its corresponding social impact, sources, impact, and content analysis. The results indicate that scientific publications have had a high social impact (with an average of 25.08 social impact records per publication), with a significant increase in research interest starting from 2019, likely driven by the emotional implications of measures taken to curb the COVID-19 pandemic. Furthermore, a lack of alignment has been identified between articles with the highest scientific impact and those with the highest social impact, as well as a lack of alignment in the most commonly used terms from both scientific and social perspectives, a significant variability in the lag in months for scientific research to make an impact on social media, and the fact that the social impact of the research did not emerge from the interest of Twitter users unaffiliated with the research, but rather from the authors, publishers, or scientific institutions. The proposed comparative methodology can be applied to any field of study, making it a useful tool given that current trends in accreditation agencies propose the analysis of the repercussion of scientific research in social media.

Use of automated conversational agents in improving young population mental health: a scoping review

Automated conversational agents (CAs) emerged as a promising solution in mental health interventions among young people. Therefore, the objective of this scoping review is to examine the current state of research into fully automated CAs mediated interventions for the emotional component of mental health among young people. Selected databases were searched in March 2023. Included studies were primary research, reporting on development, feasibility/usability, or evaluation of fully automated CAs as a tool to improve the emotional component of mental health among young population. Twenty-five studies were included (N = 1707). Most automated CAs applications were standalone preventions targeting anxiety and depression. Automated CAs were predominantly AI-based chatbots, using text as the main communication channel. Overall, the results of the current scoping review showed that automated CAs mediated interventions for emotional problems are acceptable, engaging and with high usability. However, the results for clinical efficacy are far less conclusive, since almost half of evaluation studies reported no significant effect on emotional mental health outcomes. Based on these findings, it can be concluded that there is a pressing need to improve the existing automated CAs applications to increase their efficacy as well as conducting more rigorous methodological research in this area.

Psychological wellbeing in Chinese university students: insights into the influences of academic self-concept, teacher support, and student engagement

Objective

This study investigates the complex interplay between academic self-concept, teacher support, student engagement, and psychological wellbeing among Chinese university students. We aimed to elucidate the mediating role of student engagement in these relationships.

Methods

A sample of 597 Chinese undergraduate students from diverse universities participated in the study. We employed structured questionnaires to assess academic self-concept, teacher support, student engagement, and psychological wellbeing. Confirmatory factor analyses and structural equation modeling were used to test our hypothesized model.

Results

Structural equation modeling indicated that the partial mediation model, which considered both direct and indirect effects, outperformed full mediation and direct effect models. Student engagement significantly mediated the relationships between academic self-concept, teacher support, and psychological wellbeing. Importantly, teacher support demonstrated a direct impact on psychological wellbeing, even when accounting for the mediating role of student engagement.

Conclusion

This study underscores the pivotal role of student engagement as a mediator in the relationship between academic self-concept, teacher support, and psychological wellbeing among Chinese university students. While student engagement plays a substantial mediating role, our findings also recognize the persistent direct influence of teacher support on psychological wellbeing. These insights have implications for educators and policymakers aiming to enhance the wellbeing of university students by fostering positive academic self-concept and teacher support while recognizing the importance of student engagement.

Utilizing User Preferences in Designing the AGILE (Accelerating Access to Gender-Based Violence Information and Services Leveraging on Technology Enhanced) Chatbot

INTRODUCTION

Technology advancements have enhanced artificial intelligence, leading to a user shift towards virtual assistants, but a human-centered approach is needed to assess for acceptability and effectiveness. The AGILE chatbot is designed in Kenya with features to redefine the response towards gender-based violence (GBV) among vulnerable populations, including adolescents, young women and men, and sexual and gender minorities, to offer accurate and reliable information among users.

METHODS

We conducted an exploratory qualitative study through focus group discussions (FGDs) targeting 150 participants sampled from vulnerable categories; adolescent girls and boys, young women, young men, and sexual and gender minorities. The FGDs included multiple inquiries to assess knowledge and prior interaction with intelligent conversational assistants to inform the user-centric development of a decision-supportive chatbot and a pilot of the chatbot prototype. Each focus group comprised 9-10 members, and the discussions lasted about two hours to gain qualitative user insights and experiences. We used thematic analysis and drew on grounded theory to analyze the data.

RESULTS

The analysis resulted in 14 salient themes composed of sexual violence, physical violence, emotional violence, intimate partner violence, female genital mutilation, sexual reproductive health, mental health, help-seeking behaviors/where to seek support, who to talk to, and what information they would like, features of the chatbot, access of chatbot, abuse and HIV, family and community conflicts, and information for self-care.

CONCLUSION

Adopting a human-centered approach in designing an effective chatbot with as many human features as possible is crucial in increasing utilization, addressing the gaps presented by marginalized/vulnerable populations, and reducing the current GBV epidemic by moving prevention and response services closer to people in need.

Fuzzy clustering algorithm for university students' psychological fitness and performance detection

Students' psychological fitness is unavoidable, hindering personal development, social interactions, peer influence, and adolescence. Academic stress may be the most dominant factor affecting college students' mental well-being. Therefore, improving the monitoring of mental health issues among college students is a vital topic for study. However, identifying the student's stress level is challenging, leading to uncertainty. Hence, this paper suggests Heuristic Fuzzy C-means Clustering Algorithm (HFCA) for analyzing college students' stress levels, psychological well-being and academic performance detection. The data are collected from the Kaggle stress dataset for predicting student mental health. This study investigates the psychological factors affecting students' academic performance using the suggested HFCA. Students' performance may be predicted using the Fuzzy Cognitive Map (FCM) in this study. This study used fuzzy clustering algorithms to discover the most crucial aspects of student success, such as student involvement and satisfaction. A better understanding of the risk factors for and protective factors against poor mental health can serve as the basis for developing policies and targeted interventions to prevent mental health problems and guarantee that at-risk students can access the help they need. The experimental analysis shows the proposed method HFCA to achieve a high student performance ratio of 96.7%, cognitive development ratio of 97.2%, student engagement ratio of 97.5% and prediction ratio of 95.1% compared to other methods.

Say what you want, I’m not listening!

We present a conversational social robot behaviour design that draws from psychotherapy research to support individual self-reflection and wellbeing, without requiring the robot to parse or otherwise understand what the user is saying. This simplicity focused approached enabled us to intersect the well-being aims with privacy and simplicity, while achieving high robustness. We implemented a fully autonomous and standalone (not network enabled) prototype and conducted a proof-of-concept study as an initial step to test the feasibility of our behaviour design: whether people would successfully engage with our simple behaviour and could interact meaningfully with it. We deployed our robot unsupervised for 48 h into the homes of 14 participants. All participants engaged with self-reflection with the robot without reporting any interaction challenges or technical issues. This supports the feasibility of our specific behaviour design, as well as the general viability of our non-parsing simplicity approach to conversation, which we believe to be an exciting avenue for further exploration. Our results thus pave the way for further exploring how conversational behaviour designs like ours may support people living with loneliness.

A Robotic Companion for Psychological Well-being: A Long-term Investigation of Companionship and Therapeutic Alliance

Social support plays a crucial role in managing and enhancing one's mental health and well-being. In order to explore the role of a robot's companion-like behavior on its therapeutic interventions, we conducted an eight-week-long deployment study with seventy participants to compare the impact of (1) a control robot with only assistant-like skills, (2) a coach-like robot with additional instructive positive psychology interventions, and (3) a companion-like robot that delivered the same interventions in a peer-like and supportive manner. The companion-like robot was shown to be the most effective in building a positive therapeutic alliance with people, enhancing participants' well-being and readiness for change. Our work offers valuable insights into how companion AI agents could further enhance the efficacy of the mental health interventions by strengthening their therapeutic alliance with people for long-term mental health support.