Emotion-infused deep neural network for emotionally resonant conversation

Longitudinal Examination of the Relationship Between Virtual Companionship and Social Anxiety: Emotional Expression as a Mediator and Mindfulness as a Moderator

Purpose

As the interweaving of human interaction and Artificial Intelligence (AI) intensifies, understanding the psychological impact, especially regarding social anxiety, of engaging with AI-driven virtual companionship becomes crucial. While a substantial body of research on social anxiety has concentrated on interactions between individuals, both online and offline, there is a noticeable deficit in explorations concerning how human-computer interactions influence social anxiety. This study offers a comprehensive, longitudinal examination of this underinvestigated relationship, intricately dissecting the roles of emotional expression and mindfulness within the context of AI-based interactions.

Methods

We use social support theory and emotion regulation theory as our theoretical foundation. Data were collected from 618 undergraduate students in Eastern China over two intervals (May 15, 2023 and September 15, 2023). We utilized SPSS 26.0 to conduct descriptive statistics, while AMOS 25.0 facilitated multi-group confirmatory factor analysis (CFA) and the cross-lagged panel modeling.

Results

Our findings indicate that as the frequency of virtual companionship use increases, there's a decline in online social anxiety but a rise in offline social anxiety. Emotional expression emerges as a significant mediator, with heightened emotional expression leading to reduced social anxiety in both contexts. Mindfulness serves as a potent moderator, suggesting its protective role against the potential pitfalls of frequent virtual interactions.

Conclusion

This research not only deepens our theoretical understanding of the dynamics between virtual interactions and social anxiety but also serves as a cornerstone for future endeavors aimed at optimizing AI and devising therapeutic interventions tailored for the digital generation.

Emotionally Intelligent Chatbots: A Systematic Literature Review

Conversational technologies are transforming the landscape of human-machine interaction. Chatbots are increasingly being used in several domains to substitute human agents in performing tasks, answering questions, giving advice, and providing social and emotional support. Therefore, improving user satisfaction with these technologies is imperative for their successful integration. Researchers are leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to impart emotional intelligence capabilities in chatbots. This study provides a systematic review of research on developing emotionally intelligent chatbots. We employ a systematic approach to gather and analyze 42 articles published in the last decade. The review is aimed at providing a comprehensive analysis of past research to discover the problems addressed, the techniques used, and the evaluation measures employed by studies in embedding emotion in chatbot conversations. The study’s findings reveal that most studies are based on an open-domain generative chatbot architecture. Researchers mainly address the issue of accurately detecting the user’s emotion and generating emotionally relevant responses. Nearly 57% of the studies use an enhanced Seq2Seq encoding and decoding of the input of the conversational model. Almost all the studies use both the automatic and manual evaluation measures to evaluate the chatbots, with the BLEU measure being the most popular method for objective evaluation.