Developing enhanced conversational agents for social virtual worlds

Effectiveness of Metaverse-Based Collaborative Learning in Nursing Education: A Mixed-Methods Study

BACKGROUND

Collaborative learning is an educational method widely used in nursing education that involves learners solving problems through teamwork. This study investigated the effects of using a metaverse virtual space for collaborative learning among nursing students.

METHOD

A nonequivalent control group posttest design was used, and focus group interviews were conducted. This mixed-methods study included second-year nursing students (n = 43) divided into two groups. The intervention group used metaverse methods for the group projects and the control group used traditional methods.

RESULTS

Satisfaction with collaborative learning and peer evaluation of team projects were significantly higher in the intervention group compared with the control group. Participants' learning experiences were categorized into "unwelcome changes in the learning environment," "discovering the potential of a learning environment that encourages participation," and "tips for building a metaverse learning environment."

CONCLUSION

Metaverse collaborative learning is a student-centered educational approach that fosters active interaction and addresses the shortcomings of traditional approaches. [J Nurs Educ. 2024;63(9):585-593.].

Chasing Metaverses: Reflecting on Existing Literature to Understand the Business Value of Metaverses

Metaverses refer to immersive virtual worlds in which people, places, and things of the physical world are represented by their digital representations. The wide adoption of metaverses is expected to widely disrupt the way we interact in the virtual world by elevating our online interactive experiences and bringing a plethora of implications for businesses. Following a structured literature review of related research published in the last decade, we shed light on our current understanding of metaverses and reflect on the potentially transformative value of metaverses for businesses in the near future. We draw on an established research framework to organize the insights of existing literature across different levels of analysis and activities' purpose. Through this analysis, we reveal eight propositions on the changes brought by the use of metaverses and identify a number of open questions which could serve as future research avenues.

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.

User Modeling and Profiling in Information Systems

User modeling or user profiling is fundamental to manage information overload issues in many adaptive and personalized systems (e.g., recommender systems, personalized search engines, adaptive user interfaces). Although there are some literature review papers that provide an overview of existing studies in user modeling and their usage, there is currently a lack of bibliometric studies that can provide a systematic and quantitative overview of this research area. Therefore, this paper aims to complete the existing literature in this research area through a bibliometric study based on 52,027 relevant publications extracted from Scopus, a world-leading publisher-independent global citation database. The analyses enabled us to identify the most relevant publications, sources of publications, authors, institutions, countries, and their collaboration. We also identify and classify the twelve most important associated topics, along with their subtopics and their trends. Some identified weak signals in topic trend analysis also provide good ideas of potential future research directions.

Robotic Telemedicine for Mental Health: A Multimodal Approach to Improve Human-Robot Engagement

COVID-19 has severely impacted mental health in vulnerable demographics, in particular older adults, who face unprecedented isolation. Consequences, while globally severe, are acutely pronounced in low- and middle-income countries (LMICs) confronting pronounced gaps in resources and clinician accessibility. Social robots are well-recognized for their potential to support mental health, yet user compliance (i.e., trust) demands seamless affective human-robot interactions; natural 'human-like' conversations are required in simple, inexpensive, deployable platforms. We present the design, development, and pilot testing of a multimodal robotic framework fusing verbal (contextual speech) and nonverbal (facial expressions) social cues, aimed to improve engagement in human-robot interaction and ultimately facilitate mental health telemedicine during and beyond the COVID-19 pandemic. We report the design optimization of a hybrid face robot, which combines digital facial expressions based on mathematical affect space mapping with static 3D facial features. We further introduce a contextual virtual assistant with integrated cloud-based AI coupled to the robot's facial representation of emotions, such that the robot adapts its emotional response to users' speech in real-time. Experiments with healthy participants demonstrate emotion recognition exceeding 90% for happy, tired, sad, angry, surprised and stern/disgusted robotic emotions. When separated, stern and disgusted are occasionally transposed (70%+ accuracy overall) but are easily distinguishable from other emotions. A qualitative user experience analysis indicates overall enthusiastic and engaging reception to human-robot multimodal interaction with the new framework. The robot has been modified to enable clinical telemedicine for cognitive engagement with older adults and people with dementia (PwD) in LMICs. The mechanically simple and low-cost social robot has been deployed in pilot tests to support older individuals and PwD at the Schizophrenia Research Foundation (SCARF) in Chennai, India. A procedure for deployment addressing challenges in cultural acceptance, end-user acclimatization and resource allocation is further introduced. Results indicate strong promise to stimulate human-robot psychosocial interaction through the hybrid-face robotic system. Future work is targeting deployment for telemedicine to mitigate the mental health impact of COVID-19 on older adults and PwD in both LMICs and higher income regions.