A Reflection on Virtual Reality Design for Psychological, Cognitive & Behavioral Interventions: Design Needs, Opportunities & Challenges

Despite the substantial research interest in using Virtual Reality (VR) in healthcare in general and in Psychological, Cognitive, and Behavioral (PC&B) interventions in specific, as well as emerging research supporting the efficacy of VR in healthcare, the design process of translating therapies into VR to meet the needs of critical stakeholders such as users and clinicians is rarely addressed. In this paper, we aim to shed light onto the design needs, opportunities and challenges in designing efficient and effective PC&B-VR interventions. Through analyzing the co-design processes of four user-centered PC&B-VR interventions, we examined how therapies were adapted into VR to meet stakeholders’ requirements, explored design elements for meaningful experiences, and investigated how the understanding of healthcare contexts contribute to the VR intervention design. This paper presents the HCI research community with design opportunities and challenges as well as future directions for PC&B-VR intervention design

Living Memory Home: Understanding Continuing Bond in the Digital Age through Backstage Grieving

Prolong Grief Disorder (PGD) is a condition in which mourners are stuck in the grief process for a prolonged period and continue to suffer from an intense, mal-adaptive level of grief. Despite the increased popularity of virtual mourning practices, and subsequently the emergence of HCI research in this area, there is little research looking into how continuing bonds maintained digitally promote or impede bereavement adjustment. Through a one-month diary study and in-depth interviews with 17 participants who recently lost their loved ones, we identified four broad mechanisms of how grievers engage in what we called "backstage" grieving (as opposed to bereavement through digital public space like social media). We further discuss how this personal and private grieving is important in maintaining emotional well-being hence avoiding developing PGD, as well as possible design opportunities and challenges for future digital tools to support grieving

Toward the Development of a Monitoring and Feedback System for Predicting Poor Adjustment to Grief

Losing a loved one is a fundamental and ubiquitous life experience that is often characterized by a certain period of grief and emotional distress. Although the majority of the bereaved can cope with grief resiliently, around 1 of 10 individuals could experience an unusually protracted and intense response referred to as prolonged grief disorder (PGD) following death of a loved one. PGD is associated with work and social impairment and heightened risk of severe medical and psychological conditions. Current means of diagnosis requires a minimum of 6 months to confirm and identify PGD and is discrepant with the fact that the bereaved may need psychotherapeutic intervention in a more timely manner. Contemporary studies have outlined prospective risk factors that could cause poor bereavement outcome, which can potentially contribute to early identification and prevention of problematic response to grief. Self-monitoring applications have been developed and broadly implemented in a vast spectrum of mental and health-related interventions and self-managing processes. This study presents the conceptualization and development of an Internet-based screening method designed by the researchers and psychotherapists that aims to provide meaningful and quantitative feedback in the early phase of the grief and to support decision making in the bereavement process through monitoring the susceptibility to problematic grief outcome

“Can I be more social with a chatbot?”: social connectedness through interactions of autistic adults with a conversational virtual human

The development of AI to function as communicators (i.e. conversational agents), has opened the opportunity to rethink AI’s place within people’s social worlds, and the process of sense-making between humans and machines, especially for people with autism who may stand to benefit from such interactions. The current study aims to explore the interactions of six autistic and six non-autistic adults with a conversational virtual human (CVH/conversational agent/chatbot) over 1-4 weeks. Using semi-structured interviews, conversational chatlogs and post-study online questionnaires, we present findings related to human-chatbot interaction, chatbot humanization/dehumanization and chatbot’s autistic/non-autistic traits through thematic analysis. Findings suggest that although autistic users are willing to converse with the chatbot, there are no indications of relationship development with the chatbot. Our analysis also highlighted autistic users’ expectations of empathy from the chatbot. In the case of the non-autistic users, they tried to stretch the conversational agent’s abilities by continuously testing the AI conversational/cognitive skills. Moreover, non-autistic users were content with Kuki’s basic conversational skills, while on the contrary, autistic participants expected more in-depth conversations, as they trusted Kuki more. The findings offer insights to a new human-chatbot interaction model specifically for users with autism with a view to supporting them via companionship and social connectedness

MindTalker: Navigating the Complexities of AI-Enhanced Social Engagement for People with Early-Stage Dementia

People living with dementia are at risk of social isolation, and conversational AI agents can potentially support such individuals by reducing their loneliness. In our study, a conversational AI agent, called MindTalker, co-designed with therapists and utilizing the GPT-4 Large Language Model (LLM), was developed to support people with early-stage dementia, allowing them to experience a new type of “social relationship” that could be extended to real life. Eight PwD engaged with MindTalker for one month or even longer, and data was collected from interviews. Our findings emphasized that participants valued the novelty of AI, but sought more consistent, deeper interactions. They desired a personal touch from AI, while stressing the irreplaceable value of human interactions. The findings underscore the complexities of AI engagement dynamics, where participants commented on the artificial nature of AI, highlighting important insights into the future design of conversational AI for this population

The "Conversation" about Loss : Understanding How Chatbot Technology was Used in Supporting People in Grief

While conversational agents have traditionally been used for simple tasks such as scheduling meetings and customer service support, recent advancements have led researchers to examine their use in complex social situations, such as to provide emotional support and companionship. For mourners who could be vulnerable to the sense of loneliness and disruption of self-identity, such technology offers a unique way to help them cope with grief. In this study, we explore the potential benefits and risks of such a practice, through semi-structured interviews with 10 mourners who actively used chatbots at different phases of their loss. Our findings indicated seven approaches in which chatbots were used to help people cope with grief, by taking the role of listener, acting as a simulation of the deceased, romantic partner, friend and emotion coach. We then highlight how interacting with the chatbots impacted mourners’ grief experience and conclude the paper with further research opportunities

Investigation of a Web-based Explainable AI Screening for Prolonged Grief Disorder

Losing a loved one through death is known to be one of the most challenging life events. To help the bereaved and their therapists monitor and better understand the factors that contribute to Prolonged Grief Disorder (PGD), we co-designed and studied a web-based explainable AI screening system named “Grief Inquiries Following Tragedy (GIFT).” We used an initial iteration of the system to collect PGD- related data from 611 participants. Using this data, we developed a model that could be used to screen and explain the different factors contributing to PGD. Our results showed that a Random Forest model using Bereavement risk and outcome features performed best in detecting PGD (AUC=0.772), with features such as a negative intepretation of grief and the ability to integrate stressful life events contributing strongly to the model. Afterwards, five grief experts were asked to provide feedback on a mock-up of the results generated by the GIFT model, and discuss the potential value of the explanatory AI model in real-world PGD care. Overall, the grief experts were generally receptive towards using such a tool in a clinical setting and acknowledged the benefit of offering a personalized result to the users based on the explainable AI model. Our results also showed that, in addition to the explainability of the model, the grief experts also preferred a more "empathetic" and "actionable" AI system, especially, when designing for patient end-users