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We describe an automated, multimodal embodied conversational agent that plays the role of a genetic counselor, designed to communicate breast cancer risk and the recommended medical guidelines to women. The counselor’s dialogue is driven by intelligent tutoring systems techniques, risk communication principles, and information processing theories. The virtual counselor dynamically tailors its counseling based on user traits, preferred information processing methods, and dynamic comprehension assessments. We conducted a between-subject evaluation study with 30 women, comparing the adaptive counselor to a non-adaptive version of the counselor and a control condition. Women in the adaptive condition demonstrated a significantly greater increase in breast cancer genetics knowledge compared to women in the other conditions. Our results demonstrate the effectiveness of the multidimensional adaptation mechanisms for improving cancer genetic risk communication.
Human-Computer Interaction (HCI) research on menstrual tracking has emphasized the need for more inclusive design of mechanisms for tracking and sharing information on menstruation. We investigate menstrual tracking and data-sharing attitudes and practices in educated, young (20-30 years old) menstruating individuals based in the United States, with self-identified minimal menstrual education backgrounds. Using interviews (N=18), a survey (N=62), and participatory design (N=7), we find that existing mechanisms for tracking and sharing data on menstruation are not adequately responsive to the needs of those who seek relevant menstrual education, are not in the sexual majority, and/or wish to customize what menstrual data they share and with whom. Our analysis highlights a design gap for participants with minimal sexual education backgrounds who wish to better understand their cycles. We also contribute a deepened understanding of structural health inequities that impact menstrual tracking and sharing practices, making recommendations for technology-mediated menstrual care.
Breastfeeding can be challenging, but it is difficult for antenatal education to convey issues associated with the lived experience of breastfeeding. In our work, we explore the potential of interactive simulations to support antenatal education, and present Virtual Feed, a Virtual Reality breastfeeding simulation for parents-to-be developed following a three-step process. (1) We created an experience prototype that features basic VR scenarios and a tangible baby, (2) we engaged in design sessions with 19 parents and parents-to-be to derive design implications to further refine the simulation, and (3) we evaluated the system through case studies to examine the perspectives of parents and parents-to-be on the simulation. Our results show that the simulation successfully engaged users and sparked curiosity, while also encouraging reflection about the challenges of breastfeeding. On this basis, we discuss challenges for the design of simulations with the purpose of supplementing antenatal education.
Recently, chatbots have been deployed in health care in various ways such as providing educational information, and monitoring and triaging symptoms. However, they can be ineffective when they are designed without a careful consideration of the cultural context of the users, especially for marginalized groups. Chatbots designed without cultural understanding may result in loss of trust and disengagement of the user. In this paper, through an interview study, we attempt to understand how chatbots can be better designed for Black American communities within the context of COVID-19. Along with the interviews, we performed design activities with 18 Black Americans that allowed them to envision and design their own chatbot to address their needs and challenges during the pandemic. We report our findings on our participants’ needs for chatbots’ roles and features, and their challenges in using chatbots. We then present design implications for future chatbot design for the Black American population.
Conversational interaction, for example through chatbots, is well-suited to enable automated health coaching tools to support self-management and prevention of chronic diseases. However, chatbots in health are predominantly scripted or rule-based, which can result in a stagnant and repetitive user experience in contrast with more dynamic, data-driven chatbots in other domains. Consequently, little is known about the tradeoffs of pursuing data-driven approaches for health chatbots. We examined multiple artificial intelligence (AI) approaches to enable micro-coaching dialogs in nutrition — brief coaching conversations related to specific meals, to support achievement of nutrition goals — and compared, reinforcement learning (RL), rule-based, and scripted approaches for dialog management. While the data-driven RL chatbot succeeded in shorter, more efficient dialogs, surprisingly the simplest, scripted chatbot was rated as higher quality, despite not fulfilling its task as consistently. These results highlight tensions between scripted and more complex, data-driven approaches for chatbots in health.