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Dialog systems (e.g., chatbots) have been widely studied, yet related research that leverages artificial intelligence (AI) and natural language processing (NLP) is constantly evolving. These systems have typically been developed to interact with humans in the form of speech, visual, or text conversation. As humans continue to adopt dialog systems for various objectives, there is a need to involve humans in every facet of the dialog development life cycle for synergistic augmentation of both the humans and the dialog system actors in real-world settings. We provide a holistic literature survey on the recent advancements inhuman-centered dialog systems(HCDS). Specifically, we provide background context surrounding the recent advancements in machine learning-based dialog systems and human-centered AI. We then bridge the gap between the two AI sub-fields and organize the research works on HCDS under three major categories (i.e., Human-Chatbot Collaboration, Human-Chatbot Alignment, Human-Centered Chatbot Design & Governance). In addition, we discuss the applicability and accessibility of the HCDS implementations through benchmark datasets, application scenarios, and downstream NLP tasks. 

Science gateways have been a crucial tool that lowers the barriers of computer language proficiency for researchers and scientists alike to implement digital tools to further their research agendas. However, gateways remain somewhat esoteric and difficult to use for many potential users. A chatbot has been proposed as a solution to aid gateway users and for the improvement of gateway usability. Via in-depth interviews with 10 medical professionals, we investigated the challenges they faced when extracting data, namely, slow speed, limited scope, and mixed quality of data. We suggest future gateway developments to address the issues that medical professionals face when searching for publications and data. Findings suggest that gateways could serve practitioners (i.e., clinicians, healthcare providers in this case), beyond the original vision for research and education. Moreover, gateway projects could consider conducting similar market research interviews to better understand the work context (including challenges) faced by the intended users of specific gateways. 

The COVID-19 pandemic is an unprecedented global emergency. Clinicians and medical researchers are suddenly thrown into a situation where they need to keep up with the latest and best evidence for decision-making at work in order to save lives and develop solutions for COVID-19 treatments and preventions. However, a challenge is the overwhelming numbers of online publications with a wide range of quality. We explain a science gateway platform designed to help users to filter the overwhelming amount of literature efficiently (with speed) and effectively (with quality), to find answers to their scientific questions. It is equipped with a chatbot to assist users to overcome infodemic, low usability, and high learning curve. We argue that human-machine communication via a chatbot play a critical role in enabling the diffusion of innovations.