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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. 

This study provides statistical validation of three composite scales designed to calculate metrics for gateway user competence in terms of domain knowledge, technical skills, and problem-solving orientation. Based on an online survey (N = 365) fielded by an online panel company (Centiment.co) with US based participants, analyses using SPSS software demonstrated that technical competence varied between age groups (lower scores for participants aged 60 and higher) and educational levels (lower scores for participants without a bachelor’s degree) at a statistically significant level (at 95% confidence interval). These findings suggest that gateway developers may need to provide more technical support to users who are senior researchers and when gateways are being introduced into high school classrooms. Conversely, ethnicity and gender were found to be non-predictors of technical competence. These findings suggest the stereotype of white males being more tech-savvy than other ethnic and gender groups may not hold true anymore. 

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.