Prevention is a central tenet of modern healthcare. New ways of informing, educating, and allowing patients to keep track of their health are continually developing. Integrating technology that bridges the gap between healthcare providers and their patients is essential in today's digital age. One emerging idea is the use of home healthcare embodied agents. Embodied agents are conservational interfaces that can interact and look like human beings. They can exhibit both verbal and non-verbal language cues and be capable of having a natural conversation. This kind of technology could help both patients and healthcare providers by giving patients a helpful assistant that can educate and take care of particular healthcare needs through conversations with the patient. This could potentially improve patient outcomes, thereby reducing trips to the healthcare provider's office and relieving the burden on physicians and other healthcare providers. This research will focus on home healthcare embodied agents' priorities and the opportunities that this technology can present. A literature review was done to examine the current use of embodied agents in home healthcare settings to establish their present capabilities and applications. Additionally, the strategies/techniques used by home healthcare workers interacting with patients were investigated. By examining the provider-patient relationship and the use of home healthcare embodied agents in tandem, related findings were identified and potential gaps in current research were found that may help direct future exploration.

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We study human-chatbot collaborative conversation systems that enable humans to leverage AI chatbot outputs during an online conversation with others. We evaluate response quality in two collaborative systems and compare them with human-only and chatbot-only settings. Both collaborative systems present AI chatbot results as suggestions but encourage the synthesis of human and chatbot responses to different extents. We also examine the influence of chatbot choices, including both retrieval-based and generation-based methods, and the number of suggestions on collaborative systems. Experimental results show that our collaborative systems can significantly improve the efficiency to formulate a response and improve its quality compared with a human-only system while sacrificing the fluency and humanness of the messages. Compared with a chatbot, collaborative systems can provide answers that are more fluent, human-like, and informative. We also found that the retrieval-based chatbots perform better than the generation-based one from all aspects. The optimal number of chatbot suggestions is one, and showing more suggestions has reduced user efficiency. / Master of Science / Artificial Intelligence (AI) systems have become remarkably interactive and accurate with them becoming an integral part of our life. The increasing use of personal assistants like Siri and the application of AI in important real-world tasks such as medical imaging and diagnosis show that AI can perform as good as trained human experts. Organizations today are expanding at a rapid rate and need to service millions of customers concurrently to remain competitive in the market. With the recent success of AI chatbots, the collaboration of Human and AI to augment customer service management is one of the most sought out solutions to this requirement. A service flow where virtual agents and people work together can be a boon to the industry by making the human agents smarter with a bot "whispering" in their ears. We present the design of various collaborative systems we have developed and discuss the improvements in response efficiency and quality due to them in multiple online user experiments. The results of this study can be used to improve conversational chat systems that assist human agents to improve their response time and quality and identify features of the AI agent that are most beneficial for improving the conversation.

Coherence is the quality that gives a text its conceptual unity, making a text a coordinated set of connected parts rather than a random group of sentences (turns, in the case of dialogue). Hence, coherence is an integral property of human communication, necessary for a meaningful discourse both in text and dialogue. As such, coherence can be regarded as a requirement for conversational agents, i.e. machines designed to converse with humans. Though recently there has been a proliferation in the usage and popularity of conversational agents, dialogue coherence is still a relatively neglected area of research, and coherence across multiple turns of a dialogue remains an open challenge for current conversational AI research. As conversational agents progress from being able to handle a single application domain to multiple ones through any domain (open-domain), the range of possible dialogue paths increases, and thus the problem of maintaining multi-turn coherence becomes especially critical. In this thesis, we investigate two aspects of coherence in dialogue and how they can be used to design modules for an open-domain coherent conversational agent. In particular, our approach focuses on modeling intentional and thematic information patterns of distribution as proxies for a coherent discourse in open-domain dialogue. While for modeling intentional information we employ Dialogue Acts (DA) theory (Bunt, 2009); for modeling thematic information we rely on open-domain entities (Barzilay and Lapata, 2008). We find that DAs and entities play a fundamental role in modelling dialogue coherence both independently and jointly, and that they can be used to model different components of an open-domain conversational agent architecture, such as Spoken Language Understanding, Dialogue Management, Natural Language Generation, and open-domain dialogue evaluation. The main contributions of this thesis are: (I) we present an open-domain modular conversational agent architecture based on entity and DA structures designed for coherence and engagement; (II) we propose a methodology for training an open-domain DA tagger compliant with the ISO 24617-2 standard (Bunt et al., 2012) combining multiple resources; (III) we propose different models, and a corpus, for predicting open-domain dialogue coherence using DA and entity information trained with weakly supervised techniques, first at the conversation level and then at the turn level; (IV) we present supervised approaches for automatic evaluation of open-domain conversation exploiting DA and entity information, both at the conversation level and at the turn level; (V) we present experiments with Natural Language Generation models that generate text from Meaning Representation structures composed of DAs and slots for an open-domain setting.

This study explores the motivations, challenges, and design opportunities associated with using ChatGPT. The research employs an user-centred design approach to understand user interactions with ChatGPT and propose design concepts. Key motivations for using ChatGPT include its practical utility, ability to provide personalized answers, assistive capabilities, and role as an idea-sparring partner. However, users face challenges such as navigating large amounts of text, understanding how to prompt effectively, and dealing with ChatGPT’s lack of nuanced understanding. Consequently, this project proposes a redesign incorporating interactive features and Graphical User Interface changes to tackle these challenges. The findings suggest that the proposed concepts could significantly improve navigation and glanceability and facilitate the overviewing of past interactions. This research contributes to the field of interaction design by providing insights into the use of conversational generative AI and suggesting improvements for future applications.