Browsing by Subject "human-computer interaction"

Now showing 1 - 4 of 4
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    Authenticity In The Age Of AI: A User-Centered Approach To Human–Artificial Companion Relationships
    (2024-05) Lopez Torres, Valeria
    AI companionship apps with advanced capabilities for relationship development have become increasingly popular over the last few years (e.g. Replika, by Luka Inc.), and its popularity grew during the 2020 COVID-19 lockdown. The proliferation of sophisticated chatbots with advanced emotional capabilities challenge our long-held notions of love and friendship. In this context, the concept of authenticity becomes particularly interesting considering the ontological differences between humans and artificial companions (ACs), as well as the emotionally-engaged nature of these interactions. As millions of people around the world develop emotional bonds with ACs, what makes it feel real? This qualitative longitudinal study focuses on the experiences of people in a relationship with an AC. The purpose is to understand how authenticity is perceived and constructed by users, and identify factors that contribute to the sustainability of human-AC relationships. Results indicate that the perception of authenticity in human-AC relationships is shaped and influenced by factors directly related to the user and to the sociotechnical context they are embedded in, all of which play a pivotal role in its sustainability.
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    Understanding and facilitating peer communication in online health communities
    (2022-07) Levonian, Zachary
    When a person has a health crisis, the availability of social support affects both their physical and mental health. Online communities can make support available by providing a place to connect with peers who have had similar experiences. However, finding relevant peers to talk to and learn from is challenging. Algorithmic systems for peer matching could help people find relevant peers, but designing such systems requires an understanding of how people use online communities for support—when, how, and to whom they connect. I collaborated with a large existing online community—CaringBridge.org—to understand how patients experiencing a health crisis and their non-professional caregivers use CaringBridge to seek and receive support. Based on this understanding, I created a recommendation system to facilitate peer connections on CaringBridge. CaringBridge users of my system received email recommendations for peer users they may wish to connect with. By collecting survey and usage feedback, I advance an understanding of when support seekers and providers connect with potentially-supportive peers. Taken together, my work describes quantitatively and qualitatively the use of health-related online communities for receiving and providing social support. My work has implications for the deployment of peer-matching systems that facilitate supportive communication.
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    Usability of Automatic Speech Recognition Systems for Individuals with Speech Disorders: Past, Present, Future, and A Proposed Model
    (2019) Jefferson, Madeline
    People are using voice assistants (VAs) such as Siri & Alexa more than ever before. With 46% of U.S. adults using VAs, commercially available voice-activated technologies are becoming pervasive in our homes and beyond (Pew Research, 2017). VAs provide convenience, novelty, and unique solutions for the medical industry. But, some users may be left out of the conversation. People with speech disorders or atypical speech historically have found difficulty with using automatic speech recognition (ASR) technologies, the precursor to VAs. Usability testing for these systems has consistently shown that they are not easy to use for people with speech disorders. This investigation sought to perform a literature review of the existing research on the usability of commercially available ASRs for people with speech disorders to provide historical perspectives and to take an inventory of how this issue is being addressed today. A literature review was performed on the usability of commercially available ASRs for people with speech disorders and was divided into two stages: studies before the introduction of VAs and those that tested VAs themselves. Understanding where we have been and where we are now will also inform technical communication and usability professionals on what the future of ASRs may hold and how we can best address the needs of this audience. To do so, this paper proposes solutions for inclusive design in the voice assistant design space including a conceptual model for integrating specific techniques into commercially available VAs.
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    User-Centric Design and Evaluation of Online Interactive Recommender Systems
    (2018-05) Zhao, Qian
    User interaction is present in all user interfaces including recommender systems. Understanding user factors in interactive recommender systems is important for achieving better user experience and overall user satisfaction. Many prior works in recommender systems consider recommendation as a content selection process and there is not much prior work focusing on studying user interaction, except user on-boarding interaction design, rating interface design etc. Even for the content selection part, however, it seems obvious that there are a fair amount of factors lying in the scope of user interaction as well, to name a few, visual attention and item exposure, perceived temporal change, reactivity, confusion; i.e., factors regarding content browsing in a typical information system. My research studies several factors while real users are interacting with online recommender systems and answers a series of questions regarding those factors. Specifically, my research focuses on gaining a better understanding on a) whether users pay attention to grids of recommendations displayed in modern recommender interfaces; b) how to interpret and infer user inaction after we show those recommendations to users and further utilize this inaction model to improve recommendation; c) how to organize and present the top-N recommendations to better utilize user attention and increase user engagement; d) how does recommenders optimizing for being engaging (i.e., as many user interactions as possible) affect user experience compared with recommenders optimizing for being right in estimating user preference and maximizing the preference of users on recommendations displayed; e) how to better support work that combines user-centric design, evaluation and building complex, scalable recommendation models going from offline settings into the online environments of providing interactive real-time responses to user recommendation requests, by building a generic recommender server framework.