Human-Like Conversational Agents: Chatbots, Digital Humans, and Virtual Influencers

Permanent URI for this collectionhttps://hdl.handle.net/10125/112510

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    Understanding Human Companionship with Artificial Intelligence: Insights from Replika-related Information Systems Research
    (2026-01-06) Ekandjo, Talitakuum
    The emergence of social chatbots designed to simulate emotionally supportive relationships constitutes a substantial advancement in human-technology interaction. Among these, Replika has emerged as the most salient and contentious example, garnering considerable and sustained scholarly attention within the Information Systems (IS) community. Scholars have investigated the processes by which individuals establish and cultivate companionship with Replika, as well as the broader implications of such interactions. Nevertheless, this corpus of knowledge remains fragmented, impeding a comprehensive understanding of what user’ interactions with Replika elucidate about human-AI companionship. This paper undertakes a systematic review of IS literature that centres specifically on Replika, with the objectives of consolidating extant insights and proposing avenues for future research.
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    From Detection to Discovery: A Joint Learning Framework for Medical Knowledge Discovery and Depression Detection Using User-generated Content
    (2026-01-06) Zhang, Wenli; Geng, Shuang; Xie, Jiaheng; Ram, Sudha
    Researchers have long recognized that integrating domain knowledge into machine learning can enhance the efficiency of disease detection using user-generated content. However, a critical yet often overlooked aspect of research on combining machine learning with user-generated content is knowledge discovery: extracting new knowledge directly from user-generated content using machine learning techniques, thereby contributing back to medical knowledge. In this study, we use depression as a research case and develop a joint learning and knowledge graph-based framework, namely, Joint Depression Detection and Knowledge Completion (JDeC), to facilitate the iterative loops of predicting depression and discovering new medical knowledge from user-generated content. Specifically, we create a closed-loop joint training framework that combines ontology-based knowledge graph construction and learning, knowledge learning from user generated content on social media, and knowledge completion by integrating recognized entities from user generated content into the domain ontology.
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    How Chatbots’ Conversation Skills Influence Users’ Satisfaction: Evidence from a Serial Mediation Model and Brain Activity
    (2026-01-06) Jin, Jia; Li, Yebing
    Although chatbots are increasingly adopted, users often report lower satisfaction compared to humans, partly due to their lack of social and emotional intelligence. This study examines how chatbots’ conversation content (generic vs. tailored) and style (non-varied vs. varied) jointly influence users’ satisfaction and through what psychological mechanisms. Across two studies, tailored responses enhanced satisfaction more than generic ones, especially when delivered in a varied style. Serial mediation analyses revealed that tailored responses improved satisfaction by increasing perceived warmth or competence, which in turn elevated social presence. EEG evidence showed tailored-varied responses elicited stronger neural engagement, reflecting subconscious affective and motivational processing. These results demonstrate chatbots’ conversational skills shape both users’ conscious evaluations and subconscious responses. This research advances understanding of human-chatbot interaction by identifying a moderated serial mediation mechanism and offers design guidance: chatbots should adopt socially intelligent strategies that foster warmth, competence, and presence to optimize user satisfaction.
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    The Roots and Routes of Deepfakes: Towards an Ontological, Typological and Sociotechnical Perspective
    (2026-01-06) Tang-Ear, Johnny; Hardy, Catherine; Seymour, Mike
    This paper offers a critical review of the evolving concept of deepfakes, tracing their roots in machine learning-based face-swapping technologies to their broader sociotechnical implications in contemporary society. While early research focused on technical detection and generation, the term ‘deepfake’ now broadly refers to identity deception, AI-driven or otherwise. We analyze how the barriers to deepfake creation have lowered due to accessible tools, and how public understanding has shifted from technical specificity to generalized concern. Drawing on literature from information systems (IS), media studies, and elsewhere, we develop a typology that categorizes deepfakes by intent, realism, technological accessibility, and sociotechnical impact. We also propose an initial ontology to clarify conceptual boundaries, distinguishing deepfakes from related terms such as “cheap fakes” and “synthetic media.” This interdisciplinary perspective contributes to IS discourse by framing deepfakes as dynamic artifacts embedded within complex sociotechnical systems.