Jul 2026· Psychology & Marketing· 0 citations· 62 references
TL;DR
Results show that greater user autonomy and participatory linguistic styles significantly enhance engagement, whereas authoritative language reduces it, emphasizing the importance of designing DCs that empower users and create authentic connections.
Abstract
This study addresses the need to understand human engagement with empathic Generative Artificial Intelligence (GenAI) digital companions (DC), as these systems increasingly fulfil social and emotional roles in daily life. Motivated by the rapid adoption of DC technologies and their potential to reshape human–AI relationships, the study applies Assemblage Theory to explore DC within broader socio‐material systems. Through a mixed‐methods design, 40 participants interacted with a Carl Sagan‐inspired avatar while biometric and qualitative data were collected to map engagement dynamics. Results show that greater user autonomy and participatory linguistic styles significantly enhance engagement, whereas authoritative language reduces it. Findings reveal how context, agency, and language shape bonds between humans and AI, emphasizing the importance of designing DCs that empower users and create authentic connections. This research advances theoretical and practical understanding of devices that promise to transform future social interactions.
Self-harm among young women is increasing globally, yet many do not disclose their distress, leaving families and communities unsure how to respond. Research shows that small, attuned gestures of presence and connection are often the most meaningful forms of support. ReBloom, an immersive interactive installation, was co-created to translate these insights into a publicly accessible, multimodal encounter. Created through collaboration among mental health researchers, creative technologists, artists, and 27 young women with lived experience of self-harm, the project translated research evidence into a publicly accessible encounter designed for embodied, sensory, and relational engagement. Using gesture-responsive interaction, motion capture, color transitions, sound, and narrative movement, ReBloom enabled participants to enact metaphors of attunement, emotional openness, and care. Installed in a large metropolitan shopping center, the work engaged diverse publics across ages and cultural backgrounds, generating forms of interaction ranging from playful exploration to reflective and emotionally resonant encounters. Ethnographic observation and thematic evaluation revealed that participants intuitively recognized the emotional arc of the work and frequently linked the experience to their own understandings of support, empathy, and mental health. The installation also surfaced important paradoxes in public engagement, including the tension between visibility and the desire for more private, cocoon-like spaces for reflection. This case study demonstrates how immersive, distributed storytelling can function as a powerful mode of arts-based knowledge translation, offering new pathways for communicating sensitive mental-health research in accessible, non-stigmatizing, and emotionally meaningful ways.
Barbara Doran, Simon M. Clow, Andrew Lodge et al.· Frontiers in Human Dynamics· 0 citations
The work shows how generative AI can mediate engagement with poetic heritage in culturally grounded emotional-support interactions and suggests that culturally grounded content and structured guidance should anchor system design, while multimodal presentation may strengthen resonance and engagement.
Yang-Ming Zhang, Zhi-Qian Li, Bin Wu et al.· 0 citations
The advent of Large Language Models has accelerated interest in empathetic conversational agents. Despite a surge in empirical research, artificial empathy remains deeply fragmented, often serving as a catch-all term for diverse interactional phenomena. Addressing this conceptual gap, we systematically review 89 empirical studies to map how human-machine empathy is operationalized. Our synthesis reveals that empathy is highly situated and driven by functional goals, like health and well-being, transactional service, social interaction, and learning support. Within these contexts, we classify affective responsiveness by its directional flow, detailing how agents project, elicit, or mediate empathy. We structure the literature into a cohesive framework spanning linguistic, paralinguistic, identity, and architectural strategies. Furthermore, our methodological evaluation reveals a reliance on adapted clinical metrics, a scarcity of longitudinal studies, and a disproportionate focus on text-based over voice-based interfaces. Ultimately, this review equips researchers and practitioners with an actionable foundation for designing, measuring, and implementing contextually appropriate and empathetic agents.
Supriya Khadka, Smit Desai· International Conference on...· 0 citations
Introduction With the increasing integration of artificial intelligence into digital music applications, AI singing has emerged as a novel form of human–AI interaction in mobile karaoke platforms. Despite its growing popularity, limited research has examined the psychological mechanisms underlying users’ sustained engagement with AI singing, particularly from perspectives integrating affective experience and ethical perception. To address this gap, the present study aims to investigate the technological, affective, ethical, and agency-related factors influencing users’ continuance intention toward AI singing. Methods Grounded in research on technology acceptance and psychological engagement, this study develops an extended framework incorporating Perceived Agency (PA), Ethical Acceptance (EA), and Affective Engagement (AE) to better explain users’ responses to AI-generated singing experiences. A total of 460 valid responses were collected through a survey, and structural equation modeling (SEM) was employed to test the proposed relationships. Results The findings indicate that Performance Expectancy (PEF), Effort Expectancy (EE), Perceived Enjoyment (ENJ), Ethical Acceptance (EA), and Affective Engagement (AE) significantly and positively influence continuance intention. In contrast, Perceived Agency (PA), Perceived Innovativeness (PI), and Social Influence (SI) do not show significant effects. Discussion The results suggest that sustained engagement with AI singing is shaped not only by functional evaluations, but also by emotional experience and ethical perception. This study contributes to research on psychological engagement in AI-enabled music interaction and provides practical implications for the design and optimization of AI singing applications.
Yongxin Zhou, Yuhui Wang· Frontiers in Psychology· 0 citations
It is argued that digital empathy is best conceptualised as a relational interface that complements—rather than replaces—human connection that complements—rather than replaces—human connection.
Dr. Evangelia Fragouli· International Journal of Man...· 0 citations
As artificial intelligence (AI) increasingly reshapes language learning through multimodal environments involving text, audio, and images, recent research has called for more dynamic and system-oriented approaches to understanding learner engagement. Against this background, the present study examined the relationships among AI literacy, growth mindset, self-efficacy, flow, and engagement in AI-supported multimodal language learning. Guided by Sociocultural theory (SCT), data were collected from a sample of 776 participants and analyzed using network analysis. The results revealed a fully connected and entirely positive network structure, suggesting that these variables function as closely related components of a broader learner-related psychological system. Among the five nodes, engagement emerged as the most central position, as indicated by the highest values of strength, closeness, betweenness, and expected influence. In addition, growth mindset, self-efficacy, and AI literacy showed comparatively stronger connections with engagement, whereas flow was positively associated with engagement but to a lesser extent. These findings suggest that engagement in AI-supported multimodal language learning is structurally associated not only with positive learning experiences but also with learners’ adaptive beliefs, perceived capability, and technological competence. The study contributes to the research trends in understanding AI engagement by offering a novel network-based perspective on engagement in contemporary AI-mediated language learning.
Yongliang Wang, Ali Derakhshan· International Journal of TES...· 0 citations
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