The Role of the AI Chatbot for Digital Companionship among Older Adults：A Qualitative Study.

The Use of AI Chatbots As Emotional Companions: A Qualitative Study of Their Implications for Interpersonal Communication Among Young Adults

This study concludes that the evolution of human–AI communication represents the emergence of new digital communication practices that expand the role of AI from a mere technological tool to an agent participating in users’ communication experiences.

The Influence of Trust and Need Satisfaction on Young Adults’ Interactions with AI Chatbots for Emotional Support

While ChatGPT was primarily viewed as an efficient tool in a work context, the quantitative survey reveals a weakly significant correlation between psychological stress and openness toward the social-emotional use of chatbots.

Between humans and machines: exploring how AI chatbots shape motivation for real relationships

Factors influencing older adults’ intention to use social chatbots: examining technology acceptance, anthropomorphism, authenticity, and artificial intelligence social interaction

Abstract Background and Objectives Despite emerging evidence showing that older adults have begun engaging social chatbots for companionship in recent years, little is known about the factors influencing their adoption decisions. We developed an age-friendly social chatbot and examined older adults’ intention to use it by integrating the Senior Technology Acceptance Model (STAM) with sociorelational factors relevant to human–artificial intelligence (AI) interaction. Research Design and Methods A final sample of 140 community-dwelling older adults interacted with the chatbot for approximately 20 min and then completed questionnaires assessing their attitudes toward the chatbot, perceptions of the chatbot, and their intention to use it in daily life. Results Higher control belief (e.g., perceived ease of use and facilitating conditions), one of the STAM factors, predicted higher intention to use the chatbot among older adults, whereas higher self-rated health predicted lower intention. Importantly, the stronger the sociorelational factors (e.g., authenticity; AI social interaction intensity), the higher the intention to use. However, higher levels of anthropomorphism of the chatbot predicted lower intention to use. Incorporating sociorelational factors into the STAM framework accounted for an additional proportion of variance, comparable to STAM factors alone. Discussion and Implications Our findings suggest that control belief, authenticity, and social interaction intensity with social chatbots are key drivers of adoption intention among older adults. Unmet needs in life may also drive chatbot engagement. However, overly human-like chatbots may deter adoption. Taken together, integrating human–AI interaction factors with the STAM framework is essential when evaluating older adults’ adoption of social chatbots.

AI Chatbots vs Human Friends: Examining Student Preferences for Emotional Support

The study suggests that AI chatbots work best as a complement to human relationships rather than a substitute for them, in which AI helps with day-to-day emotional regulation while human friendships continue to anchor deeper emotional connection and stability.

Talking to AI Like a Friend: Emotional Attachment to Artificial Intelligence among Students

The swift adoption of AI chatbots in higher education is making these technologies not just functionally useful, but also socially and emotionally meaningful, but the affective implications have been under-theorized. This study investigates the effects of emotional attachment to AI chatbots on motivation to learn languages, psychological well-being, and academic success as well as the cultural moderating factors of such effects. Semi-structured interviews were conducted with 50 students from Bangladesh, China, Germany and the US using an exploratory qualitative design based on the theoretical frameworks of Self-Determination Theory, Social Presence Theory and the CASA paradigm. Reflexive Thematic Analysis found that emotional attachment to AI is a complement rather than a substitute for human social relations. The social presence of AI was found to mediate students’ willingness to communicate in a second language. The collectivist students felt more engaged than the individualists. AI satisfied all three SDT needs, despite autonomous motivation decreasing after eight weeks of exposure. The best affective results were obtained with the RAG-powered AI. The communication with AI peers was most effective to mitigate feelings of shame about language learning in rural and socio-economically deprived students. The findings informed the development of the Integrated Attachment–Motivation–Pedagogy (AMP) model with direct implications for designing AI in culturally sensitive pedagogical practices. It will be interesting to validate the Integrated AMP model in future research through longitudinal studies with structural equation modelling and experimental designs and develop interventions that preserve the motivational and equitable properties of emotional attachment to AI without sacrificing students’ autonomy.