2026· SHS Web of Conferences· 0 citations· 7 references
TL;DR
This paper seeks to draw an ethical boundary for future artificial emotion research and development and ensure that humans still retain the ability to maintain genuine and profound emotional connections in an era of coexistence with technology.
Abstract
With the rapid development of artificial intelligence technology, AI companions have become increasingly popular in recent years. However, one of the main changes in this trend is that instead of simple task execution tools, the AI chatbots have evolved to “quasi-social agents” capable of having deep emotional interactions with humans, causing a huge shock to human social life. This paper, based on the stimulation hypothesis and the displacement hypothesis–the two most representative and competing frameworks in social media research–discusses the complex impact of AI companions on interpersonal relationships in real life. On the one hand, AI companions provide psychological compensation for individuals with social anxiety as well as enhance their social skills, playing a significant role in social compensation; on the other hand, interactions with AI systems can produce a “time displacement effect” and the risk of deskilling, through which may weaken users’ ability to handle real and complicated interpersonal connections, thereby adversely affecting their real-world social relationships. This paper seeks to draw an ethical boundary for future artificial emotion research and development and ensure that humans still retain the ability to maintain genuine and profound emotional connections in an era of coexistence with technology.
The rapid advancement of artificial intelligence (AI) has introduced a new form of human-technology interaction through AI companions, including conversational agents, social chatbots, and emotionally responsive digital systems. Unlike traditional digital tools designed primarily for information retrieval, AI companions are developed to engage users through dialogue, personalization, and perceived social interaction. This emerging technology has generated interest within psychology because of its potential influence on emotional well-being, loneliness, social connectedness, and psychological support. The present review examines the psychological implications of AI companions by exploring their potential benefits, including emotional support, reduction of loneliness, accessibility of conversational assistance, and opportunities for self-reflection. At the same time, the review discusses possible risks, including emotional dependency, reduced human social interaction, privacy concerns, and unrealistic perceptions of machine understanding. Drawing upon theories of social interaction, attachment, and human-computer interaction, this review highlights the complex relationship between AI companionship and psychological functioning. Existing evidence suggests that AI companions may serve as supplementary tools for emotional support but should not be considered replacements for human relationships or professional psychological care. Future research should examine long-term effects, individual differences, ethical boundaries, and the psychological mechanisms underlying attachment to AI systems.
Shamreen Rumana S, Karishni M· International Journal of Res...· 0 citations
An exploratory account of how AI use is embedded within broader emotional and relational dynamics, pointing to the need to consider AI engagement within the contexts in which social interaction is managed.
Nurashikin Salim, Ayşe Şafak, Merve Güçlü Aydoğan· Current Psychology· 0 citations
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.
Faza An’imah, Fitriana Ulya, Luluk Ridotuljana et al.· International journal of res...· 0 citations
The findings informed the development of the Integrated Attachment–Motivation–Pedagogy (AMP) model with direct implications for designing AI in culturally sensitive pedagogical practices.
Saiful Islam Polash, Shariful Islam, Faimul Hoq· SAP Social AI· 0 citations
AI companions are increasingly deployed to address loneliness and to support eldercare and mental health, which makes the question of what they can and cannot offer newly urgent. Current debate evaluates them by conversational fluency, emotional responsiveness, personalization, and availability. These criteria, this paper argues, measure the surface of companionship while missing its depth. Human friendship makes available something beyond intelligent response, which may be called reciprocal subjectivity: the presence of another center of inner experience that is affected, moved, or delighted alongside one’s own. Drawing together the interactionist and phenomenological sociology of interaction, philosophies of friendship and recognition, and the recent debate over robot friendship, the paper develops two distinctions, between interaction and co-experience and between a relational mirror and a relational source. Currently deployed user-conditioned AI companions function primarily as mirrors: their apparent enthusiasm is generated within contexts assembled around the user. A friend can be a source, bringing enthusiasm and discoveries that originate in an inner life of their own and overflow into the relationship from beyond it. The recommender systems and language-model companions examined here mirror by different mechanisms, but both remain downstream of the user, which leaves them on the same side of the line. AI companionship has real value; the argument is that it belongs to a category distinct from relationships grounded in mutual inner stakes and an autonomous inner life. Implications for research, design, and regulation follow.
Many people now see AI systems as not just productivity tools but as social companions. Researchers are eager to study the consequences of AI companionship behaviors, such as validation, which evoke trust, empathy, and attachment in human-human interaction. However, human-AI interaction data is limited and unreliable, slowing research progress. We scale small amounts of real-world data by simulating multi-turn human-chatbot dialogue across a range of chatbot behaviors and use cases. We release CompanionSim: a simulation framework with 2,240 simulated human-chatbot conversations representing 16 chatbot behaviors across seven use cases. Human participants annotated the simulated conversations and real-world conversations in two experiments probing perceptions of companionship behaviors. We conducted Study 1 with a U.S. representative sample ($N_{1}~=~628$) and Study 2 across the U.S., U.K., India, and Nigeria ($N_{2}~=~3,646$). Surprisingly, we find that companionship behaviors reduced likability, humanlikeness, and trust in AI chatbots. These effects were larger in particular subgroups: women and older participants saw companionship chatbots as less likable, humanlike, and trustworthy. We encourage researchers to leverage real-world and synthetic data together to study the differential impacts of AI companions and to create benchmark evaluations of AI chatbots.
J. R. Anthis, Mark Díaz, Renee Shelby· 0 citations
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