子供とLLMチャットボットの間の擬人化：ドライバーと結果に関する体系的レビュー

Computer Science > Human-Computer Interaction arXiv:2607.18250 (cs) [Submitted on 9 May 2026] Title:Anthropomorphism in Children's Interactions with LLM Chatbots: A Systematic Review of Drivers and Outcomes Authors:Hansinie Madushika Jayathilake, Renkai Ma View a PDF of the paper titled Anthropomorphism in Children's Interactions with LLM Chatbots: A Systematic Review of Drivers and Outcomes, by Hansinie Madushika Jayathilake and 1 other authors View PDF HTML (experimental) Abstract:Researchers across domains have investigated children's use of LLM-based chatbots through various perspectives and methodologies. However, prior research remains fragmented regarding anthropomorphism, the tendency for children to assign human characteristics to those large language Model (LLM) chatbots as non-human objects. By analyzing 35 empirical studies published between 2022 and 2025, this systematic literature review identifies the drivers of anthropomorphism in children's LLM chatbot interactions and the subsequent outcomes of these interactions. We found that human-like persona construction, adaptive scaffolding, supportive companionship, and non-human embodied design drive children's anthropomorphic interactions. Additionally, five anthropomorphic outcomes emerged, including children exhibiting paradoxical social and moral responses, dual consciousness about the chatbots, forming varying social ties, exploring social boundaries, and attributing human narratives to conversation breakdowns. The findings, including both benefits and risks, can inform the future design and development of LLM chatbots focused on children's well-being and promoting sustainable interactions that meet children's developmental needs. Comments: Accepted by ACM IDC '26 Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2607.18250 [cs.HC] (or arXiv:2607.18250v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2607.18250 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Renkai Ma [view email] [v1] Sat, 9 May 2026 04:38:40 UTC (263 KB) Full-text links: Access Paper: View a PDF of the paper titled Anthropomorphism in Children's Interactions with LLM Chatbots: A Systematic Review of Drivers and Outcomes, by Hansinie Madushika Jayathilake and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.HC < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)