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Characterizing LLM-Based Family Education Through The Lens Of Activity Theory: A Scoping Review Of The Hci Literature

Date
Publisher
arXiv
Large language models (LLMs) are increasingly involved in family education, yet HCI has not systematically explained the educational interactions that emerge around them. This scoping review analyzes 53 HCI studies from 6,540 records across 19 venues. Using activity theory and AODM, it relates participants and educational objects to mediation, labour, and rules. We find that the literature centers on child--parent interaction and on language, AI literacy, and relational learning. The introduction of LLMs enabled conversational, embodied, and spatial systems to generate support from the context of an unfolding interaction. LLMs redistributed educational labour, while family and institutional rules left parents and professionals responsible for interpreting outputs and deciding how they entered practice. Evidence across families and educational purposes remains limited, especially on sustained personalization, repair labour, and how families negotiate authority and rules. The review offers a framework explaining how LLM capabilities become organized through family participation.
Study at a glance
  • Question: How do large language model systems mediate family education activities, roles, and rules across human-computer interaction research?
  • Setting and data: 53 human-computer interaction studies from 6,540 records across 19 venues, published through May 2026.
  • Method: Scoping review using the Activity-Oriented Design Method
Why it matters

Large language models are increasingly used in family education, but human-computer interaction research lacks a systematic framework explaining how educational interactions emerge around them.

Key findings
  • Most reviewed studies focused on child–parent interaction (83%) and centered on language, narrative, AI literacy, and relational development rather than mathematical or scientific inquiry.
  • Systems mediated learning by generating materials, structuring dialogue, interpreting activity context, and connecting family practices with external knowledge and institutional standards.
  • Generative tools redistributed educational labor by producing provisional materials while leaving parents responsible for oversight, output verification, error correction, and final decision-making.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

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