AI-ASSISTED ANALYSIS OF GRANDPARENTS’ INFLUENCE ON CHILDREN’S ORAL LANGUAGE LITERACY: A PILOT CASE STUDY IN XI’AN, CHINA
DOI:
https://doi.org/10.35631/IJEPC.1163040Keywords:
AI-Assisted Analysis, China, Early Childhood, Family Interaction, Grandparents, Oral Language LiteracyAbstract
Oral language literacy plays a foundational role in early childhood development and later academic learning, with everyday family interaction serving as a primary context for its emergence (Dickinson et al., 2023). In contemporary urban China, grandparents frequently assume substantial caregiving responsibilities. However, the interactional mechanisms through which grandparents may shape children’s oral language experiences remain underexplored, particularly through scalable, transparent analytic approaches (Zhang et al., 2024). This study reports a pilot case study conducted in Xi’an, China, employing an Artificial Intelligence (AI) assisted analytical workflow to examine grandparents’ influence on preschool children’s oral language literacy. Specifically, the data were collected from six grandparent–child dyads (children aged 3–6 years), including short naturalistic home audio recordings (5–10 minutes) and semi-structured interviews with grandparents and parents. Automated speech processing was used as an assistive tool to structure language samples and extract interpretable interaction indicators, complemented by confidence-aware processing and targeted human audit (Liu, 2023; Pelfrey et al., 2024). Interview data were thematically coded and systematically triangulated with AI-derived discourse features to support interpretability in a small-sample design (Braun & Clarke, 2022). Rather than producing generalizable estimates, the study demonstrates the methodological feasibility of integrating AI-assisted language sample analysis with qualitative family research to examine intergenerational language interaction. Overall, the findings highlight patterned associations between grandparents’ interactional practices and children’s observed language behaviors, generating testable hypotheses for future large-scale research on family literacy and early language development (Creswell & Poth, 2023).
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