Date of Award

12-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Education and Human Development

Committee Chair/Advisor

Golnaz Arastoopour Irgens, Ph.D.

Committee Member

Danielle Herro, Ph.D

Committee Member

Luke Rapa, Ph.D

Committee Member

Joseph E. Michaelis, Ph.D

Abstract

As generative AI (GenAI) becomes increasingly prevalent, institutions seek to understand how students can explore AI in classrooms and develop AI literacy. However, little is known about how, families use these Large Language Models (LLMs) at home and how such engagement supports the development of foundational AI literacy skills. This study examines family engagement with generative AI to understand how AI competencies develops outside formal classrooms and to provide insights for designing contextually relevant AI learning experiences in schools. The study employs a single case study design, integrating quantitative analysis, thematic analysis, and quantitative ethnography to examine and analyze data from field notes, interviews, video and audio recordings, artifacts and chatlogs with GenAI. The findings indicate that families can function as third spaces for developing AI competencies in ways that are contextually relevant. Additionally, GenAI acted as and was treated by human participants as an agentic social partner shaping interaction and learning processes. This agentic capability of GenAI challenges existing theoretical framings of the mediative role of technology. Furthermore, four JME processes, dialogic inquiry, mutual engagement, boundary crossing and co-creation served distinct functional roles and complimented each other. Finally, this study provides evidence for how educators can take on similar roles as the parents did to scaffold students’ critical engagement with GenAI in ways that are contextually relevant to students. This study recommends that schools design learning environments which mirror third space dynamics, where students’ home cultures, interests, and lived experiences are explored as foundations for learning about and using AI.

Author ORCID Identifier

https://orcid.org/0000-0002-1041-7219

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