An electronic copy of the proposal is available through UT Box at: https://utexas.box.com/s/h5d3xq9r9sbgeb4adgh2x9nzrp0zmlpt. The title and abstract are below.

Title: Understanding Children’s Search as a Learning (SAL) with Voice Assistants in the Home Environment  

Abstract: Children are growing up in a world where AI-based conversational voice search technology has been part of their daily lives. Voice assistants (VAs) have great potential for facilitating children’s learning by providing easy access to rich online information and engaging children to learn through interactions with information by removing literacy requirements (e.g., typing and spelling). Existing research efforts on children and VAs have primarily used a single data collection method with one study session or one-off interviews over a short period. In addition, the impact of VA use on children’s search as learning (SAL) at home has gone largely unnoticed in current studies. Addressing these gaps in the literature, this study aims to (1) investigate how children interact with VAs to seek information at home; (2) identify the types of learning-related search tasks that children attempt when they use VAs at home; (3) examine the factors that influence how children use VAs to learn new information at home; and (4) explore the impact of VAs on children’s motivation, confidence, and curiosity in learning new information. The development of this doctoral dissertation project has been guided by literature and a pilot study with five child and parent dyads that I conducted from June to August 2023. For the proposed dissertation, a total of 30 child (6-10 years old) and parent dyad participants will be recruited to take part in this multi-method research for two weeks. There will be three phases in the study: the first study session, the device deployment period, and the final study session. Data will be collected through interviews, observation notes, questionnaires, device log files, and diary entries. This study will make theoretical and methodological contributions to the field of information seeking and retrieval by investigating the use of commercially available AI-based VAs as potentially important learning technology for children’s information behavior at home. This work will also have practical implications for future VA design that fully considers children to ensure VAs are child-friendly and age-appropriate for children’s learning. 

Committee: Soo Young Rieh (Chair), Kenneth R. Fleischmann, Yan Zhang, and Dania Bilal (University of Tennessee)