Abstract
AI technology is rapidly evolving and has vast potential for educational applications. This paper focuses on the technical iterations that took place as our project team developed a semi-intelligent conversational agent (CA) that uses speech recognition to fire spoken prompts to promote caregiver-child interaction as they read books aloud together. Situating this work in a design-based research methodology, the technical iterations reported here are part of the iterative “build” phase. (Easterday et al., 2018; Hoadley & Campos, 2022). The CA app promotes conversations between caregivers and children by listening to the human dyads as they read, matching their spoken words to marker words that would pinpoint the page of the storybook the dyads are reading, and playing a prompt corresponding to the page. The dynamic system that supports the app involves multiple components: web accessible services, data processing services, and human outputs, and it has gone through a combined seven iterations in three prototypes. Though a very small part of the DBR cycle, the technical iterations presented here have the potential to inform others interested in incorporating text-to-speech and other AI technologies into educational applications. We close with considerations for future directions.
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Acknowledgment
This work was funded by the Chan Zuckerberg Foundation as part of the Reach Every Reader project. We would like to thank all the playtesters in and out of The Education Arcade who gave valuation feedback throughout the development process. We would also like to give special shout-outs to Louisa Rosenheck, who led the project early on and established the design values and principles, Scot Osterweil, who designed and created the character that embodied the conversational agent, Melissa Callaghan, who, besides being an exceptional researcher and designer, also voiced the first iteration (human version) of the conversational agent, and Dr. Cigdem Uz-Bilgin, without whom the formal usability testing would likely have fallen apart. Finally, the work would not have been possible without Dr. Kathryn Leech, whose dialogic reading strategies are the basis of our early literacy app, and Dr. James Kim, who provided invaluable insights toward the prompts the conversational agent should ask.
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Brandon Hanks developed the literacy app and performed writing – original draft and conceptualization of this paper. Grace Lin performed writing – original draft and conceptualization of this paper. Ilana Schoenfeld and Vishesh Kumar performed writing – review and editing.
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Hanks, B., Lin, G.C., Schoenfeld, I., Kumar, V. (2023). From Spoken Words to Prompt Triggers: Technical Iterations of a Semi-Intelligent Conversational Agent to Promote Early Literacy. In: Zaphiris, P., et al. HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14060. Springer, Cham. https://doi.org/10.1007/978-3-031-48060-7_5
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