Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/8268
Title: Co-Designing AI-Based Orchestration Tools to Support Dynamic Transitions: Design Narratives Through Conjecture Mapping
Authors: Lawrence, LuEttaMae
Guo, Boyuan
Yang, Kexin
Echeverria, Vanessa
Kang, Zimmy
Bathala, Vikrant
Li, Christina
Huang, William
Aleven, Vincent
Rummel, Nikol
Keywords: CSCL
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Lawrence, L., Guo, B., Yang, K., Echeverria, V., Kang, Z., Bathala, V., Li, C., Huang, W., Aleven, V., & Rummel, N. (2022). Co-designing AI-based orchestration tools to support dynamic transitions: Design narratives through conjecture mapping. In Weinberger, A. Chen, W., Hernández-Leo, D., & Chen, B. (Eds.), Proceedings of the 15th International Conference on Computer-Supported Collaborative Learning - CSCL 2022 (pp. 139-146). International Society of the Learning Sciences.
Abstract: Dynamically transitioning between individual and collaborative learning has been hypothesized to have positive effects, such as providing the optimal learning mode based on students’ needs. There are, however, challenges in orchestrating these transitions in real-time while managing a classroom of students. AI-based orchestration tools have the potential to alleviate some of the orchestration load for teachers. In this study, we describe a sequence of three design sessions with teachers where we refine prototypes of an orchestration tool to support dynamic transitions. We leverage design narratives and conjecture mapping for the design of our novel orchestration tool. Our contributions include the orchestration tool itself; a description of how novel tool features were revised throughout the sessions with teachers, including shared control between teachers, students, and AI and the use of AI to support dynamic transitions, and a reflection of the changes to our design and theoretical conjectures.
Description: Long Paper
URI: https://doi.dx.org/10.22318/cscl2022.139
https://repository.isls.org//handle/1/8268
Appears in Collections:ISLS Annual Meeting 2022

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