Co-designing AI-based orchestration tools to support dynamic transitions. Design narratives through conjecture mapping.

Autor/inn/en	Lawrence, LuEttaMae; Guo, Boyuan; Yang, Kexin; Echeverria, Vanessa; Kang, Zimmy; Bathala, Vikrant; Li, Christina; Huang, William; Aleven, Vincent; Rummel, Nikol
Titel	Co-designing AI-based orchestration tools to support dynamic transitions. Design narratives through conjecture mapping.
Quelle	Aus: Weinberger, Armin (Hrsg.); Chen, Wenli (Hrsg.); Hernández-Leo, Davinia (Hrsg.); Chen, Bodong (Hrsg.): 15th International Conference on Computer-Supported Collaborative Learning (CSCL) 2022. CSCL Proceedings. Hiroshima, Japan: International Society of the Learning Sciences (2022) S. 139-146 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Reihe	ISLS annual meeting. 2022
Sprache	englisch
Dokumenttyp	online; Sammelwerksbeitrag
ISSN	1573-4552
ISBN	978-1-7373306-4-6
Schlagwörter	Künstliche Intelligenz; Lehrer; Kollaboration; Unterstützung; Tool
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. (übernommen).
Erfasst von	Externer Selbsteintrag
Update	2025/2