Problem
Research questions and friction points this paper is trying to address.
Educator Configuration
Open-Ended Learning
K-12 Career Exploration
Generative AI
Understanding How Educators Configure GenAI Support for Open-Ended Learning -- An Exploratory Study of K-12 Career Exploration
🤖 AI Summary
研究探讨了教育者如何配置生成式AI以支持K-12职业探索中的开放式学习，通过访谈和设计活动识别出教育者在将教学需求转化为AI配置时面临的挑战。
📝 Abstract
Generative AI (GenAI) can support open-ended learning through generation, personalization, and learner modeling, yet educators need ways to shape these capabilities around educational goals. Through interviews and design activities with 15 U.S. educators, we examined educator configuration of GenAI using K-12 career exploration as an exploratory context. Educators configured not only AI-generated experiences, but also when student activity became an inference, whether learner information persisted, who could access it, and how it informed subsequent human action. They also faced challenges translating teaching needs into configurations: recognizing possibilities for control beyond familiar uses of GenAI, decomposing general-purpose AI into understandable functions and responsibilities, and identifying useful information through intended teaching actions. We discuss how GenAI systems can support educators in expressing and testing configurations, while establishing boundaries around personalization, inference, persistence, disclosure, and action to keep AI-supported learning aligned with evolving learner needs.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative AI
Educator Configuration
Open-Ended Learning
K-12 Career Exploration
Personalization
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