理解教育者如何配置生成式AI以支持开放式学习——一项K-12职业探索的探索性研究
Understanding How Educators Configure GenAI Support for Open-Ended Learning -- An Exploratory Study of K-12 Career Exploration
- University of Notre Dame（圣母大学）
- University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过访谈和设计活动，探讨美国教育者如何配置GenAI以支持K-12职业探索中的开放式学习，发现配置挑战并提出系统支持方向。
AI中文摘要：
生成式人工智能（GenAI）可以通过生成、个性化和学习者建模来支持开放式学习，然而教育者需要方法来围绕教育目标塑造这些能力。通过对15位美国教育者的访谈和设计活动，我们以K-12职业探索作为探索性情境，考察了教育者对GenAI的配置。教育者不仅配置了AI生成的体验，还配置了学生活动何时成为推断、学习者信息是否持久保存、谁可以访问这些信息，以及这些信息如何影响后续的人类行动。他们还面临将教学需求转化为配置的挑战：识别超出GenAI常见用途之外的控制可能性，将通用AI分解为可理解的功能和职责，以及通过预期的教学行动识别有用信息。我们讨论了GenAI系统如何支持教育者表达和测试配置，同时围绕个性化、推断、持久性、披露和行动建立边界，以保持AI支持的学习与不断变化的学习者需求相一致。
英文摘要：
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.
