Understanding How Educators Configure GenAI Support for Open-Ended Learning -- An Exploratory Study of K-12 Career Exploration

Computer Science > Human-Computer Interaction — arXiv:2609.21019 (cs), submitted on 17 Sep 2026.

Title: Understanding How Educators Configure GenAI Support for Open-Ended Learning -- An Exploratory Study of K-12 Career Exploration

Authors: Si Chen, Xinyue Chen, Artur Mullagaliyev, Alexander Nwanganga, Shifu Hou, Deng Pan, Ronald Metoyer, Sugana Vijay Chawla.

The abstract notes that 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, the authors 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. The authors 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.

Comments: 32 pages. Subjects: Human-Computer Interaction (cs.HC). Cite as: arXiv:2609.21019 [cs.HC] (or arXiv:2609.21019v1 [cs.HC] for this version), https://doi.org/10.48550/arXiv.2609.21019 — arXiv-issued DOI via DataCite (pending registration).

Submission history: From Artur Mullagaliyev; [v1] Thu, 17 Sep 2026 19:15:28 UTC (5,888 KB).