How educators set up AI tools for student career exploration

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

Human-Computer Interaction

Summary

Using AI to help students explore career options has big potential but needs to match teaching goals. This study looked at how 15 U.S. teachers set up AI tools for career learning in a way that fits their classroom needs. The teachers decided not only what AI does but also how student info is used and shared, and when humans should step in. They faced challenges figuring out how to control AI in ways beyond basic uses and how to turn teaching goals into AI settings. The researchers suggest AI systems should let teachers create, test, and adjust these setups while keeping student info private and the learning aligned with student needs.

What this means in practice

  • For educational technology developers: Design AI tools that let teachers customize how AI supports student career learning and data use based on classroom goals.$Commercial implications: Enables development of configurable AI education software tailored to teacher preferences and student privacy needs.
  • For instructional designers: Create learning experiences incorporating AI that respect students' data and support teacher oversight by using configurable AI settings.

Authors

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

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.
View PDFOpen arXiv