AI4CAREER: Responsible AI for STEM Career Development at Scale in K-16 Education
Sugana Chawla, Si Chen, Julia Qian, Gina Svarovsky, Alison Cheng, Rick Johnson, Nitesh V. Chawla, Ronald Metoyer

TL;DR
This paper discusses the development and governance of responsible AI tools to support STEM career exploration and readiness across K-16 education, emphasizing equity, developmental alignment, and ethical considerations.
Contribution
It introduces a framework for responsible AI design and governance tailored for STEM career development in K-16 settings, addressing ethical, developmental, and equity challenges.
Findings
Identified key themes for responsible AI in STEM career development.
Outlined design tensions and governance principles for AI tools.
Highlighted research gaps and future directions in responsible AI use.
Abstract
Rapid advances in artificial intelligence (AI) are reshaping how students imagine, explore, and prepare for STEM careers across K-16 education. As AI systems increasingly influence feedback, advising, and access to information about opportunities, they are becoming part of the developmental infrastructure that shapes career identity formation and readiness. Yet uncertainty remains about how AI-supported career exploration tools should be designed, governed, and evaluated at scale, particularly across developmental stages and institutional contexts. This half-day workshop convenes researchers, educators, practitioners, and policymakers to examine responsible AI for STEM career development. We focus on four themes: (1) how AI reshapes definitions and assessment of STEM career readiness; (2) appropriate roles and boundaries for AI in career decision-making; (3) developmental alignment of…
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Taxonomy
TopicsCareer Development and Diversity · Teaching and Learning Programming · Intelligent Tutoring Systems and Adaptive Learning
