AI4CAREER: Responsible AI for STEM Career Development at Scale in K-16 Education

• URL: http://arxiv.org/abs/2603.02568v1
• Date: Tue, 03 Mar 2026 03:42:19 GMT
• Title: AI4CAREER: Responsible AI for STEM Career Development at Scale in K-16 Education
• Abstract summary: This half-day workshop convenes researchers, educators, practitioners, and policymakers to examine responsible AI for STEM career development.<n>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 AI supports across the K-16 continuum; and (4) equity-related design considerations that prevent the reproduction of structural disparities.
• Score: 22.121795663062084
• License: http://creativecommons.org/licenses/by/4.0/
• 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 AI supports across the K-16 continuum; and (4) equity-related design considerations that prevent the reproduction of structural disparities. Through lightning talks, structured group activities, and cross-sector dialogue, participants will surface design tensions, articulate governance principles, and identify research gaps. The workshop aims to advance shared language and actionable frameworks for responsible, developmentally grounded AI use in STEM career learning at scale.

Related papers

• Educating the Agentic Engineer: Curricula, Collaboration, and Continuous Learning in the AI Era [0.0]
  Generative and agentic artificial intelligence (AI) are reconfiguring software and systems engineering from a discipline centered on human authorship to one focused on directing, verifying, and governing autonomous systems.<n>This article presents an integrative conceptual synthesis across engineering education, computing education, human-AI interaction, human factors, and the learning sciences to derive an evidence-grounded educational architecture for this archetype.
  arXiv  Detail & Related papers  (2026-07-31T16:45:36Z)
• Reshaping Undergraduate Computer Science Education in the Generative AI Era [85.78707817763434]
  Generative AI represents a turning point for Computer Science (CS) education.<n>To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts.<n>We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI.
  arXiv  Detail & Related papers  (2026-05-02T05:26:24Z)
• The AI Pyramid A Conceptual Framework for Workforce Capability in the Age of AI [2.134211474877041]
  Recent evidence shows that generative AI disproportionately affects highly educated, white collar work.<n>This paper proposes the AI Pyramid, a conceptual framework for organizing human capability in an AI mediated economy.<n>The framework has implications for organizations, education systems, and governments seeking to align learning, measurement, and policy with the evolving demands of AI mediated work.
  arXiv  Detail & Related papers  (2026-01-10T09:27:56Z)
• Beyond Automation: Rethinking Work, Creativity, and Governance in the Age of Generative AI [0.0]
  generative artificial intelligence (AI) systems are reshaping the nature, distribution and meaning of work, creativity, and economic security.<n>This paper investigates four inter-related phenomena in the current AI era: (1) the evolving landscape of employment and the future of work; (2) the diverse patterns of AI adoption across socio-demographic groups, sectors, and geographies; and (3) whether universal basic income (UBI) should become a compulsory policy response to the AI revolution.
  arXiv  Detail & Related papers  (2025-12-09T20:25:24Z)
• Integration of AI in STEM Education, Addressing Ethical Challenges in K-12 Settings [0.0]
  The rapid integration of Artificial Intelligence into K-12 STEM education presents transformative opportunities alongside significant ethical challenges.<n>This paper examines the dual-edged impact of AI in STEM classrooms, analyzing its benefits (e.g., adaptive learning, real-time feedback) and drawbacks (e.g., surveillance risks, pedagogical limitations) through an ethical lens.
  arXiv  Detail & Related papers  (2025-10-22T03:07:26Z)
• Look: AI at Work! - Analysing Key Aspects of AI-support at the Work Place [39.146761527401424]
  We look at the areas of AI that the applications are concerned with.<n>This includes the importance of high-quality data for training learning-based systems.<n>In terms of the psychological factors we derive research questions to investigate in the development of AI supported work systems.
  arXiv  Detail & Related papers  (2025-09-02T12:51:23Z)
• AI4Research: A Survey of Artificial Intelligence for Scientific Research [55.5452803680643]
  We present a comprehensive survey on AI for Research (AI4Research)<n>We first introduce a systematic taxonomy to classify five mainstream tasks in AI4Research.<n>We identify key research gaps and highlight promising future directions.
  arXiv  Detail & Related papers  (2025-07-02T17:19:20Z)
• Generative AI Literacy: Twelve Defining Competencies [48.90506360377104]
  This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI.<n>The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations.<n>These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly.
  arXiv  Detail & Related papers  (2024-11-29T14:55:15Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Competency Model Approach to AI Literacy: Research-based Path from Initial Framework to Model [0.0]
  Research on AI Literacy could lead to an effective and practical platform for developing these skills. We propose and advocate for a pathway for developing AI Literacy as a pragmatic and useful tool for AI education.
  arXiv  Detail & Related papers  (2021-08-12T15:42:32Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.