Preparing Students for AI-Integrated Workplaces: Curriculum Design, Skill Development, and Career Readiness

The rapid integration of AI across industries is fundamentally reshaping workplace expectations and skill requirements. While AI tools become increasingly prevalent in professional environments, educational institutions face the challenge of preparing graduates who can effectively collaborate with, leverage, and critically evaluate AI systems in their careers. Unlike research focused on using AI as a teaching tool, this Research Topic investigates what students need to know about and do with AI once they enter the workforce. We seek research that bridges the gap between academic learning and professional AI literacy, exploring how curricula can be designed to develop both technical competencies and critical thinking skills necessary for AI-integrated work environments. This collection addresses the urgent need for evidence-based approaches to workforce preparation in an AI-transformed economy.

This Research Topic addresses the critical gap between academic AI education and workforce readiness by examining how educational institutions can effectively prepare students for AI-integrated professional environments. The primary problem is that while AI adoption accelerates across industries, educational programs often lack systematic approaches to developing the specific competencies graduates need to succeed in AI-enhanced workplaces. Recent advances in AI applications across sectors, from healthcare and finance to manufacturing and creative industries, have created new skill requirements that traditional curricula do not address.

By contrast to broader explorations of how AI reshapes pedagogy or the classroom environment, this Research Topic is explicitly centered on the transition from academic study to professional employment. Specifically, our focus is on workforce preparation and employability in AI-integrated industries, distinguishing this collection from work that primarily addresses teaching and learning dynamics within educational institutions.

We aim to foster evidence-based solutions through research on curriculum frameworks that integrate AI literacy across disciplines, assessment methods for AI-related competencies, and industry-academic partnerships that ensure alignment between educational outcomes and employer needs. This collection seeks to advance understanding of how institutions can develop students' technical proficiency with AI tools while building the critical thinking and ethical reasoning skills essential for responsible AI use in professional contexts.

This Research Topic invites original research, theoretical analyses, case studies, and comprehensive reviews exploring AI workforce preparation across educational levels, with particular emphasis on practical applications and industry relevance. We particularly encourage submissions that include industry perspectives, longitudinal outcome data, and cross-institutional collaborations. We welcome quantitative, qualitative, and mixed-methods studies, as well as conceptual frameworks and policy analyses that demonstrate clear connections to professional practice and employability outcomes. Potential themes for submissions include, but are not limited to:

• Applied curriculum design and AI competency frameworks across disciplines with demonstrated workplace relevance
• Industry-validated assessment and evaluation methods for AI-related skills and professional readiness
• Industry-academic partnerships and employer feedback mechanisms that inform curriculum development
• Student preparedness, confidence, and career outcomes in AI-integrated fields with employment tracking data
• Professional development and certification programs that bridge academic and industry requirements
• Regional, cultural, and disciplinary variations in AI workforce needs and educational responses
• Ethical considerations and responsible AI use in professional contexts and workplace training

Submissions should clearly articulate the practical implications for workforce development and demonstrate relevance to industry needs and employment outcomes.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Pedagogical Innovation, Ethical AI Integration, Personalized Learning, AI Literacy Development, Prompt Engineering, Educational Equity, Human-AI Collaboration, Risk Mitigation Strategies

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.