OpenAI has expanded the OpenAI Academy with new role-focused courses aimed at developers, leaders, educators, and college students. These courses complement the existing Apply AI at Work pathway, which concentrates on foundational skills and creating repeatable AI workflows for everyday work.
Why role-based learning?
OpenAI frames safe and effective AI use as a skill that requires practice: people need to learn how to apply models to concrete tasks — whether improving routine processes, building products, leading teams, or teaching. Role-based pathways are intended to help organizations establish shared habits for working with AI and to develop the specific skills required by different roles.
Learning approach and assessment
OpenAI Academy courses follow the principle of "use AI to learn AI": learners practice on real tasks, give instructions, add context, and review results continuously. Courses include assessments so participants can demonstrate their competencies; passing an assessment awards an OpenAI Academy course badge.
Main pathways and focus areas
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Apply AI at Work: This foundational path teaches everyday AI usage—writing clear instructions, adding relevant context, and reviewing outputs against task requirements. Learners convert successful approaches into reusable workflows and progress to directing larger pieces of work with agents, deciding what to delegate and where to include checkpoints and human review.
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Build with AI: Designed for developers and technical teams using Codex or the OpenAI API. The courses cover planning and implementing changes across the software development lifecycle while maintaining review and quality control. For API-based teams the path addresses solution design, evaluations, agents, information retrieval, and operating AI systems in production.
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Lead AI Adoption / AI Leadership: Aimed at people responsible for strategy, adoption, and change management. The course helps assess where AI can create business value, link initiatives to business priorities, define ownership and governance, and develop an initial AI strategy and roadmap for adoption.
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AI for Educators and AI for College Students: These extend the portfolio into teaching, learning, and career preparation. In AI for Educators, instructors use materials they have permission to use to plan classes, create activities or assessments, and evaluate ChatGPT responses against learning objectives and source materials. AI for College Students focuses on academic and career tasks such as organizing readings and deadlines into a study plan, assigning roles in group projects, reviewing drafts against assignment requirements, and preparing applications or interviews.
Organizational use and rollout
Organizations can mix and match pathways to match their goals. For example, a company might use Apply AI at Work for onboarding employees, offer Build with AI to developers, and include AI Leadership in executive or transformation programs. Educational institutions can combine AI Foundations with AI for Educators and AI for College Students.
OpenAI emphasizes that learning is most effective when participants have time to practice and apply it: managers and Champions can encourage learners to bring real tasks into courses, share examples, and connect learning to ongoing work. The OpenAI Academy Deployment Guide provides recommendations for introducing courses, engaging leaders and managers, building participation, and tracking progress.
Development and updates
The courses were shaped by teams across OpenAI and will be updated over time as models, products, and guidance evolve.
Visit OpenAI Academy to explore the expanded course portfolio and identify suitable learning paths for your teams.
