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Expanding OpenAI Academy with new learning paths
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Expanding OpenAI Academy with new learning paths

Using AI safely and effectively takes practice. People need to learn how to apply it to the work in front of them, whether they’re improving a daily workflow, building a product, leading a team, or teaching a class.

At OpenAI, we treat learning as part of deployment. Today, we’re expanding OpenAI Academy with new courses for developers, leaders, educators, and college students. These join Apply AI at Work, which helps people build foundational skills, create repeatable workflows, and direct work with agents.

Together, the pathways help organizations build shared habits for working with AI and develop the skills different roles require. Course assessments give learners a way to demonstrate what they’ve learned. Learners earn an OpenAI Academy course badge by passing the course assessment.

Choose a learning path for the work you do

OpenAI Academy ⁠(opens in a new window)courses are built around a simple idea: you should use AI to learn AI. Learners practice on real tasks, giving instructions, adding context, and reviewing results as they go.

Organizations can combine Apply AI at Work with focused learning for developers, leaders, educators, and students, choosing the courses that support their teams’ work.

Knowledge workers: Apply AI at Work

The Apply AI at Work⁠(opens in a new window) pathway helps people use AI more effectively in their everyday work. Learners start with the basics, practicing how to give clear instructions, add relevant context, and review responses against the task at hand. They then turn successful approaches into workflows they can reuse.

As learners progress, they practice directing larger pieces of work with agents, deciding what to delegate and where to add checkpoints and human review. The pathway helps employees take on more complex tasks with AI while retaining responsibility for the final result.

Developers: Build with AI

These courses are designed to help organizations move from experimentation to useful, reliable AI systems.

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