Plan a coherent sequence
Proposed teaching guides will connect prerequisites, learning objectives, practice activities, and expected evidence. Educators will be encouraged to explain why a task matters before introducing a particular tool.
Education & standards
A practical foundation for AI education.
We are working toward clear learning outcomes, useful educator guidance, and assessments that show what learners can actually do with AI.
Contribute to education standards
Define the learning before choosing the tools.
A useful education standard should remain understandable when models, products, and teaching tools change.
Development status: the framework below is proposed. It is an invitation to shape our work, not a published standard or an existing accreditation scheme.
Our education initiative will connect AI literacy with practical judgment. Learners should understand what a system is doing, recognize the limits of its output, and make informed choices about when to use it. These capabilities belong alongside technical skills, rather than appearing only as an ethics lesson at the end.
We will develop guidance that educators can adapt to their learners, institutional setting, and available resources. A school club, a college course, and professional development will need different examples and levels of technical depth. Shared outcomes can provide direction while leaving room for those differences.
Proposed curriculum areas
Each area pairs knowledge with something a learner can explain, evaluate, or demonstrate.
| Area | Learning focus | Possible evidence |
|---|---|---|
| AI foundations | Models, training, inference, uncertainty, and common limitations. | Explain a system’s purpose and identify where its output needs checking. |
| Data and evaluation | Data quality, representative examples, measurement, and error analysis. | Compare outcomes using a small, documented evaluation set. |
| Applied workflows | Tool selection, iteration, reproducibility, and human review. | Build a workflow and explain the decisions behind it. |
| Responsible participation | Privacy, attribution, bias, access, and appropriate use. | Identify risks and document proportionate safeguards. |
Guidance educators can use
Assess the reasoning
Assessment guidance will consider the process as well as the result: how a learner checked an answer, responded to a failure, and justified a choice. A polished AI-generated artifact alone will not establish understanding.
Design for participation
Resources will consider differing device access, language needs, accessibility requirements, and prior experience. We will explore exercises that teach essential concepts without requiring expensive computing resources.
How the framework will develop
- Gather educational needs.
Invite educators and practitioners to identify unclear expectations, missing resources, and challenges in assessing AI learning.
- Draft and test the guidance.
Develop learning outcomes and sample activities, then seek feedback on their clarity, practicality, and suitability for different learners.
- Publish and maintain revisions.
Propose a documented review cycle so changes to criteria, examples, and terminology can be explained and tracked.
Bring the classroom perspective.
Tell us whom you teach, the outcomes you are working toward, and the guidance that would help you most.
Discuss education standards →