AI in Education
What AI in Education Needs Next: Lessons from Youth Leaders Across Five Countries
Field signals from five countries, translated into what education systems and funders should do next.
This note synthesises what youth leaders and practitioners are seeing in classrooms, unions, and ministries — and what it implies for the next phase of AI in education policy.
What changes when implementation leads
The most durable lessons are not about tools alone. They are about trust, evaluation capacity, and the redesign of assessment when generative models are ubiquitous.
For institutional readers
Use this as a checklist for where to invest next: governance, teacher judgement, procurement, and cross-sector dialogue.
About the author
Qiqing He works at the intersection of artificial intelligence, workforce transformation, education, and public-interest institutions, translating technical change into institutional readiness.