On June 9, 2026, I spoke at the ASEM Joint Meeting of the Expert Group “SDGs & Education” and the Expert Group “Digitalisation & Artificial Intelligence”. The meeting focused on opportunities and challenges around inclusion, access, and autonomy with AI in higher education.

My presentation was titled From Training Machines to Teaching Students to Think with AI. The central argument was simple: methods education is about intellectual autonomy. In statistics, data analysis, and data science, students do not need manual routines as an end in themselves. But they do need enough understanding to ask sharper questions, evaluate AI-generated outputs, expose assumptions, and remain accountable for analytical work.

AI lowers operational friction. It helps with code generation, debugging, method explanations, documentation, and workflow design. That is useful. At the same time, some of the friction it removes was also where students learned to reason. The teaching challenge is therefore not to ban assistance, but to keep students inside the reasoning loop: ask, try, explain, and govern.

I discussed this from the perspective of large introductory statistics and data-analysis courses with about 800 students per year. At that scale, AI-era teaching quickly becomes an infrastructure question. Good learning design needs repeated practice, visible feedback, randomised and varied tasks, transfer across contexts, and privacy-aware ways for teaching teams to see where students struggle.

That is why my practical conclusion was institutional rather than merely individual. Universities need more than access to AI tools. They need deployment infrastructure, AI inference capacity, orchestration skills, and governance routines that let lecturers build and maintain course-specific support responsibly. Agentic coding can make small teaching apps easier to build, but hosting, authentication, monitoring, data access, privacy, and maintenance remain institutional responsibilities.

The presentation also connected naturally to the broader governance discussion in the expert group. Teaching and governance are two sides of the same problem: students need autonomy, and institutions need capacity. Adoption without capacity risks hidden dependence; responsible use requires that students, lecturers, and universities know where judgement and accountability remain.

My main takeaway from the talk was this: teach students to think with AI, not to watch AI think for them.