Our Educational AI approach
"Educational AI starts with educational content, learning objectives and didactic frameworks. Rather than generating text freely from open data, Educational AI operates with controlled content, explicit curriculum alignment, and clearly defined pedagogical and didactical principles."
At Sanoma Learning, we focus our efforts on Educational AI. Unlike generic AI, which is broad and for general use, Educational AI is purpose‑built for learning and teaching*. It is integrated into learning methods and designed around what schools actually need. Our approach is grounded in three pillars:
Pedagogy first: Every feature is grounded in national curricula, trusted learning content and Sanoma’s didactic models, not generic prompts.
Co‑created with teachers: Educational AI should reflect real classroom needs. That means developing together with teachers and schools across our markets, shaped by real workflows and constraints.
Trusted European AI: Educational AI must be safe, compliant and trustworthy, fully aligned with EU privacy and AI regulations, and backed by a European education partner that understands how schools work.
* In line with the OECD (Organisation for Economic Co‑operation and Development) Digital Education Outlook 2026.
How AI can help teachers and students
In practice, we believe AI can play an important role in saving teachers’ time and improving students’ learning outcomes through personalisation.
Teachers’ time is one of the scarcest resources in K12 education. Purpose-built AI can reduce workload by supporting recurring, time-consuming tasks without taking decisions away from teachers.
The goal is simple: more time for high‑impact teaching. With AI tools that help increase the efficiency of recurring tasks, teachers can focus more on what teachers do best: guidance, motivation, classroom interaction, and support for individual learners.
In our view, AI should help teachers:
- Differentiate learning materials more easily, for example by creating practice at the right level for different students
- Support assessment workflows, for example, by assisting with reviewing open answers or suggesting feedback that teachers can adapt
- Prepare and adapt lessons faster, while staying aligned with curriculum and classroom context
Personalisation is one of the most important shifts ahead. With AI embedded into learning materials, personalisation becomes faster and more precise: content, pace, and support can adjust based on how the student is progressing week by week, and increasingly even in real time.
This is not about replacing the teacher-student relationship. Instead, this approach complements the teacher’s role, strengthening their ability to meet individual learners’ needs while keeping oversight and making the final pedagogical choices.
Students benefit when AI can provide:
- Targeted practice and explanations aligned with what they’re learning
- Support at home with more engaging homework and tutoring-like help
- Feedback loops that help learners understand mistakes and build mastery