AI Dev Solutions shares MathAI, an AI-powered math tutoring platform designed to teach method, not just output. Instead of handing over a final result, the tutor guides students step by step with hints, explanations, and follow-up questions so the learner does the thinking. Built-in motivation mechanics like points for progress are paired with a teacher view that summarizes student performance, helping educators see where misconceptions start. https://www.aidevsolutions.dk/
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MathAI is positioned as a chat-based, interactive assistant that blends pedagogy with modern LLM-style prompting: scaffold a solution, verify each step, and keep the student articulating the reasoning in their own words. The product is being rolled out first for Danish schools, yet the demo shows bilingual readiness with Danish/English exercises, and the company describes personalized learning paths that adapt to a student’s level and pace. That “no final answer” policy functions as a guardrail against answer-spoonfeeding while still letting students validate each intermediate step in practice.
Later in the discussion, the team connects product design to math anxiety: many students label themselves “not a math person” early, then disengage long before topics become truly complex. The CEO points to decades of math teaching experience (including in Denmark and the MENA region), while the lead developer focuses on turning classroom practice into repeatable, stepwise guidance for everyday homework and in-class work. The interview was filmed at Web Summit Lisbon 2025, and their go-to-market plan is Denmark first, then Sweden and Norway, before scaling across Europe and the MENA region next.
For schools, the interesting question is how to integrate generative tutoring without eroding learning integrity: align help to curriculum objectives, keep explanations inspectable, and give teachers signals they can act on. Done well, AI tutoring can support mastery learning, reduce repetitive grading overhead, and provide extra practice without replacing human teaching. The next frontier is calibration and evaluation, so the tutor knows when to nudge, when to pause, and when to route a student back to a teacher.
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