The problem
The idea behind this concept is to help teachers, school managers and parents with what matters to them most: students. It addresses what parents look for nowadays: that their children are treated as individuals, so schools take their strengths and weaknesses into account and personalise parts of their teaching to boost their academic performance. In other words, to be ready for the future.
Schools manage a large amount of data about their students. We just need to add some AI layers and a platform where all the stakeholders can get the insights they need to take action on students' performance.
How it works
ALMA sits on top of a school's existing operational data (grades, attendance, behaviour, and wellbeing or psychologist notes where available) and runs a set of specialised AI agents that turn that data into role-specific, actionable insight for four stakeholder groups:
- School owners and directors: business-level dashboards with retention risk, cohort performance trends, and staffing and programme ROI.
- Teachers and psychologists: a per-student "method and motivation" copilot. It shows which teaching approach and motivational lever is working, and what to try next.
- Parents: plain-language progress reports and early notice when a path needs to change.
- Students (age-appropriate): feedback that builds self-awareness of effort, strengths and next steps.
The differentiator
The differentiator is not the dashboards: every school management system has dashboards. It is the personalisation engine: a system that models the individual child well enough to recommend a method, not just report a grade.
This is what I like most about AI: it can give us insights we cannot see unless we dedicate a lot of time to improving many parts of a business.
All experiments