Digital transformation maturity of schools and universities

The framework assesses the digital maturity of an education institution across its entire value chain: governance and strategy, infrastructure and learning spaces, student records information systems, digitally supported teaching practices, data and performance management, student services, staff and teacher skills, security and data protection. Each theme is scored on a progressive five-level scale, from an absence of practice to a reviewed and documented one. The framework applies equally to a single school and to a multi-site university, a network of campuses or a private higher education group.

In practice, the challenge is not knowing whether the institution is equipped. It is knowing what that equipment actually delivers. Do teachers use the learning platform beyond uploading documents, or to design their courses? Can student records, campus life and research data be cross-referenced, or does each department keep its own file? Who decides on the purchase of a digital tool: senior management, IT, the faculty, or the individual teacher? These questions come up at board meetings without a shared answer, because no common measure exists across departments.

The digital transformation of education has also changed in nature. After a rapid equipment phase, the focus has shifted to the quality of usage, blended learning, accessibility, learners’ digital identity and, more recently, the use of generative AI by students and teachers alike. A common mistake is to treat this last topic as a matter of tools. It is first and foremost a question of teaching policy, assessment rules and staff training. The assessment places it alongside the other dimensions of maturity.

This framework is not a certification and does not prepare for any external audit. It measures a trajectory. An audit ends with a gap. The maturity assessment answers a different question: what level of proficiency does each practice sit at, what target has the institution set for itself, and what specific action moves it up to the next level. It is this gap between the current score and the target score that feeds the action plan, prioritised by AI into a roadmap.

The framework is ready to use and adaptable. You can change the themes, rephrase the questions to match your own vocabulary (faculty, school, department, campus) or adjust the levels. The AI can also build a version tailored from your own documents: digital master plan, performance contract, self-assessment report. A university can thus assess each faculty with the same grid, then compare results across faculties and over time.