Decision Intelligence for higher education

Helping universities and business schools make better educational decisions.

We bring your institution’s data together and help you assess a change before you make it.

Platform

One data foundation for the whole institution

Didaflow connects your institution’s data sources in a single layer. On top of it, dedicated AI assistants support teachers, programme coordinators, leaders and students.

Educators

Improve a course

Could a different assessment change the learning experience?

An example

Reconsider the assessment

Compare how a different assessment could affect motivation, satisfaction and performance, using a model built with educational experts.

Explore teaching support

Programme coordinators

See where students get stuck

Where is student progression getting stuck?

An example

Find the bottleneck

Spot where student progression stalls. Explore how changes to guidance or course design could help, using your institution’s data and co-built model.

Explore programme support

Institutional leaders

Decide which programmes to offer

Should we launch a new programme or renew an existing one?

An example

A new programme, or a better existing one?

Compare demand, student progression and graduate outcomes. Look at the options before committing resources.

Explore governance support

Students

Study with your own course materials

Where can I find the materials, methods and expectations for my course?

An example

Practise for the exam

Practise an assessment or explore an applied case with formative feedback grounded in your course’s curated materials and educational approach.

Explore student support

Method

Machine in the Loop

Decisions stay with people

Teachers and experts set the questions and the hypotheses. Didaflow turns them into a model, simulates the consequences and shows the evidence behind them.

Read the methodology
  1. Hypotheses come from experts

    Data are used to test and refine them.

  2. Everything can be checked

    Assumptions, sources and uncertainty are visible. The model can be challenged and revised.

  3. Interventions are compared

    We estimate what happens if the institution changes something, before it does.

In practice

Two projects with real institutions

Designed with the people who work there. Names are confidential.

A public university

Monitoring programmes and understanding dropout

The question

How can an institution bring fragmented data together to monitor its programmes, understand dropout and support educational decisions?

The implementation
A monitoring platform integrates institutional data. A proprietary causal model, co-built with domain experts and educational scientists, enables what-if simulations within the same platform.
Current status
Platform ready. The work connects programme monitoring to the exploration of interventions.

A business school

A student assistant and tools for staff

The question

How can fragmented institutional knowledge support students and decision-makers while keeping educational responsibility with the school?

The implementation
A contextualised student assistant uses curated materials and is co-designed with stakeholders. Dedicated agents support educators, coordinators and administrators, using a co-built causal model to explore the impact of interventions.
Current status
Tools ready. A shared data foundation supports both learning and institutional decisions.

Training and co-design

People learn the tools by designing them with us

A new tool works only if the people who use it understand it. So training is part of the project: we design each tool with the teachers, coordinators, leaders and students who will use it, and while they shape it they learn how to use it and when not to trust it. We also offer consulting and training on AI in teaching and governance.

Get in touch

Commons

Open tools for teachers and students

We publish free resources for the education community, with sources and dates. The first is the AI Map: which AI tools to use for which tasks, with the evidence and the rules that apply.

  • Available

    AI Map

    Students, teachers and instructional designers: compare AI tools task by task.

    Open the AI Map
  • In preparation

    Autonomy Budget

    A guided exercise on who gains and who loses autonomy when an AI tool comes in.

  • In preparation

    Monthly notes

    New studies, product changes and new rules, every month.

All open tools

Contact

What would you like to change?

Tell us about a decision you are facing.
We will show you how Didaflow can help.

Book a demo