Decision Intelligence for Higher Education
Helping universities and business schools make better educational decisions.
Connect your institutional knowledge.
Explore what could change.
One shared foundation
Your data integration layer brings institutional sources together. Dedicated AI agents put that shared knowledge to work for the people making educational decisions.
Educators
Could a different assessment change the learning experience?
Compare how a different assessment could affect motivation, satisfaction and performance, using a model built with educational experts.
Programme coordinators
Where is student progression getting stuck?
Spot where student progression stalls. Explore how changes to guidance or course design could help, using your institution’s data and co-built model.
Institutional leaders
Should we launch a new programme or renew an existing one?
Bring programme demand, progression and graduate outcomes into the same conversation. Explore options before committing resources.
Students
Where can I find the materials, methods and expectations for my course?
Practise an assessment or explore an applied case with formative feedback grounded in your course’s curated materials and educational approach.
Machine in the Loop
You frame the question and define what matters. Didaflow helps formalise the assumptions, explore consequences and examine the evidence.
Explore our methodologyHypotheses come from educators and domain experts. Data help refine them.
Inspect assumptions, sources and uncertainty. Challenge and revise the model.
Compare what could happen if the institution changes its conditions.
In practice
Two implementations, co-designed with the people who know their institution. Names remain confidential.
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.
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.
Alongside the platform
Consulting and training help your institution adopt AI in teaching and governance, with its own educational goals at the centre.
Talk about your institutionYour institution · Your next question
Bring a real educational decision.
Let’s explore how Didaflow could support it.
