This paper from Educause was in George Siemen's presentation last week. It proposes a tightening of personalised learning through 'precision learning'.
The paper was published in Novermber 2025 with authors from the U S of A -
An important first step is to review institutional data architectures so that they can be drawn on to support the demands of adaptive and personalised learning. Real-time adaptivity is required so that 'just-in-time' feedback is availed to learners. Hence, in the article, the following is required:
"Delivering real-time recommendations and interventions that improve learning outcomes requires a fundamentally reimagined architecture. Instead of static, siloed data collection, institutions need systems capable of dynamic ingestion, immediate processing, and centralized analysis of learner interactions and performance. Such an architecture might leverage event streaming platforms, robust APIs, cloud-native databases, or other technologies to make data available and actionable the moment it is created. With this shift, educational systems can identify learning gaps, trigger personalized recommendations, and adapt curricula in real time—moving from post hoc analysis to proactive support."
The combination of agile learner data and LLM can then allow for real-time support and intervention. Then, combining the above with learning profile and curriculum leads to 'precision learning'.
The paper provides a way forward, explained in lay language for those with little computer science background. Now the first steps are to find out how my institute stores their learner data and see how complex or large the task ahead will be to format and provision precision learning!
