How UDE activates large cohorts and makes learning gaps visible earlier
From one course to faculty-wide usage with didactically useful analytics.
About the institution and contact person
In large foundational courses, activating students is difficult. At the same time, it is often unclear during the semester which topics are not truly understood. Prof. Erwin Amann focused on this gap early - and on the reality that AI was already being used, but rarely embedded in teaching.
“In courses with hundreds of students, activation is often a challenge. At the same time, it is practically impossible to make deficits and learning progress visible in time and in a systematic way.”
Prof. Dr. Erwin Amann
Dean, University of Duisburg-Essen
Decision for a Controlled Course Framework
UDE did not want AI as an open black box. The goal was a system grounded in its own teaching materials. This ensured reliable support while fostering responsible AI use within academic teaching.
“We chose acemate because it lets us use our teaching materials as the core knowledge base and places AI into a controllable framework.”
Rollout without a Major IT Project
Implementation started as a pilot. A key requirement was to launch without heavy IT projects and to scale step by step, instead of changing everything at once.
“The pilot could be launched without major IT projects. That gave us the ability to scale gradually.”
Outcome in Teaching Operations
For the faculty, the value was primarily didactic and organizational. Teaching became more data-informed, activation improved and materials could be refined more precisely because difficult topics became visible much earlier.
“We can see which topics are truly difficult, improve materials and exercises in a targeted way and thereby increase motivation and engagement.”