Flexiformal documents – i.e. documents with embedded semantic annotations that make some aspects of their content machineactionable – can be instrumented to make interaction with the underlying knowledge more efficient and effective. Fostering such interactions via semantic services has proven very successful in university education, but the practical applicability is limited by the cost of flexiformalization. A method for lowering (flexi)-formalization costs is to use modular representations to profit from enhanced source sharing and induced (generated) content. In fully formal environments this is well-understood and implemented in many systems. In this paper we show that many of the formal techniques carry over to the informal setting if we parse (rigorous) natural language with a semantically optimized grammar and work on abstract syntax trees instead of formulae. We present i) a set of use cases for generating learning material to be used in an educational setting – concretely in the field of theoretical computer science, ii) a GF grammar that allows to syntactically analyze the underlying language fragment, iii) a set of AST-to-AST simplifications that can be used to fine-tune the wording and formulae of the generated content and adapt it to the scientific jargon, and iv) a prototypical implementation that shows the technique in action.