  Digital learning platforms enable students to learn on a flexible and
individual schedule as well as providing instant feedback mechanisms. The field
of STEM education requires students to solve numerous training exercises to
grasp underlying concepts. It is apparent that there are restrictions in
current online education in terms of exercise diversity and individuality. Many
exercises show little variance in structure and content, hindering the adoption
of abstraction capabilities by students. This thesis proposes an approach to
generate diverse, context rich word problems. In addition to requiring the
generated language to be grammatically correct, the nature of word problems
implies additional constraints on the validity of contents. The proposed
approach is proven to be effective in generating valid word problems for
mathematical statistics. The experimental results present a tradeoff between
generation time and exercise validity. The system can easily be parametrized to
handle this tradeoff according to the requirements of specific use cases.
