  Since the COVID-19 outbreak, the use of digital learning or education
platforms has significantly increased. Teachers now digitally distribute
homework and provide exercise questions. In both cases, teachers need to
continuously develop novel and individual questions. This process can be very
time-consuming and should be facilitated and accelerated both through exchange
with other teachers and by using Artificial Intelligence (AI) capabilities. To
address this need, we propose a multilingual Wikimedia framework that allows
for collaborative worldwide teacher knowledge engineering and subsequent
AI-aided question generation, test, and correction. As a proof of concept, we
present >>PhysWikiQuiz<<, a physics question generation and test engine. Our
system (hosted by Wikimedia at https://physwikiquiz.wmflabs.org) retrieves
physics knowledge from the open community-curated database Wikidata. It can
generate questions in different variations and verify answer values and units
using a Computer Algebra System (CAS). We evaluate the performance on a public
benchmark dataset at each stage of the system workflow. For an average formula
with three variables, the system can generate and correct up to 300 questions
for individual students based on a single formula concept name as input by the
teacher.
