Location-based games have become a worldwide phenomenon, but their multiplayer options typically are either superficial or force players to play together only locally. We introduce a system that compares game areas around the globe and matches points of interest onto each other, allowing for simultaneous gameplay across cities and countries. Thereby, we achieve game areas with an equal representation of their point of interest structure, allowing for fair and equal starting conditions. Our approach procedurally generates content for multiple players based on factors such as spatial relation and cultural relevance. Each generated game area can be adapted in size, orientation, and cultural location selection criteria to provide a level playing field for players with different mobility patterns or within areas with different sociocultural conditions. The content creation process utilizes publicly available OpenStreetMap data and achieves comparable results of high quality even in suburban or rural areas, where today’s location-based games usually have a lower content density.

Practice is essential for language learning. This is true for writing as well as speaking. However, in contrast to writing, it can be challenging to offer students sufficient time to practice speaking while receiving corrective feedback from a teacher. Considering the importance of corrective feedback for language mastery, automatic feedback systems could provide vital assistance through additional supervised speaking exercise. For this reason, this paper proposes an end-to-end feedback generation pipeline to correct grammar errors in unconstrained speech. The approach consists of four steps, converting raw speech files into transcripts with automatic speech recognition, removing disfluency, correcting grammatical errors, and preparing the feedback for presentation to the user. An explorative analysis of the pipeline with English language learners indicates that out-of-the-box automatic speech recognition models degrade in performance when used by language learners. However, training the model with only 15 minutes of learners’ speech decreases the word error rate almost by half.

Learners comprehend complex texts only if they understand the concepts involved, and assessing their comprehension works best if the assessment targets those concepts. Hence, assessment systems, such as question generators, often profit from learning-relevant concepts as input to solve their tasks. However, automatically detecting a given text’s relevant concepts is non-trivial. If a concept is deemed relevant heavily depends on the context and thus on automatic text understanding. Recent advancements in supervised machine learning improved state-of-the-art automatic text understanding. However, those methods need training corpora to learn the association between the relevant concepts and their text’s context. This work shows that textbooks comprise all the necessary information to construct such corpora. We introduce and evaluate an open-source automatic back-of-the-book index extractor and a corpus construction algorithm to derive training corpora from a set of high-quality PDF textbooks. We furthermore investigate to what extent state-of-the-art machine learning models can learn to extract concepts from constructed corpora. The results show that the models have decent performance and provide evidence generalization to unseen concepts.