Low-resourced and marginalized languages suffer from generational attrition, with dwindling incentives for younger speakers to learn a language to which they may be linked culturally but which offers limited opportunities for use in education, employment, and other domains of everyday life. Gamification techniques and the integration of digital support structures may hold considerable potential for language revitalization and digitization efforts. This work explores a research design that explores approaches targeting both extrinsic and intrinsic motivation for learners of Kurdish varieties across three contrasting learning environments. By running comparable conditions in environments that differ sharply in institutional pressure, cost, and voluntariness, we aim to produce a multifaceted account of how gameful design and behavior-oriented guidance interact with learner contexts, and thereby inform future initiatives in low-resource language learning.

This work introduces a dialect-aware text filtering framework to pre-process, clean, and enhance large text corpora, creating variety-specific sub-corpora for neglected language varieties. We apply our framework to Kurdish, a language with rich dialectal diversity, which presents significant challenges for Natural Language Processing due to its low-resource status and the noisy nature of available text corpora. Leveraging lexicographic features, we assign multi-language-labels to text instances and synthesize over 130 dialect specific corpora from large “noisy” data sets containing unlabeled mixtures of Kurdish varieties, representing to our knowledge the largest collection of dialect-specific Kurdish NLP resources to date. This work contributes to the creation of low-resource language technology foundations, especially dialect-specific NLP applications. Specifically, we advance research on Kurdish languages by providing insights into the linguistic relationships among Kurdish varieties.