Adaptive Learning Algorithms in a Mobile Application for Foreign Language Learning

This paper presents an adaptive approach to foreign-language learning in a mobile application based on spaced-repetition algorithms. The approach implements principles of the Ebbinghaus forgetting curve and a spaced repetition system (SRS) to dynamically schedule presentations of learning material. Flashcards provide a multimodal representation of vocabulary items - textual form, transcription, an in- sentence example, and audio accompaniment for training listening and pronunciation. The translation is revealed only after a tap, which promotes active recall; knowledge is reported via gestures: swipe right - known, swipe left - unknown, swipe up - learned (the card is archived and won’t reappear). The mechanism aligns with SM-2-style modeling: intervals automatically increase for well-learned items and shrink for difficult ones, keeping the system focused on “weak” words and avoiding excessive repetition of familiar material. The algorithmic integration is specified: the data model stores the last review date, interval, repetition count, knowledge rating, and an ease factor, enabling personalization of the schedule to individual memory characteristics. Interface decisions combining gesture interaction with audio accompaniment are demonstrated, and the expected effects are outlined: longer-term retention of vocabulary, fewer redundant exposures, and stronger motivation due to transparent progress feedback. The approach is scalable and suitable for further extensions (e.g., parameter tuning or data-driven models for scheduling the next review) without requiring complex infrastructure on mobile platforms.