Abstract
This paper presents and tests AllAI, an app that utilizes state-of-the-art NLP technology to assist second language acquisition through a novel method of sentence-based spaced repetition. Diverging from current single word or fixed sentence repetition, AllAI dynamically combines words due for repetition into sentences, enabling learning words in context while scheduling them independently. This research explores various suitable NLP paradigms and finds a few-shot prompting approach and retrieval of existing sentences from a corpus to yield the best correctness and scheduling accuracy. Subsequently, it evaluates these methods on 26 learners of Danish, finding a four-fold increase in the speed at which new words are learned, compared to conventional spaced repetition. Users of the retrieval method also reported significantly higher enjoyment, hinting at a higher user engagement.- Anthology ID:
- 2024.bea-1.29
- Volume:
- Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Ekaterina Kochmar, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 351–364
- Language:
- URL:
- https://aclanthology.org/2024.bea-1.29
- DOI:
- Cite (ACL):
- Benjamin Paddags, Daniel Hershcovich, and Valkyrie Savage. 2024. Automated Sentence Generation for a Spaced Repetition Software. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 351–364, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Automated Sentence Generation for a Spaced Repetition Software (Paddags et al., BEA 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.bea-1.29.pdf