Can AI replace traditional language learning? A new study says not yet
When university students set out to learn English as an additional language, it's not just about internalizing a new list of words; they also have to learn common word combinations.
In academic settings, mastering collocations—common word combinations—proves particularly challenging for language learners transitioning to terminology-rich fields such as economics and engineering. A new study from the University of British Columbia Sauder School of Business indicates that while data-driven learning (DDL) techniques are the most reliable method for teaching these linguistic patterns, incorporating generative AI (GenAI) can enhance the process.
The study, authored by Dr. Déogratias (Deo) Nizonkiza, underscores the pivotal role of corpora—extensive collections of real-world language data—since their inception in the 1960s with the Brown Corpus, and their significant expansion with the Corpus of Contemporary American English (COCA) in the 1980s and 2008 respectively. Although GenAI tools streamline the discovery of collocations, they could lack transparency and accuracy.
Hence, a hybrid learning approach integrating both traditional DDL methods and GenAI is recommended. The study proposes a hybrid teaching strategy where instructors identify target vocabulary, discover common collocations using established corpora, and then employ GenAI tools to generate examples and provide feedback. This method not only aids students in recognizing collocations and improving accuracy but also promotes critical engagement with AI technologies.
Written by urgent.news from Phys.org's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
