Leading by Example: The Use of Generative Artificial Intelligence to Create Pedagogically Suitable Example Sentences

Abstract

Several second language acquisition studies have argued in favour of practising vocabulary in authentic contexts. After the tradition of obtaining these usage examples by "invention" (i.e. language experts creating examples based on their intuitions) was superseded by corpus-based approaches (i.e. using dedicated tools to select examples from corpora), the rise of large language models led to a third possible "data source": Generative Artificial Intelligence (GenAI). This paper aims to assess GenAI-based examples in terms of their pedagogical suitability by conducting an experiment in which second language (L2) learners compare GenAI-based examples to corpus-based ones, for L2 Spanish. The study shows that L2 learners find GenAI-based sentences more suitable than corpus-based sentences, with -- on a total of 400 pairwise comparisons -- 265 artificial examples being found most suitable by all learners (compared to 10 corpus-based examples). The prompt type (different zero-shot and few-shot prompts were designed) did not have a noticeable impact on the results. Importantly, the GenAI approach also yielded a number of unsuitable example sentences, leading us to conclude that a "hybrid" method which takes authentic corpus-based examples as its starting point and employs GenAI models to rewrite the examples might combine the best of both worlds.