Popular language learning applications such as Duolingo use large language models (LLMs) to generate lessons for its users. Most lessons focus on general real-world scenarios such as greetings, ordering food, or asking directions, with limited support for profession-specific contexts. This gap can hinder learners from achieving professional-level fluency, which we define as the ability to communicate comfortably various work-related and domain-specific information in the target language. We surveyed five employees from a multinational company in the Philippines on their experiences with Duolingo. Results show that respondents encountered general scenarios more frequently than work-related ones, and that the former are relatable and effective in building foundational grammar, vocabulary, and cultural knowledge. The latter helps bridge the gap toward professional fluency as it contains domain-specific vocabulary. Each participant suggested lesson scenarios that diverge in contexts hen analyzed in aggregate. With this understanding, we propose that language learning applications should generate lessons that adapt to an individual's needs through personalized, domain specific lesson scenarios while maintaining foundational support through general, relatable lesson scenarios.

翻译：热门语言学习应用程序如Duolingo使用大语言模型（LLMs）为其用户生成课程。大多数课程侧重于通用现实场景，如打招呼、点餐或问路，对职业特定情境的支持有限。这一缺口可能阻碍学习者达到专业流利水平，我们将专业流利定义为能用目标语言自如交流各种工作相关及领域特定信息的能力。我们对菲律宾一家跨国公司的五名员工进行了关于Duolingo使用体验的问卷调查。结果显示，受访者遇到通用场景的频率高于工作相关场景，且前者在构建基础语法、词汇和文化知识方面具有关联性和有效性；后者因包含领域特定词汇而有助于弥合通往专业流利的差距。每位参与者建议的课程场景在聚合分析后呈现出情境差异。基于这一认知，我们建议语言学习应用程序应通过个性化、领域特定的课程场景来适应个体需求，同时通过通用、关联性强的课程场景维持基础支持。