针对语言学习者需求，提出个性化职业场景教学建议
Evaluating LLM-Generated Lessons from the Language Learning Students' Perspective: A Short Case Study on Duolingo
- 基于用户反馈，分析通用与职业场景的互补性教学设计
- 5名菲律宾员工中，通用场景出现频次高于工作场景
- 建议融合通用与领域特定内容，实现基础与专业双提升
主流语言学习应用如Duolingo使用大语言模型生成课程，但多数内容聚焦于日常通用场景（如问候、点餐、问路），缺乏职业相关语境支持。这一缺失可能阻碍学习者达成专业级流利度——即在目标语言中自如表达工作与领域相关信息的能力。我们对菲律宾一家跨国公司的5名员工进行了调研，结果显示：受访者遇到的通用场景远多于工作场景；前者有助于建立基础语法、词汇与文化认知，后者则包含领域专用词汇，能有效填补向专业流利度过渡的空白。综合分析发现，每位参与者建议的场景存在显著差异。据此，我们建议语言学习应用应通过个性化机制，生成适配个体需求的领域特定课程，同时保留通用、可共鸣的基础课程以维持学习连贯性。
原文摘要 · Abstract (English)
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 when 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.
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