arXiv:2512.23136v2 Announce Type: replace Abstract: Code-switching (CSW), the use of multiple languages within the same interaction, has been widely studied in NLP for multilingual communication. While CSW also occurs in language-learning contexts, the functions and pedagogical implications of learner CSW in learner-AI interactions remain underexplored. To address this gap, we introduce LAKE, a dataset of Learner-AI dialogues with Korean-English CSW. This dataset captures South Korean learners' CSW behavior during English speaking practice with LLM-based conversation partners, comprising 675 dialogues, 19.6K utterances, and 43.9 hours of learner speech. We develop a taxonomy of learner CSW functions, finding that learners switch to Korean for diverse purposes, including signaling lexical gaps, negotiating meaning, and conveying culturally specific content. We also observe learners reusing and adapting English expressions introduced by the LLM partner following their code-switches, suggesting opportunities for contextualized lexical support. To support research on speech recognition for learner CSW, we release 4.9 hours of code-switched learner speech with human-verified transcripts and benchmark speech-to-text models on Automatic Speech Recognition (ASR), revealing difficulties in recognizing learner CSW. These findings highlight learner CSW as a communicative and pedagogically meaningful resource that can inform the design of AI-based conversational language learning systems.