Good Fences Make Good Learning: How Self-Directed Language Learners Navigate LLM Delegation Decisions

Self-directed language learners increasingly turn to large language models (LLMs) for assistance, but face the challenge of deciding what learning tasks to delegate to LLMs and how. While prior research has examined the effectiveness of LLM in improving language proficiency, less is known about how learners negotiate agency and what values guide delegation strategies. To address this gap, we conducted a two-part study: an analysis of discussions in the r/languagelearning subreddit to map learners’ LLM usage patterns and factors driving delegation, followed by a technology probe study where learners designed learning activities and experimented with LLM support. Our findings reveal three key considerations influencing delegation: accuracy, independence, and authenticity. We analyze these considerations through two types of obstacles: selection challenges in choosing appropriate strategies and execution challenges in following through on intentions. These insights inform the design of AI-assisted learning systems that preserve learner agency while supporting diverse learning goals.