We propose a dialogue system for second language learning that dynamically adjusts the difficulty of its utterances by adapting to changes in the learner’s utterance difficulty across dialogue topics. Recent studies have advanced second language learning dialogue systems based on large language models (LLMs) grounded in the Input Hypothesis. However, although learners’ language proficiency varies based on their familiarity with a given topic, conventional fixed-difficulty dialogue systems do not adequately account for this variability. In this study, we propose a dialogue system for second language learning that minimizes the difficulty gap between the system and a user model. The user model dynamically varies utterance difficulty based on the topic during dialogue. Our LLM-based dialogue system controls utterance difficulty by prompt engineering and Direct Preference Optimization (DPO). Experimental results in both Japanese and English demonstrate that the proposed method is effective in tracking utterance difficulty for second language learners.