Work

A Dialogue System for Second Language Learning with Dynamic Adaptation to In-Dialogue Difficulty Changes

Year: 2026

Type: conference-paper

Abstract: 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.

Source: Annual Meeting of the Special Interest Group on Discourse and Dialogue

DOI: 10.18653/v1/2026.sigdial-1.13

Authors: Taku Morioka, Junya Takayama, Tomoyuki Kajiwara

Institutions: Ehime University, The University of Osaka

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: bronze

Funder: Japan Society for the Promotion of Science

OpenAlex ID: W7214049319