Automatic dubbing (AD) aims to replace the original speech in a video with translated speech that maintains precise temporal alignment (isochrony). Achieving natural synchronization between dubbed speech and visual content remains challenging due to variations in speech durations across languages. To address this, we propose an end-to-end AD framework that leverages large language models (LLMs) to integrate translation and timing control seamlessly. At the core of our framework lies Duration-based Translation (DT), a method that dynamically predicts the optimal phoneme count based on source speech duration and iteratively adjusts the translation length accordingly. Our experiments on English, Spanish, and Korean language pairs demonstrate that our approach substantially improves speech overlap—achieving up to 24% relative gains compared to translations without explicit length constraints—while maintaining competitive translation quality measured by COMET scores. Furthermore, our framework does not require language-specific tuning, ensuring practicality for multilingual dubbing scenarios.

Recent advancements in educational platforms have emphasized the importance of personalized education. Accurately estimating question difficulty based on the ability of the student group is essential for personalized question recommendations. Several studies have focused on predicting question difficulty using student question-solving records or textual information about the questions. However, these approaches require a large amount of student question-solving records and fail to account for the subjective difficulties perceived by different student groups. To address these limitations, we propose the LLaSA framework that utilizes large language models to represent students at various levels. Our proposed method, LLaSA and the zero-shot LLaSA, can estimate question difficulty both with and without students’ question-solving records. In evaluations on the DBE-KT22 and ASSISTMents 2005–2006 benchmarks, the zero-shot LLaSA demonstrated a performance comparable to those of strong baseline models even without any training. When evaluated using the classification method, LLaSA outperformed the baseline models, achieving state-of-the-art performance. In addition, the zero-shot LLaSA showed a high correlation with the regressed IRT curve when compared to question difficulty derived from students’ question-solving records, highlighting its potential for real-world applications.