The field of education and language processing is moving towards increased personalization and multilingualism. Researchers are exploring the use of large language models to personalize educational content and improve student learning outcomes. Additionally, there is a growing interest in developing machine translation systems that can effectively translate educational materials into multiple languages, including low-resource languages. This has the potential to increase access to education for non-native speakers and improve overall educational outcomes. Noteworthy papers include: Learning in Context: Personalizing Educational Content with Large Language Models to Enhance Student Learning, which introduces a novel framework for personalizing educational materials using large language models. Translate, then Detect: Leveraging Machine Translation for Cross-Lingual Toxicity Classification, which demonstrates the effectiveness of translation-based pipelines for cross-lingual toxicity classification.