Reflective writing is considered an important metacognitive skill, especially in vocational education where students must bridge theoretical knowledge and practical experiences. However, meaningful reflection often requires supervision and guidance, as students struggle to make connections between classroom concepts and workplace experiences. While generative large language models (LLMs) have shown promise in such educational applications including personalized learning and writing support, their effectiveness is hindered by inherent issues such as hallucination and lack of personalization. To address this, retrieval-augmented generation (RAG) has emerged as a solution to enable models to integrate external information in text generation. While RAG has demonstrated success in various domains, its potential for enhancing reflective writing remains untapped. In this work-in-progress, we introduce Memoire, a writing assistant designed to utilize the capabilities of RAG to support students in reflective writing. By leveraging external knowledge and memory from prior reflections of each student, Memoire helps them write reflections that are both insightful and grounded in accurate prior information. We also conduct a pre-study to evaluate and compare three modalities of providing writing support in the domain of reflective writing from prior works. Finally, we introduce our study design plan for an in-classroom evaluation of Memoire. © 2025 Copyright for this paper by its authors.
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