Homophone errors are a common challenge in written communication, affecting both high-resource languages, such as English, and low-resource languages, such as Khmer. These errors are often difficult to detect because they require contextual understanding rather than simple spelling correction. While existing spelling correction tools enhance text accuracy, they do little to improve users’ long-term writing skills, often leading to an over-reliance on automated corrections. This study aims to bridge this gap by investigating the challenges of homophone usage, specifically among Khmer users, and proposing a foundational theoretical blueprint for future solution development. Through a questionnaire-based survey, we analyzed the prevalence of homophone errors and their impact on Khmer speakers. Additionally, we conducted an experimental study using Typing Tracker, where participants transcribed audio-recorded articles to determine their ability to correctly use homophones in context. Based on these insights, we introduce Sor-Ser, an innovative conceptual approach that integrates Natural Language Processing (NLP) with Learning Analytics (LA) techniques. This preliminary framework provides a foundation for addressing homophone errors while enhancing writing proficiency. By addressing both error correction and skill development, Sor-Ser provides a potential pathway for improving Khmer writing accuracy while fostering long-term proficiency and confidence.