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Academic writing poses significant challenges for students, particularly non-native English speakers. While existing tools, such as Grammarly, provide surface-level corrections, they often lack detailed explanations, long-term skill development and personalized support. In this paper, we introduce WILLM, a system for Academic Writing Improvement based on Large Language Models (LLMs). WILLM provides context-aware feedback on grammar, vocabulary, coherence, and organization while integrating active recall quizzes and personalized reviews to enhance long-term writing proficiency. A three-week user study with 19 non-native English-speaking participants demonstrated improvements in grammar and vocabulary scores and high usability ratings (SUS score: 84.21). Results suggest that further refinement of long-term improvement features is essential for enhancing academic writing development.

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WILLM: A System for Academic Writing Improvement Based on Large Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • Yongli Mou,
  • Fatemeh Fathi,
  • Ben Thillen,
  • Stefan Decker

摘要

Academic writing poses significant challenges for students, particularly non-native English speakers. While existing tools, such as Grammarly, provide surface-level corrections, they often lack detailed explanations, long-term skill development and personalized support. In this paper, we introduce WILLM, a system for Academic Writing Improvement based on Large Language Models (LLMs). WILLM provides context-aware feedback on grammar, vocabulary, coherence, and organization while integrating active recall quizzes and personalized reviews to enhance long-term writing proficiency. A three-week user study with 19 non-native English-speaking participants demonstrated improvements in grammar and vocabulary scores and high usability ratings (SUS score: 84.21). Results suggest that further refinement of long-term improvement features is essential for enhancing academic writing development.