WriteAssist: A Personalized Generative AI System for Autonomous Authoring of Scholarly Literature Reviews

In an era of information overload, research writing, particularly literature review composition, has become increasingly burdensome due to the sheer volume of scholarly publications released each year. This paper introduces WriteAssist, a novel standalone authoring system that helps researchers efficiently generate literature review sections. Given the title and abstract of a work-in-progress manuscript, WriteAssist automatically retrieves relevant and recent peer-reviewed articles, highlighting portions that offer supporting or contrasting perspectives. A key innovation lies in its personalized recommendation engine, which tailors results based on the user’s prior publications and research profile, enabling context-aware synthesis. We position WriteAssist within the landscape of intelligent writing assistants, academic search platforms, and personalized recommender systems, and we detail its architecture – integrating natural language processing and user modeling to streamline academic writing. The system represents a significant step toward alleviating cognitive overload in scholarly composition and offers a blueprint for smarter, adaptive tools in academic research support.