FOCUS: An AI-assisted reading workflow for information overload

Information overload has become a defining challenge for researchers navigating an exponentially expanding scientific literature alongside podcasts, preprints, and social media discussions. While concerns about AI deskilling and superficial engagement are legitimate, strategic implementation of AI tools can amplify rather than replace scholarly judgment. This paper introduces the FOCUS method—Find, Organize, Condense, Understand, and Synthesize—a framework for integrating AI into research workflows while preserving intellectual rigor. The approach uses AI-powered search to cast wider nets across formal and informal knowledge sources, converts ephemeral audio content into searchable text, employs structured prompts to extract insights from dense papers, leverages dialogue for deeper comprehension, and maintains human synthesis of disparate findings. Privacy concerns can be addressed through local models or platforms that don't train on user data. When implemented thoughtfully, these tools enable researchers to read more widely and think more deeply without sacrificing the critical thinking that defines good science. As research volume continues growing beyond individual capacity, the FOCUS method offers a path to expand intellectual reach while maintaining scholarly depth.