How can I use AI to check if the exposition in a paper is concise?

Employing AI for assessing expositional conciseness in academic papers is feasible through specialized natural language processing (NLP) tools. These tools analyze text for redundancies, overly complex phrasing, and non-essential information. Key principles involve measuring metrics like word and sentence length, identifying filler words, passive voice overuse, and lexical redundancies. Necessary conditions include clear input text and appropriate tool calibration. Applicability spans drafts needing editing refinement, but AI must be context-aware; it cannot fully grasp argumentative flow or disciplinary norms. Precautions are essential: AI results are indicative, not definitive, and require critical review by the author to preserve intended meaning and nuance while eliminating true verbosity. To implement this, utilize AI-powered writing assistants (e.g., Grammarly Premium, Hemingway Editor, or specific LLM prompts). Steps include pasting text sections into the tool, setting the goal for conciseness, and reviewing the tool's highlighted suggestions for complex sentences, adverbs, redundancy, and passive constructions. Critically evaluate each suggestion within your argument's context, accepting only those enhancing clarity without sacrificing accuracy or depth. This process enhances readability and ensures efficient communication of research findings.