How to use AI to check whether the structure in a paper is reasonable?

AI tools, particularly generative and analysis-focused models, can effectively evaluate the structural coherence of academic papers. These tools analyze text organization against established academic conventions and logical flow principles to identify potential weaknesses. Key principles involve natural language processing (NLP) techniques like topic modeling to track thematic progression, coherence metrics assessing sentence and paragraph transitions, and discourse parsing to map rhetorical moves. Necessary conditions include inputting clean, structured text sections (e.g., clearly separated Abstract, Introduction, Methods). Accuracy is contingent on the AI model's training data quality and specificity for academic genres. While highly efficient for flagging abrupt shifts, missing sections, or imbalance, AI analysis should supplement human expert review, as models may miss nuanced argumentation or field-specific structural norms. Users should verify AI suggestions critically. Implementation involves several steps: preparing the paper text with section headings; selecting a suitable AI tool or platform specializing in academic writing analysis; inputting the text and requesting a structure/coherence check; reviewing the tool's output highlighting illogical transitions, disproportionate section lengths, or deviations from a conventional IMRAD/CARS model; and iteratively revising based on these insights. This application significantly accelerates structural refinement, enhancing readability and argumentative flow before submission or peer review.