How to check the hypothetical reasoning in a paper through AI?

AI can verify hypothetical reasoning in academic papers through automated logic assessment, checking structural soundness and coherence. This application leverages natural language processing (NLP) capabilities to evaluate argument chains. Key principles involve identifying premise-conclusion relationships and assessing reasoning validity using formal logic patterns and semantic analysis. Techniques include argument mining to parse text components and logical coherence models to flag contradictions or unsupported leaps. Necessary conditions encompass clear text structure and unambiguous statements within the paper. Crucially, AI's capability remains constrained by the quality of input text, complex non-deductive reasoning forms, and deeply contextual knowledge, requiring human oversight for nuanced interpretation. Implementing AI-assisted checking involves several steps: First, preprocess the paper text and define the hypothetical scenario and its proposed implications. Next, apply NLP argument mining to extract premises, conclusions, and causal links. Third, utilize inference validation models (e.g., entailment recognition, consistency checkers) to test logical flow and identify fallacies like non-sequiturs. Finally, generate a report highlighting potential weaknesses or inconsistencies for author review. This enhances rigor while saving time during the review process.