How to use AI to ensure the feasibility of the paper content?

Leveraging artificial intelligence can significantly enhance the feasibility assessment of paper content through systematic validation of proposed ideas against existing data and computational models. AI tools can rapidly simulate outcomes, evaluate resource requirements, and identify potential methodological or theoretical conflicts before committing extensive research effort. Key principles include utilizing domain-specific AI models trained on high-quality, relevant literature and datasets. Essential conditions are access to sufficient, reliable data and clearly defined research parameters. Applicability spans hypothesis testing, experimental design optimization, and resource allocation simulation. Caution is necessary: AI outputs require expert scrutiny for contextual relevance and potential bias, and feasibility remains bounded by model training scope and current knowledge limitations. Reliance solely on AI without scholarly judgment is inadvisable. Implementation involves several steps. First, deploy AI literature mapping tools to identify foundational works and gaps. Next, use predictive modeling or simulation software to test core hypotheses against synthetic or real-world data. Finally, employ AI-powered project management assistants to forecast timelines, costs, and resource needs. This process accelerates validation, reduces wasted effort on impractical directions, and highlights viable paths, significantly improving research efficiency and success potential. Typical scenarios include complex computational studies, large-scale data analysis proposals, or interdisciplinary research requiring synthesis across fields.