Can AI help me accurately assess the influence of academic papers?

AI can assist in assessing academic paper influence but cannot provide perfectly accurate standalone judgments. Its capabilities require careful interpretation and human oversight to yield meaningful evaluations. Several principles govern AI's application in influence assessment. Machine learning algorithms primarily analyze citation patterns, altmetrics, and textual content to identify impact indicators. Effective use necessitates pairing AI tools with domain expertise to interpret context-specific significance and address disciplinary citation norm variations. Key constraints include reliance on comprehensive, high-quality data inputs; limitations in capturing nascent impact or influence beyond formal publications; and vulnerability to manipulative practices like citation stacking. Notably, AI excels at handling data volume but struggles with assessing subtle scholarly contributions or novelty. For practical application, AI tools like scite.ai or ResearchRabbit accelerate influence evaluation by processing citation networks and identifying key papers. Implementation involves selecting appropriate AI tools based on specific assessment goals, integrating them with bibliometric databases, and systematically interpreting outputs within disciplinary frameworks. This hybrid AI-human approach enhances efficiency in literature reviews and tenure evaluations, though vigilance against biases and acknowledging AI's inherent limitations in assessing conceptual influence remain essential.