Benchmarking Generative AI: A Comparative Evaluation and Practical Guidelines for Responsible Integration into Academic Research

Abstract

Generative artificial intelligence (AI) systems show immense potential to transform scholarship, but their capabilities and responsible use require rigorous scrutiny. This pioneering study provides an empirical benchmarking of four leading generative models. Through standardized assessments, the systems are evaluated on their ability to assist ten key aspects of academic research spanning literature reviews to hypothesis generation. Quantitative scoring of completeness, accuracy, and relevance combined with thematic analysis of the AI systems' perspectives reveal nuances between their strengths, risks, and needs for validation. Key findings show promising competencies in focused tasks like summarization but major limitations in contextual adaptation, reasoning, and bias mitigation. While narrow augmentation appears feasible presently, fully automating scholarly work remains challenging. The study yields critical insights including pragmatic adoption strategies, governance priorities, and ethical considerations to steer these technologies towards responsible integration. It also articulates future research directions such as enhancing transparency and reasoning. Overall, this work constitutes a crucial roadmap for realizing generative AI's immense potential in scholarship while proactively addressing risks and limitations. With prudent oversight and wisdom, these rapidly evolving tools could positively transform academic discovery for the betterment of society.

Keywords: Generative AI, Academic Research, Benchmarking, Responsible AI, Scholarly Workflows