Revolutionizing Literature Search: AI vs. Traditional Methods in Digital Divide Literature Screening and Reviewing

The emergence of Artificial Intelligence (AI), particularly Generative AI, is poised to bring significant changes to various aspects of the human world. While there is ongoing debate about the use of AI for writing manuscripts, completing assignments, and other working tasks, this research takes a step back to address a fundamental question: How accurate and efficient is AI in helping to conduct research? This research focuses on the initial step of research - literature search and screening. As the volume of academic literature grows exponentially, efficient and effective methods for synthesizing existing knowledge become crucial for scientific discovery. This research aims to explore the potential of AI-driven solutions in the literature screening process and compare them with traditional labor- and time-intensive approaches. Adopting a comparative analysis approach, this research examines both traditional manual screening techniques and AI-powered approaches and evaluates AI’ s performances. Data were collected from a comprehensive review of existing literature on the multidisciplinary topic of the digital divide. The analysis focuses on the accuracy, efficiency, and usefulness of AI in processing large volumes of literature. Results suggested that AI-driven approaches can reduce substantial manual work and time investment while maintaining certain levels of accuracy in identifying relevant studies. However, the challenge of algorithmic bias remains, and this calls for human intervention. Instead of AI replacing humans, our findings suggest a hybrid approach - combining AI capabilities with human expertise. Such an approach offers the most promising solutions for future literature review and screening processes, evidencing the synergy between AI and humans.