Enhancing literature reviews through AI integration: A case study on cognitive efficiency.

Abstract

The term "cognitive efficiency" (CE) lacks a unified definition and consistent measurement across diverse academic disciplines, hindering interdisciplinary research. Concurrently, while artificial intelligence (AI) tools are rapidly evolving, systematic methodologies for their application in literature reviews remain nascent. This paper addresses these two critical gaps. First, through an AI-assisted systematic review of 96 scholarly articles, we propose a consolidated definition of CE as "a measure of an individual's memory recall and ability to process information within a given reaction time," providing much-needed clarity. Second, we present a novel, iterative methodology for conducting systematic reviews that strategically integrates the strengths of currently accessible AI tools with essential human judgment and expertise. Our findings highlight AI's proficiency in individual article comprehension and theme identification, while also demonstrating its current limitations in complex data synthesis and inter-paper comparison. This research offers both a clearer conceptualization of cognitive efficiency and a robust, reproducible framework for leveraging AI to enhance the efficiency and rigor of future systematic literature reviews.