Cheap, Quick, and Rigorous: Artificial Intelligence and the Systematic Literature Review

Abstract

The systematic literature review (SLR) is the gold standard in providing research a firm evidence foundation to support decision-making. Researchers seeking to increase the rigour, transparency, and replicability of their SLRs are provided a range of guidelines towards these ends. Artificial Intelligence (AI) and Machine Learning Techniques (MLTs) developed with computer programming languages can provide methods to increase the speed, rigour, transparency, and repeatability of SLRs. Aimed towards researchers with coding experience, and who want to utilise AI and MLTs to synthesise and abstract data obtained through a SLR, this article sets out how computer languages can be used to facilitate unsupervised machine learning for synthesising and abstracting data sets extracted during a SLR. Utilising an already known qualitative method, Deductive Qualitative Analysis, this article illustrates the supportive role that AI and MLTs can play in the coding and categorisation of extracted SLR data, and in synthesising SLR data. Using a data set extracted during a SLR as a proof of concept, this article will include the coding used to create a well-established MLT, Topic Modelling using Latent Dirichlet allocation. This technique provides a working example of how researchers can use AI and MLTs to automate the data synthesis and abstraction stage of their SLR, and aide in increasing the speed, frugality, and rigour of research projects.