AI meets academia: transforming systematic literature reviews

Purpose: This study synthesizes the role of artificial intelligence (AI) and automation in systematic literature reviews (SLRs), focusing in particular on efficiency, methodological quality and human–machine collaboration. Design/methodology/approach: A systematic review methodology was applied, analyzing studies from Scopus and Web of Science databases to explore the use of AI and automation in SLRs. A final sample of 28 articles was selected through a rigorous and interdisciplinary screening process. Findings: Our analysis leads to seven themes: human and machine collaboration; efficiency and time savings with AI; methodological quality; analytical methods used in SLRs; analytical tools used in SLRs; SLR stages AI is utilized for and living systematic reviews. These themes highlight AI’s role in enhancing SLR efficiency and quality while emphasizing the critical role of human oversight. Research limitations/implications: The rapid advancement of AI technologies presents a challenge in capturing the current state of research, suggesting the need for ongoing evaluation and theory development on human–machine collaboration. Practical implications: The findings suggest the importance of continuously updating AI applications for SLRs and advocating for living systematic reviews to ensure relevance and utility in fast-evolving fields. Social implications: Integrating AI and automation in SLRs could democratize access to up-to-date research syntheses, informing policy and practice across various disciplines and redefining the researcher’s role in the digital age. Originality/value: This review offers a unique synthesis of AI and automation contributions to SLRs, proposing a conceptual model emphasizing the synergy between human expertise and machine efficiency to improve methodological quality.