Deconstructing the Hype: A Critical Literature Review on AI in Journalism

Abstract

This article presents a systematic review of scholarly literature on artificial intelligence (AI) in Journalism published between 2020 and 2024. The objective of the study is to critically examine academic production from this period. Specifically, it aims to identify prevailing research trends, dominant analytical approaches, and persistent gaps in the field. This review responds to the need to systematize a prolific yet fragmented body of work. Such fragmentation reflects a lack of theoretical integration that hinders a consolidated understanding of the phenomenon. The review follows PRISMA guidelines to conduct a structured search across major academic databases (Web of Science and Scopus), applying well-defined inclusion and exclusion criteria. The analysis reveals a predominance of descriptive and exploratory studies. In contrast, empirical and longitudinal investigations remain limited. Notable gaps are also identified regarding the impact of AI on editorial transparency and audience trust. By providing a PRISMA-based mapping of the field and identifying key blind spots such as editorial transparency, audience trust, and the lack of longitudinal evidence-this review contributes to a more critical and systematic understanding of the impact of artificial intelligence on Journalism. The findings of this systematic review contribute to clarifying the current state of knowledge on the application of artificial intelligence in Journalism and offer guidance for future research agendas and critical reflection within both academic and professional communities. The findings contribute to clarifying the current state of knowledge on AI in Journalism and offer guidance for future research and critical reflection within academic and professional communities. These findings also provide practical guidance for policymakers and newsrooms facing ethical, organizational, and technological challenges.

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