The rapid acceleration of artificial intelligence (AI) and, more recently, generative AI is reshaping journalism in ways that extend far beyond earlier forms of news automation. As generative AI tools become widely accessible and capable of processing unstructured data, longstanding definitions of automated journalism—once centered on structured datasets and template-based text generation—are being fundamentally reconfigured. This paper presents the most comprehensive and up-to-date systematized review of automated journalism scholarship, expanding on earlier research by synthesizing 185 peer-reviewed, English studies published between 2012 and 2024 about machine-generated textual news content published online. Through a rigorously designed search strategy across four major social science databases, this review maps how the field’s conceptual, methodological, and geographical contours have transformed as AI and generative AI become increasingly ubiquitous. The findings show a surge of research in 2024 alone, as well as the emergence of more than 150 overlapping terms used to describe AI- and algorithmically generated news, illustrating significant conceptual fragmentation. Despite no overly dominant theories, concepts or frameworks, key themes include credibility and trust, human–machine collaboration, newsroom adoption and institutional logics, transparency and disclosure, and the ethical and regulatory challenges introduced by increasingly sophisticated AI systems. By consolidating patterns, evaluating an expanded selection of key terms, and assessing theoretical and conceptual frameworks, this review demonstrates how AI and especially generative AI reflect the speed of industrial change, but also the lack of shared academic frameworks to make sense of that change.
Bartleman et al. (Sat,) studied this question.