Artificial Intelligence in Journalism and Media Practice: A Systematic Review of Applications, Limitations, and Research Approaches

Abstract

This systematic review investigates the application of artificial intelligence (AI) and machine learning (ML) in journalism and media practice from 2020 to 2026. Following PRISMA guidelines, we analysed 121 peer-reviewed articles from Scopus and Web of Science using a multi-method approach that combined qualitative thematic analysis, structural topic modelling (STM), and bibliometric network analysis. Four primary research domains emerged: news production and automation (38.0%), audience perception and content analysis (24.8%), ethical and legal considerations (19.8%), and meta-research and implementation studies (17.4%). Publication output accelerated sharply from 2023 onward, driven by the emergence of large language models and generative AI. The STM analysis confirmed the four-domain structure and revealed that legal-regulatory vocabulary pervades the literature across all categories, indicating a field-wide preoccupation with the institutional implications of AI. Key findings demonstrate that successful AI adoption depends on workflow redesign and human–machine collaboration rather than full automation; that audience evaluations of AI-generated content vary significantly across cultural contexts, with the machine heuristic—originating from Sundar’s MAIN model—playing a central mediating role; and that copyright frameworks for AI-generated news remain contested across jurisdictions. This study develops an integrated theoretical framework that maps directional relationships among the four research domains, identifies cultural context and disclosure practices as key moderators, and generates testable propositions for future investigation.