فائزه پیریایی

ackground: Over the past decade, advances in machine learning, natural language processing, and generative artificial intelligence have enabled news organizations to automate routine reporting tasks, enhance investigative capabilities, and deliver content tailored to increasingly segmented audiences. Aims: This paper presents a systematic review of scholarly literature on the integration of AI into journalism, covering studies published between 2010 and 2025. Methodology: Drawing on research from diverse geographical contexts and methodological approaches, it synthesizes findings on AI’s technological capabilities, its economic and ethical implications, and its broader societal impact on the news ecosystem. Findings: The review identifies AI’s transformative role in automating routine reporting, enhancing investigative journalism, enabling personalized content delivery, and streamlining newsroom operations. However, it also reveals significant concerns regarding transparency, accountability, bias, audience trust, and the erosion of human editorial oversight. The findings highlight regional disparities in adoption, shaped by technological infrastructure, market readiness, and policy environments, underscoring the need for context-sensitive approaches to AI governance. By mapping prevailing trends and identifying underexplored dimensions—such as cross-cultural differences in adoption, long-term effects on democratic deliberation, and evolving newsroom ethics—this study provides an evidence-based foundation for policymakers, media professionals, and researchers. Conclusion: While AI holds the potential to enhance journalism’s efficiency, reach, and innovation, its responsible implementation requires robust ethical standards, governance frameworks, and sustained human involvement to safeguard the profession’s democratic role.

This study presents an integrative review of scholarly literature published between 2016 and 2022 to synthesize how AI literacy has been conceptualized, taught, and measured. Drawing on over 40 empirical and theoretical sources, the review maps trends across educational levels—from primary to higher and adult education—and user groups, including students, teachers, professionals, and the general public. Key findings reveal significant diversity in pedagogical strategies, ranging from project-based learning and digital storytelling to flipped classrooms and professional development programs. Conceptualizations of AI literacy vary widely, encompassing technical, ethical, civic, and empowerment-oriented dimensions. Meanwhile, the field continues to face challenges related to definitional ambiguity, limited standardization of assessment tools, and inconsistent integration of ethical and socio-cultural considerations. The review emphasizes the need for convergence around shared frameworks, scalable pedagogies, and cross-contextual evaluation tools. It concludes by identifying future directions for inclusive, empirically grounded, and ethically aware AI literacy initiatives aimed at fostering reflective and informed engagement with AI systems.