Women Journalists in Surabaya: Role Interpretation in AI-Driven Media Transformation - Tripalupi, Merry Fridha; Dewi, AAI. Prihandari Satvika; Irawan, Rahmat Edi; Sjuchro, Dian Wardiana; Tachopisalwong, Attanan (Jurnal Kajian Jurnalisme, 2026)
Background: The increasing adoption of artificial intelligence (AI) has transformed newsroom practices by accelerating news production and reshaping editorial decision-making. While previous studies have primarily examined AI from technological and organizational perspectives, limited attention has been given to how women journalists experience and negotiate these changes, particularly in local media contexts such as Surabaya. Purpose: This study examines how women journalists in Surabaya interpret and negotiate their professional roles amid AI-driven media transformation through the lens of Media Feminism Theory. Methods: This study employed a qualitative critical ethnographic approach. Data was collected through in-depth interviews, participant observation, and documentation. The data were analyzed using thematic analysis through open, axial, and selective coding. Results: The findings reveal four major themes: AI has transformed newsroom routines, supported journalistic work efficiency, influenced the visibility of women-related issues through algorithmic logic, and created new professional pressures for women journalists. Although AI improves productivity, participants emphasized that editorial judgment, ethical verification, and professional autonomy remain essential. The findings further indicate that algorithm-driven editorial practices may reinforce existing gender inequalities by reducing the visibility of women-centered issues and intensifying workload expectations. Conclusion: A media feminist perspective remains relevant for analysing the dynamics of the current digital media industry, particularly as algorithms begin to play a role in determining which issues are deemed important and worthy of public attention. Implications: This study extends Media Feminism Theory by demonstrating how AI-mediated newsroom transformation intersects existing gender structures, providing insights for developing more gender-sensitive AI.