AI-DRIVEN MEDIA CONSUMPTION AND THE TRANSFORMATION OF PUBLIC DISCOURSE - Kanojia , Sagar Kanojia; Pandey, Yogendra Kumar; Gautam , Rashmi; Dabral, Jitendra; Kanaujia, Swati; Gupta, Swati (ShodhKosh: Journal of Visual and Performing Arts, 2026)
Artificial intelligence now plays a major role in how we interact with the news and talk about politics. Despite this, we still don't have a clear picture of how algorithms actually change which issues get noticed, how they are presented, or how we see the state of public conversation. This study examines the connection between our dependency on AI-driven media and the tendency to focus exclusively on material we already agree with, which can give the impression that public discourse is collapsing. This study develops and evaluates a theory-driven model that connects selective exposure and subsequent perceptions of discourse fragmentation to AI-driven media dependence. The fundamentals of this study are based on well-established agenda-setting, framing, and cultivation theory. The study uses a survey-experimental design with 200 participants and a 2 × 2 embedded manipulations contrasting AI-personalized versus non-personalized feeds and conflict versus solution framing. The expected measures include AI-driven media reliance, selective exposure propensity, congruent headline choice, issue salience, perceived discourse fragmentation, civility, trust in news, and algorithmic awareness. Statistical analyses include Cronbach’s alpha, Pearson correlations, chi-square tests, ordinary least squares regression with robust standard errors, and constructed communication. Results show that AI-driven media reliance significantly predicts selective exposure propensity (β = .32, p < .001). Exposure to AI-personalized feeds increases issue salience (B = 1.11, p < .001) and the likelihood of high congruent headline selection, χ²(1, N = 200) = 31.25, p < .001, Cramér’s V = .23. Conflict framing significantly increases perceived discourse fragmentation (β = .29, p < .001) and reduces perceived civility (β = −.21, p < .001). Mediation analysis indicates that selective exposure partially affects the relationship between AI feed exposure and discourse fragmentation (indirect effect = .11, 95% CI [.07, .16]). The findings suggest that AI systems do not merely transmit information efficiently, but they also participate in structuring the public sphere by shaping what users attend to, how they interpret social conflict, and how fragmented they believe civic life has become. The study contributes a testable model of algorithmic public discourse and offers implications for platform governance, media literacy, and the future of public opinion research.