Generative AI and Media Convergence in Education: Transforming Student Communication on Social Media

Abstract

Generative Artificial Intelligence (AI) and media convergence are prompting a rethinking of how students communicate, allowing for visually active creation and interpretation in educational and social media contexts. Although there has been descriptive analysis of media literacy training, there is a limited empirical literature on the training. This research examines the impact of media literacy training on enhancing students’ ability to verify AI-generated images in educational and digital communication contexts. A quasi-experimental design was adopted with a purposive sample of 430 undergraduate students representing both genders. The training program was designed to strengthen four key media literacy competencies: access, analysis, collective reasoning, and evaluation. The experiment was implemented using SPSS to assess the program’s effectiveness. Pre-test and post-test scores were analyzed using paired sample t-test, correlation analysis, and regression analysis. The analysis revealed statistically significant differences (p < 0.05) in mean scores for access and analysis skills, with higher performance in the post-test phase. The results indicated statistically significant differences in mean scores for access and analysis skills, with higher performance in the post-test phase. Analysis (β = 0.41) was the strongest predictor, followed by Communication (β = 0.39) and Evaluation. The calculated t-values exceeded the tabulated values, confirming the success of the intervention. The findings indicate that media literacy within generative AI and media convergence enhances students’ critical thinking, verification, and responsible engagement skills. This research highlights the transformative role of generative AI in fostering informed, ethical, and creative digital communication in education.