The emergence of AI-generated visual media has reconfigured the production– distribution relationship on Chinese short-video platforms. On the production side, AI reduces costs, diminishes the need for large teams, and lowers professional barriers through models, workflows, and computational power, enabling individuals to enter the field of content creation. Yet on the distribution side, such openness has not translated into formal innovation. Instead, through the combined effects of platform algorithms, short-video perceptual regimes, and gamified reward mechanisms, AI-generated content has increasingly converged toward a lightweight, highly stimulating, and easily replicable form. This convergence does not lead to an expansion of visual expressive freedom; rather, it facilitates the comprehensive migration of short-drama logic into AI-generated visual media. “Pleasure points,” hooks, upgrades, feedback, and conversion have become new organizing principles, while the narrative depth, authorial subjectivity, and aesthetic autonomy of traditional visual media continue to give way to platform calculability. At the same time, this convergence is also embedded in the technological logic of AI itself. Rather than generating images directly from lived experience, AI reconstructs and recombines existing data relations through probabilistic processes and formal collage, thereby naturally favoring high-frequency, stable, and readily deployable template-based syntax. AI-generated visual media, therefore, is no longer merely a new genre emerging under new technological conditions, but also a concentrated manifestation of the convergence toward visual templateization in the platform era.

This study presents a career-integrated English education model incorporating generative AI through two tracks: (1) content development, where English education majors in four career-oriented groups (teaching vs. non-teaching; decided vs. undecided) created TOEIC-format listening materials aligned with their interests; and (2) classroom application, where the AI-generated content was implemented in first-year general classes with 103 students. Career maturity was measured by a 30-item scale, and content effectiveness by ratings of difficulty, appropriateness, usefulness, and effectiveness. Results showed that content-related factors, not learner background, best predicted overall satisfaction. Although no statistically significant differences were found, education majors tended to rate the materials as slightly more useful and slightly less difficult. Pre–post tests showed improved decisiveness, suggesting that AI-integrated, career-oriented instruction enhanced students’ confidence in career decision-making and demonstrated the potential of generative AI in fostering professional competence and career readiness.

Artificial intelligence generated content (AIGC) refers to content produced by artificial intelligence that represents the perspectives of its users, and a new technique of content Generation. Continuous development in deep learning and algorithms have facilitated the adoption of AIGC. This research summarizes literature published under the topic of AIGC using bibliometric analysis method, aims to provide insightful research directions for future studies. 342 documents were collected from Database of Web of science, network visualization analysis among authors and citation analysis over publications are presented to scholars who wish to further research into this area.