A Study on the AIGC-enabled Teaching Paths and Practices in Film Scriptwriting and Audio-Visual Language across Universities

Abstract

This study systematically explores the application logic, practical paths, and reflective strategies of Generative Artificial Intelligence (AIGC) technology in the teaching of drama, film, and television literature, as well as audio-visual language in higher education institutions. Addressing practical challenges in traditional pedagogy-such as the detachment between script texts and visual language, high pre-visualization costs, and lengthy feedback cycles-this paper proposes an integrated pedagogical workflow: "LLM-assisted script incubation - Prompt engineering-driven audio-visual language reconstruction - Dynamic pre-visualization presentation - Processual and portfolio-based evaluation." Through in-depth observation and evaluation of teaching practices, this research analyzes the efficacy of AIGC in enhancing students' standardized audio-visual expression and accelerating creative iteration. Furthermore, it offers coping strategies for potential risks, such as algorithmic homogenization and the weakening of humanistic spirit, aiming to provide a practical reference for the digital transformation and pedagogical reform of film and television education in universities.