Reconstruction of Writing instruction with Generative AI in SAMR Framework: A Multimodal Empirical Study
Year: 2025
Type: conference-paper
Study design: Clinical Trial
Abstract: As a case of education digital transformation, traditional English writing pedagogy is difficult to truly encourage the interest of the students on writing and develop writing ability. A joint of generative AI and SAMR model offers innovation potential to writing teaching, but fewer studies exist on its implementation in vocational English writing teaching. This study enlisted students from two comparable classes of writing courses in a vocational school. The control group was instructed in generative AI supported writing instruction based on the SAMR model, while the control group was instructed in traditional writing instruction. Writing tests were given before and after teaching, and score data were collected and analyzed in terms of several aspects. The test results showed that the experimental group students' writing scores improved significantly with a dense distribution of scores and the number of students scoring high. The improvement in the control group was smaller, with a diffuse distribution and wide fluctuation. Independent sample t-tests also confirmed the effectiveness of the new teaching method. The study provides strong evidence for the reform of English writing education in vocational colleges.
Author: Le Chang
Institution: Beijing Polytechnic
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4413987069