Exploring Chinese EFL Learners's Acceptance of Generative Artificial Intelligence Tools in English Academic Writing: The Integrated Model of Technology Acceptance

Abstract

The integration of Generative Artificial Intelligence (GenAI) into English academic writing has attracted increasing attention, yet empirical evidence on learners’ acceptance of GenAI in undergraduate thesis writing remains limited. This study investigates Chinese EFL learners’ acceptance of GenAI tools in undergraduate thesis writing and identifies the factors associated with their adoption using the Integrated Model of Technology Acceptance (IMTA). Data were collected from 287 senior English majors across ten universities in Chongqing through questionnaires (N = 287) and semi-structured interviews (N = 14). Quantitative data were analyzed using descriptive statistics, reliability and validity analyses, and structural equation modeling, while qualitative data were examined through thematic analysis. The results showed that perceived enjoyment (PE) played a central role in the model: PE was significantly associated with behavioral intention (BI) (β = .613, p = .003), perceived usefulness (PU) (β = .723, p < .001), and perceived ease of use (PEOU) (β = .767, p < .001). In contrast, PU was not significantly associated with BI (β = .319, p = .155), and PEOU also showed no significant direct effect on BI (β = −.104, p = .377). PEOU was significantly associated with PU (β = .224, p = .046). Qualitative findings identified six aspects of learners’ perceptions: information retrieval, knowledge construction, idea inspiration, language polishing, ease of use, and emotional support. This study provides insights into technology acceptance in English academic writing in the era of GenAI, and offers implications for future research and pedagogical practice.