Bridging technology and confidence: Linking AI acceptance to writing self-efficacy among English instructors

Document Type : Original Article

Authors

1 Assistant Professor of Applied Linguistics, Department of English Language Teaching, Ker.C., Islamic Azad University, Kermanshah, Iran

2 Assistant Professor of Applied Linguistics, Department of English Language, CT.C., Islamic Azad University, Tehran, Iran

10.22077/ali.2025.9184.1127

Abstract

The present study examined the relationship between artificial intelligence (AI) acceptance and writing self-efficacy among 278 Iranian English university instructors. The quantitative findings indicated a statistically significant positive relationship between AI acceptance and writing self-efficacy (r = 0.42, p < 0.01). The qualitative phase examined the instructors' perceptions and their practices using four main domains from Khalifa and Albadawy's framework (2024). Based on the findings, the interviewees indicated that AI tools had alleviated their writing difficulties in (1) Content Development and structuring, with more experienced instructors utilizing advanced text structuring while novice instructors focused on grammar; (2) Idea Development, where AI warranted research design with assistance particularly in identifying gaps in the literature; and (3) Editing/Publishing support (all users appreciated being able to check for plagiarism, though the more experienced instructors did take advantage of peer revision options); (4) Literature Review and Synthesis, the data showed that instructors evaluated texts differently albeit dependent on experience level. Additionally, it appeared that emotional responses such as enthusiasm or skepticism were also mediating factors that influenced acceptance. Barriers to AI use and acceptance also emerged regarding institutional support and ethical considerations. The data suggest that self-efficacy is influenced positively through prior experience or use, and the self-efficacy gains were more pronounced for more experienced instructors. This reveals how a cycle of adopting technology establishes confidence. The findings suggest that AI benefits are conditional based on experience level, institutional and contextual barriers, emotional factors, and contextual differences providing rich practical insights for training programs.

Keywords

Main Subjects

Applied Linguistics Inquiry
Volume 3, Issue 2
September 2025
Pages 95-110

Files

History

  • Receive Date: 04 February 2025
  • Revise Date: 10 April 2025
  • Accept Date: 13 May 2025

Share

How to cite

Statistics