Proceedings of the 6th International Conference on Internet, Education and Information Technology (IEIT 2026)

6th International Conference on Internet, Education and Information Technology (IEIT 2026)

📍Wuxi, China🗓️ 8-10 May 2026

Why Some Vocational College Students Resist AI-Assisted English Learning: Evidence from 80 Non-Users in a Survey of 437 Distributed Questionnaires

Authors
Yuanyuan Huang1, Jing Gu1, *, Hui Shi1, *
1Shanghai Urban Construction Vocational College, Shanghai, China
*Corresponding author. Email: gujing@succ.edu.cn
*Corresponding author. Email: shihui@succ.edu.cn
Corresponding Authors
Jing Gu, Hui Shi
Available Online 17 July 2026.
DOI
10.2991/978-94-6239-733-0_12How to use a DOI?
Keywords
AI-assisted English learning; non-users; technology acceptance model; learner resistance; higher vocational education
Abstract

Although artificial intelligence tools are increasingly visible in language learning, a non-trivial group of students still refuse to use them. This study investigates why some learners remain resistant to AI-assisted English learning and under what conditions their stance may change. A questionnaire collected 437 valid responses, of which 80 respondents explicitly reported that they did not use AI for English learning. Drawing on the Technology Acceptance Model as the interpretive framework, the study combines descriptive statistics with item-level ranking analysis. The results show that resistance is shaped less by mere technical complexity than by a cluster of trust, habit, and autonomy concerns. The strongest barriers were uncertainty about reliable tools and preference for traditional learning (both agreement rate = 47.5%), followed by fear of AI dependency (43.8%); however, pairwise tests confirm that these top barriers are statistically indistinguishable and function as an inseparable bundle rather than a ranked hierarchy. When respondents were asked to identify the single most important reason, fear of AI dependency ranked first (35.0%), well above perceived inaccuracy of AI information (17.5%). Qualitative elaboration further reveals that this dependency concern operates on cognitive, metacognitive, and identity levels. At the same time, resistance was not absolute: students reported that a simple and free tool, teacher recommendation, and support during learning bottlenecks could significantly increase their willingness to try AI in the future.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 6th International Conference on Internet, Education and Information Technology (IEIT 2026)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
17 July 2026
ISBN
978-94-6239-733-0
ISSN
2667-128X
DOI
10.2991/978-94-6239-733-0_12How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Yuanyuan Huang
AU  - Jing Gu
AU  - Hui Shi
PY  - 2026
DA  - 2026/07/17
TI  - Why Some Vocational College Students Resist AI-Assisted English Learning: Evidence from 80 Non-Users in a Survey of 437 Distributed Questionnaires
BT  - Proceedings of the 6th International Conference on Internet, Education and Information Technology (IEIT 2026)
PB  - Atlantis Press
SP  - 88
EP  - 101
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6239-733-0_12
DO  - 10.2991/978-94-6239-733-0_12
ID  - Huang2026
ER  -