 |
[email protected] |
 |
3275638434 |
 |
 |
| Paper Publishing WeChat |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
YAO Shuchao
Full-Text PDF
XML 34 Views
DOI:10.17265/2159-5836/2026.10.005
Guangdong Medical University, Dongguan, China Guangdong University of Foreign Studies, Guangzhou, China
Every syntax class conveys values to students through what it rewards and what it tolerates, whether or not values education is deliberately designed into it, and generative artificial intelligence (AI) has made it easier than ever to “look right”. The key question for values education is therefore not whether it is present, but whether it is effective. This paper proposes the concept of evidential visibility. For values to be genuinely learned, two conditions must be met. First, the value must follow the same logic as a syntactic rule. Second, it must leave traces in students’ work and be checked in assessment. On this basis, the paper outlines the features of effective hidden-curriculum education and the obstacles to it, and illustrates classroom practice with three examples: descriptive grammar, the Coordinate Structure Constraint, and competing analyses of ellipsis.
hidden curriculum, generative AI, values education, syntax pedagogy, evidential visibility
Journal of Literature and Art Studies, October 2026, Vol. 16, No. 10, 828-834
Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment & Evaluation in Higher Education, 49(6), 893-905.
Carnie, A. (2023). Syntax: A generative introduction (4th ed.). Beijing: Foreign Language Teaching and Research Press.
Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: Why structural assessment changes are needed for a time of GenAI. Assessment & Evaluation in Higher Education, 50(7), 1087-1097.
Jackson, P. W. (1968). Life in classrooms. New York: Teachers College Press.
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6), 49-66.
Wang, C. M. (2026). Optimal interaction The learning mechanism of the“Xu+Al" Model. Foreign Language World, (4), 2-7.
Wang, G. H., Xing, X. R., Tian, L. H., & Zhang, F. (2025). Research on the formation pathways of academic misconduct among college students in the intelligent era. e-Education Research, 46(12), 26-33.
Wang, J. M., & Wang, J. J. (2022). Ideological and political education in foreign language courses: “Implicit” hypothesis and “explicit” logic. Journal of Xi’an International Studies University, 30(2), 44-49.
Wen, X. (2021). How to implement ideological and political instruction in linguistics courses. Foreign Languages in China, 18(2), 71-77.
Wu, Y., & Ji, J. J. (2026). Research on Influencing Factors of GAI Dependence in Undergraduate ESL Writing. Foreign Languages Research, (3), 78-83.
Yeung, L. K. C., Chow, D. P. L., Wong, P. H., & Lau, S. S. S. (2026). In ChatGPT they trust: A study of students’ perceptions and misuse of ChatGPT in higher education. AI and Ethics, 6(11), 10-20.
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28.
Zou, M. M., Reinders, H., & Wang, W. (2026). Can incorporating GenAI chatbots into L2 writing instruction promote student agency and writing performance? Insights from a mixed-methods quasi-experimental study. Computer Assisted Language Learning, 1-44.