Generative AI and Students’ Understanding of Language Variation in a Sociolinguistics Course: Evidence from UIN Raden Intan Lampung
DOI:
https://doi.org/10.63324/aij.2.1.2026.144Keywords:
Generative AI, language variation, Sociolinguistics, student learning, undergraduate studentsAbstract
Generative AI (GenAI) has increasingly been incorporated into higher education and language learning because of its potential to provide immediate explanations, examples, and interactive support. In Sociolinguistics, however, limited research has examined how Generative AI supports students’ understanding of language variation concepts, particularly in Indonesian higher education contexts. To address this gap, this study investigated how students used Generative AI in a Sociolinguistics course and the extent to which it enhanced their understanding of language variation concepts at UIN Raden Intan Lampung. This study employed an explanatory sequential mixed-methods design. The participants were 32 undergraduate students enrolled in a Sociolinguistics course. The instruments consisted of a language variation concept test, a GenAI use questionnaire, weekly learning logs, and semi-structured interviews. Data were collected over six weeks through pretest and posttest administration, questionnaire distribution, learning-log documentation, and follow-up interviews with selected students. Quantitative data were analyzed using descriptive statistics and a paired-samples t-test, while qualitative data were analyzed thematically. The findings showed that students’ posttest scores (M = 69.75, SD = 10.81) were significantly higher than their pretest scores (M = 58.84, SD = 7.24), t(31) = 6.45, p < .001, with a large effect size (Cohen’s dz = 1.14). The qualitative findings revealed that students mainly used Generative AI to simplify difficult concepts, generate contextualized examples, refine their understanding through follow-up prompts, and verify AI-generated responses. Overall, Generative AI functioned as a supportive learning resource in Sociolinguistics, although its effectiveness depended on students’ critical and strategic use.
References
Alfiras, M. I. I., Emran, A. Q., & Mohamed, A. M. (2025). Ethics and governance of generative AI in education: a systematic review on responsible adoption. Discover Education, 5(1), 37. https://doi.org/10.1007/s44217-025-01051-y
Andewi, W., Waziana, W., Wibisono, D., Putra, K. A., Hastomo, T., & Oktarin, I. B. (2025). From prompting to proficiency: A mixed-methods analysis of prompting with ChatGPT versus lecturer interaction in an EFL classroom. Journal of Studies in the English Language, 20(2), 210–238. https://so04.tci-thaijo.org/index.php/jsel/article/view/282318
Blahopoulou, J., & Ortiz-Bonnin, S. (2025). Student perceptions of ChatGPT: benefits, costs, and attitudinal differences between users and non-users toward AI integration in higher education. Education and Information Technologies, 30(14), 19741–19764. https://doi.org/10.1007/s10639-025-13575-9
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. Sage Publications.
Campbell-Kibler, K. (2009). The nature of sociolinguistic perception. Language Variation and Change, 21(1), 135–156. https://doi.org/10.1017/S0954394509000052
Chappell, W., & Kanwit, M. (2022). Do Learners Connect Sociophonetic Variation With Regional and Social Characteristics? Studies in Second Language Acquisition, 44(1), 185–209. https://doi.org/10.1017/S0272263121000115
Darvin, R. (2025). The need for critical digital literacies in generative AI-mediated L2 writing. Journal of Second Language Writing, 67, 101186. https://doi.org/10.1016/j.jslw.2025.101186
Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving Integration in Mixed Methods Designs—Principles and Practices. Health Services Research, 48(6pt2), 2134–2156. https://doi.org/10.1111/1475-6773.12117
George, A., & Barrios, H. (2024). Teaching language attitudes through digital storytelling projects. Canadian Journal of Linguistics/Revue Canadienne de Linguistique, 69(3), 333–354. https://doi.org/10.1017/cnj.2024.25
Hastomo, T., Widiati, U., Ivone, F. M., & Zen, E. L. (2025). EFL teachers’ self-efficacy in using AI tools: A comparative study in Indonesia. Advanced Education, 19(27), 103–123. https://doi.org/10.20535/2410-8286.335502
Lo, C. K., Yu, P. L. H., Xu, S., Ng, D. T. K., & Jong, M. S. (2024). Exploring the application of ChatGPT in ESL/EFL education and related research issues: a systematic review of empirical studies. Smart Learning Environments, 11(1), 50. https://doi.org/10.1186/s40561-024-00342-5
Marcos Miguel, N. (2022). Exploring the Use of Corpus Tools for Teaching Language Variation to L2 Spanish Majors. Language, 98(2), e80–e107. https://doi.org/10.1353/lan.2022.0001
Qu, K., & Wu, X. (2024). ChatGPT as a CALL tool in language education: A study of hedonic motivation adoption models in English learning environments. Education and Information Technologies, 29(15), 19471–19503. https://doi.org/10.1007/s10639-024-12598-y
Sawalha, G., Taj, I., & Shoufan, A. (2024). Analyzing student prompts and their effect on ChatGPT’s performance. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2397200
Stöhr, C., Ou, A. W., & Malmström, H. (2024a). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7, 100259. https://doi.org/10.1016/j.caeai.2024.100259
Stöhr, C., Ou, A. W., & Malmström, H. (2024b). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7, 100259. https://doi.org/10.1016/j.caeai.2024.100259
Strzelecki, A. (2024). Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innovative Higher Education, 49(2), 223–245. https://doi.org/10.1007/s10755-023-09686-1
Trinovita, D., Nurchurifiani, E., Hastomo, T., Andewi, W., & Hasbi, M. (2025). Exploring the Influence of Generative AI on Self-Regulated Learning: A Mixed-Methods Study in the EFL Context. Jurnal Iqra’ : Kajian Ilmu Pendidikan, 10(2), 301–316. https://doi.org/10.25217/ji.v10i2.6389
Vo, T. K. A., & Nguyen, H. (2024). Generative Artificial Intelligence and ChatGPT in Language Learning: EFL Students’ Perceptions of Technology Acceptance. Journal of University Teaching and Learning Practice, 21(06). https://doi.org/10.53761/fr1rkj58
Wang, C., & Wang, Z. (2025). Investigating L2 writers’ critical AI literacy in AI-assisted writing: An APSE model. Journal of Second Language Writing, 67, 101187. https://doi.org/10.1016/j.jslw.2025.101187
Waziana, W., Andewi, W., Wibisono, D., Hastomo, T., & Muslihudin, M. (2025). Exploring ChatGPT’s impact on critical, creative, and reflective thinking skills: A mixed-methods study in an Indonesian EFL classroom. Applied Research on English Language, 14, 77–114. https://doi.org/10.22108/are.2025.145896.2564
Xiao, Y., & Zhi, Y. (2023). An Exploratory Study of EFL Learners’ Use of ChatGPT for Language Learning Tasks: Experience and Perceptions. Languages, 8(3), 212. https://doi.org/10.3390/languages8030212
Zhai, C., & Wibowo, S. (2023). A systematic review on artificial intelligence dialogue systems for enhancing English as foreign language students’ interactional competence in the university. Computers and Education: Artificial Intelligence, 4, 100134. https://doi.org/10.1016/j.caeai.2023.100134





