Skip to main content Skip to journal navigation menu Skip to site footer
contact@ukscip.com
Article

Language Ideology Formation in AI-Mediated Educational Context: A Sociolinguistic Analysis

Rugaiyah ORCID ,  Andi Idayani ORCID ,  Roziah ORCID ,  Nunuk Suryanti ORCID ,  Novri Gazali ORCID
    Received: 24 January 2026; Revised: 22 February 2026; Accepted: 25 March 2026; Published: 18 September 2026

    Abstract

    This study explores the formation of language ideology in an AI-mediated educational context through a sociolinguistic lens. Adopting an interpretative qualitative approach, the research investigates how interactions with generative Artificial Intelligence (AI) tools, such as ChatGPT, influence learners' and educators' perceptions of linguistic legitimacy, authority, and identity. In-depth semi-structured interviews were conducted with 25 participants, comprising undergraduate students, graduate students, and lecturers who regularly utilize AI for academic writing and communication. Using reflexive thematic analysis, three central themes emerged: (1) Normalization of AI-Standardized Language, where users consciously or unconsciously adapt their register and sentence structures to align with AI-generated formal patterns; (2) AI as Linguistic Authority, reflecting a shift where algorithmic systems are increasingly trusted as objective arbiters of academic correctness; and (3) Linguistic Resistance and Identity Preservation, demonstrating that users actively negotiate, modify, or reject AI outputs to maintain personal voice, naturalness, and contextual appropriateness. The findings reveal that AI serves not merely as a technical writing aid, but as a formative sociolinguistic environment that continuously shapes and redefines language norms. This study contributes to the intersection of educational sociolinguistics and digital media by illustrating the dialectical relationship between algorithmic standardization and human linguistic agency.

    Keywords

    References

    1. 1. Zhao, D. The impact of AI-enhanced natural language processing tools on writing proficiency: an analysis of language precision, content summarization, and creative writing facilitation. Educ. Inf. Technol. 2025, 30, 8055–8086. DOI: https://doi.org/10.1007/S10639-024-13145-5
    2. 2. Fitzek, S.; Bârgăoanu, A. Introducing Large Language Models in Communication and Public Relations Education: A Mixed-Methods Pilot Study. Int. J. Artif. Intell. Educ. 2025, 35, 2478–2494. DOI: https://doi.org/10.1007/S40593-025-00477-7
    3. 3. Li, B.; Tan, Y.L.; Wang, C.; et al. Two years of innovation: A systematic review of empirical generative AI research in language learning and teaching. Comput. Educ. Artif. Intell. 2025, 9, 100445. DOI: https://doi.org/10.1016/J.CAEAI.2025.100445
    4. 4. Wang, P.; Jing, Y.; Shen, S. A systematic literature review on the application of generative artificial intelligence (GAI) in teaching within higher education: Instructional contexts, process, and strategies. Internet High. Educ. 2025, 65, 100996. DOI: https://doi.org/10.1016/J.IHEDUC.2025.100996
    5. 5. Law, L. Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Comput. Educ. Open 2024, 6, 100174. DOI: https://doi.org/10.1016/J.CAEO.2024.100174
    6. 6. Wang, Y. Cognitive and sociocultural dynamics of self-regulated use of machine translation and generative AI tools in academic EFL writing. System 2024, 126, 103505. DOI: https://doi.org/10.1016/J.SYSTEM.2024.103505
    7. 7. Lee, S.; Jeon, J.; Mckinley, J.; et al. Generative AI and English language teaching: A global Englishes perspective. Annu. Rev. Appl. Linguist. 2025, 45, 85–108. DOI: https://doi.org/10.1017/S0267190525100184
    8. 8. Hu, T.; Kyrychenko, Y.; Rathje, S.; et al. Generative language models exhibit social identity biases. Nat. Comput. Sci. 2024, 5, 65–75. DOI: https://doi.org/10.1038/s43588-024-00741-1
    9. 9. Ayoub, N.F.; Balakrishnan, K.; Ayoub, M.S.; et al. Inherent Bias in Large Language Models: A Random Sampling Analysis. Mayo Clin. Proc. Digit. Health 2024, 2, 186–191. DOI: https://doi.org/10.1016/j.mcpdig.2024.03.003
    10. 10. Maly, I. AI, power and sociolinguistics. J. Sociolinguistics 2024, 28, 11–15. DOI: https://doi.org/10.1111/JOSL.12681
    11. 11. Park, S. AI Chatbots and Linguistic Injustice. J. Univers. Lang. 2024, 25, 99–119. DOI: https://doi.org/10.22425/JUL.2024.25.1.99
    12. 12. Grieve, J.; Bartl, S.; Fuoli, M.; et al. The sociolinguistic foundations of language modeling. Front. Artif. Intell. 2024, 7, 1472411. DOI: https://doi.org/10.3389/FRAI.2024.1472411
    13. 13. Muawanah, U.; Marini, A.; Sarifah, I. The interconnection between digital literacy, artificial intelligence, and the use of E-learning applications in enhancing the sustainability of Regional Languages: Evidence from Indonesia. Soc. Sci. Humanit. Open 2024, 10, 101169. DOI: https://doi.org/10.1016/J.SSAHO.2024.101169
    14. 14. Creswell, J.W.; Creswell, J.D. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches; Sage: Los Angeles, CA, USA, 2018.
    15. 15. Woolard, K.A. Language Ideology. Int. Encycl. Linguist. Anthropol. 2020, 1–21. DOI: https://doi.org/10.1002/9781118786093.IELA0217
    16. 16. McGrath, C.; Palmgren, P.J.; Liljedahl, M. Twelve tips for conducting qualitative research interviews. Med. Teach. 2019, 41, 1002–1006. DOI: https://doi.org/10.1080/0142159X.2018.1497149
    17. 17. Jarrahi, M.H. Interviewing AI: Using qualitative methods to explore and capture machines’ characteristics and behaviors. Big Data Soc. 2025, 12. DOI: https://doi.org/10.1177/20539517251381697
    18. 18. Guest, G.; Bunce, A.; Johnson, L. How Many Interviews Are Enough? An Experiment with Data Saturation and Variability. Field Method. 2006, 18, 59–82. DOI: https://doi.org/10.1177/1525822X05279903
    19. 19. Malterud, K.; Siersma, V.D.; Guassora, A.D. Sample Size in Qualitative Interview Studies: Guided by Information Power. Qual. Health Res. 2016, 26, 1753–1760. DOI: https://doi.org/10.1177/1049732315617444
    20. 20. Raees, M.; Meijerink, I.; Lykourentzou, I.; et al. From explainable to interactive AI: A literature review on current trends in human-AI interaction. Int. J. Hum. Comput. Stud. 2024, 189, 103301. DOI: https://doi.org/10.1016/J.IJHCS.2024.103301
    21. 21. Gal, S. Language Ideologies. In Oxford Research Encyclopedia of Linguistics; Aronoff, M., Ed.; Oxford Academic: New York, NY, USA, 2023. DOI: https://doi.org/10.1093/ACREFORE/9780199384655.013.996
    22. 22. Blodgett, S.L.; Barocas, S.; Daumé, H.; et al. Language (Technology) is Power: A Critical Survey of ‘Bias’ in NLP. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 5–10 July 2020; pp. 5454–5476. DOI: https://doi.org/10.18653/V1/2020.ACL-MAIN.485
    23. 23. Shin, D. Automating epistemology: how AI reconfigures truth, authority, and verification. AI Soc. 2025, 41, 1553–1559. DOI: https://doi.org/10.1007/s00146-025-02560-y
    24. 24. Benk, M.; Kerstan, S.; von Wangenheim, F.; et al. Twenty-four years of empirical research on trust in AI: a bibliometric review of trends, overlooked issues, and future directions. AI Soc. 2025, 40, 2083–2106. DOI: https://doi.org/10.1007/s00146-024-02059-y
    25. 25. Helm, P.; Bella, G.; Koch, G.; et al. Diversity and language technology: how language modeling bias causes epistemic injustice. Ethics Inf. Technol. 2024, 26, 8. DOI: https://doi.org/10.1007/S10676-023-09742-6
    26. 26. Ilbury, C.; Grieve, J.; Hall, D. Using social media to infer the diffusion of an urban contact dialect: A case study of Multicultural London English. J. Sociolinguistics 2024, 28, 45–70. DOI: https://doi.org/10.1111/JOSL.12653
    ×