1887
  1. Home
  2. e-Journals
  3. Australian Review of Applied Linguistics
  4. Online First Articles
  5. Online First Article
image of AIsplaining
PDF

Abstract

Abstract

The growing implementation of Generative AI (GenAI) in education has implications on the representation of knowledge and identity across languages. In a context where content biases have been reported in AI-generated content, it becomes relevant to interrogate the ways in which AI technologies represent different linguistic identities. This article conducts a systematic analysis of AI-generated content to identify the potential discursive strategies that can contribute to the perpetuation of existing sociolinguistic hierarchies. Data for this study consist of a set of GenAI explanations of assorted linguistic identities comprising dominant and non-dominant languages. The method combines specialization codes from the sociology of knowledge with discourse analysis. Specialization codes are composed of two axes with a differing degree of emphasis (+/−) on epistemic and social relations (ER/SR). This tool is useful for understanding explanations because it focuses on what sort of information is considered legitimate knowledge and what kinds of knowers are considered valid. The analysis of epistemic and social relations reveals a sociolinguistic hierarchy articulated across three definitory aspects of identity: relationship to the world, structuring across time and space, and possibilities for the future.

© John Benjamins Publishing Company
Available under the CC BY 4.0 license.
Loading

Article metrics loading...

/content/journals/10.1075/aral.24077.car
2025-01-06
2025-01-20

  • From This Site

    /content/journals/10.1075/aral.24077.car
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/10.1075/aral.24077.car/aral.24077.car.html?itemId=/content/journals/10.1075/aral.24077.car&mimeType=html&fmt=ahah

References

  1. Accurso, K.
    (2015) Language dominance. Encyclopedia of diversity and social justice, –.
     [Google Scholar]
  2. Adler, J. M., Dunlop, W. L., Fivush, R., Lilgendahl, J. P., Lodi-Smith, J., McAdams, D. P., McLean, K. C., Pasupathi, M., & Syed, M.
    (2017) Research methods for studying narrative identity: A primer. Social Psychological and Personality Science, (), –. 10.1177/1948550617698202
     https://doi.org/10.1177/1948550617698202 [Google Scholar]
  3. Aparicio, F. R., & Chávez-Silverman, S.
    (1997) Tropicalizations : transcultural representations of latinidad. Dartmouth College, University Press of New England.
     [Google Scholar]
  4. Appleby, R.
    (2016) Researching privilege in language teacher identity. TESOL Quarterly, (), –. 10.1002/tesq.321
     https://doi.org/10.1002/tesq.321 [Google Scholar]
  5. Baidoo-Anu, D., & Ansah, L. O.
    (2023) Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, (), –. 10.61969/jai.1337500
     https://doi.org/10.61969/jai.1337500 [Google Scholar]
  6. Bail, C. A.
    (2024) Can Generative AI improve social science?Proceedings of the National Academy of Sciences, (), e2314021121. 10.1073/pnas.2314021121
     https://doi.org/10.1073/pnas.2314021121 [Google Scholar]
  7. Boxleitner, A.
    (2023) Integrating AI in Education: Opportunities, Challenges and Responsible Use of ChatGPT. Education: Opportunities, Challenges and Responsible Use of ChatGPT (September 9, 2023).
     [Google Scholar]
  8. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., & Askell, A.
    (2020) Language models are few-shot learners. Advances in neural information processing systems, , –.
     [Google Scholar]
  9. Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L.
    (2023) A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. arXiv preprint arXiv:2303.04226.
     [Google Scholar]
  10. Carbajal-Carrera, B.
    (2023) Hierarchies among intertextual references: reading Reggaeton Ilustrado’s digital humour through the colonial matrix of power. Critical Discourse Studies, (), –. 10.1080/17405904.2022.2157851
     https://doi.org/10.1080/17405904.2022.2157851 [Google Scholar]
  11. Choudhury, M.
    (2023) Generative AI has a language problem. Nature human behaviour, (), –. 10.1038/s41562‑023‑01716‑4
     https://doi.org/10.1038/s41562-023-01716-4 [Google Scholar]
  12. Clarence, S.
    (2016) Exploring the nature of disciplinary teaching and learning using Legitimation Code Theory Semantics. Teaching in Higher Education, (), –. 10.1080/13562517.2015.1115972
     https://doi.org/10.1080/13562517.2015.1115972 [Google Scholar]
  13. Clyne, M.
    (1992) Pluricentric languages. Different Norms in Different Nations, Mouton de Gruyter.
     [Google Scholar]
  14. Coschignano, S., & Zanchi, C.
    (2023) Linguistic Means to Discursively Construct Dehumanization. Language, Expressivity and Cognition, . 10.5040/9781350332898.0011
     https://doi.org/10.5040/9781350332898.0011 [Google Scholar]
  15. Dignum, V.
    (2019) Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way (1st 2019. ed.). Springer International Publishing AG. 10.1007/978‑3‑030‑30371‑6
     https://doi.org/10.1007/978-3-030-30371-6 [Google Scholar]
  16. Dixon-Román, E., Nichols, T. P., & Nyame-Mensah, A.
    (2020) The racializing forces of/in AI educational technologies. Learning, Media and Technology, (), –. 10.1080/17439884.2020.1667825
     https://doi.org/10.1080/17439884.2020.1667825 [Google Scholar]
  17. Ducar, C.
    (2019) The sound of silence: Spanish heritage textbooks’ treatment of language variation ((Vol., pp.–). Iberoamericana Vervuert. 10.31819/9783865279033‑018
     https://doi.org/10.31819/9783865279033-018 [Google Scholar]
  18. Falbo, A., & LaCroix, T.
    (2022) Est-ce que vous compute? Code-switching, cultural identity, and AI. Feminist Philosophy Quarterly, (()). 10.5206/fpq/2022.3/4.14264
     https://doi.org/10.5206/fpq/2022.3/4.14264 [Google Scholar]
  19. Fang, X., Che, S., Mao, M., Zhang, H., Zhao, M., & Zhao, X.
    (2024) Bias of AI-generated content: an examination of news produced by large language models. Scientific Reports, (), –. 10.1038/s41598‑024‑55686‑2
     https://doi.org/10.1038/s41598-024-55686-2 [Google Scholar]
  20. Ferrara, E.
    (2024) GenAI against humanity: Nefarious applications of generative artificial intelligence and large language models. Journal of Computational Social Science, –. 10.1007/s42001‑024‑00250‑1
     https://doi.org/10.1007/s42001-024-00250-1 [Google Scholar]
  21. Flores, N., & Rosa, J.
    (2015) Undoing Appropriateness: Raciolinguistic Ideologies and Language Diversity in Education. Harvard Educational Review, (), –. 10.17763/0017‑8055.85.2.149
     https://doi.org/10.17763/0017-8055.85.2.149 [Google Scholar]
  22. Gilmore, N. J.
    (2009) Twenty-one countries, millions of native speakers, and one semester to teach it all: Linguistic variation in entry level college textbooks for Spanish.
     [Google Scholar]
  23. Goddard, C.
    (2020) De-Anglicising humour studies. The European Journal of Humour Research, (), –. 10.7592/EJHR2020.8.4.Goddard
     https://doi.org/10.7592/EJHR2020.8.4.Goddard [Google Scholar]
  24. Godwin-Jones, R.
    (2022) Partnering with AI: Intelligent writing assistance and instructed language learning. Language learning & technology, (), . 10.125/73474
     https://doi.org/10.125/73474 [Google Scholar]
  25. Gupta, M., Parra, C. M., & Dennehy, D.
    (2022) Questioning racial and gender bias in AI-based recommendations: Do espoused national cultural values matter?Information Systems Frontiers, (), –. 10.1007/s10796‑021‑10156‑2
     https://doi.org/10.1007/s10796-021-10156-2 [Google Scholar]
  26. Harwell, D., Mayes, B., Walls, M., & Hashemi, S.
    (2001) The accent gap. The Washington Post, 19 July 2018.
     [Google Scholar]
  27. Haslam, N.
    (2006) Dehumanization: An integrative review. Personality and social psychology review, (), –. 10.1207/s15327957pspr1003_4
     https://doi.org/10.1207/s15327957pspr1003_4 [Google Scholar]
  28. Haslam, N., Bain, P., Douge, L., Lee, M., & Bastian, B.
    (2005) More human than you: attributing humanness to self and others. Journal of personality and social psychology, (), . 10.1037/0022‑3514.89.6.937
     https://doi.org/10.1037/0022-3514.89.6.937 [Google Scholar]
  29. Hidalgo, M.
    (2006) Socio-historical determinants in the survival of Mexican indigenous languages. CONTRIBUTIONS TO THE SOCIOLOGY OF LANGUAGE, , . 10.1515/9783110197679.1.100
     https://doi.org/10.1515/9783110197679.1.100 [Google Scholar]
  30. Johnson, J.
    (2021) Internet penetration rate worldwide 2021, by region. Statista. https://www.statista.com/statistics/269329/penetration-rate-of-the-internet-by-region
     [Google Scholar]
  31. Kirk, H. R., Jun, Y., Volpin, F., Iqbal, H., Benussi, E., Dreyer, F., Shtedritski, A., & Asano, Y.
    (2021) Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models. Advances in neural information processing systems, , –.
     [Google Scholar]
  32. Kteily, N. S., & Landry, A. P.
    (2022) Dehumanization: trends, insights, and challenges. Trends in cognitive sciences, (), –. 10.1016/j.tics.2021.12.003
     https://doi.org/10.1016/j.tics.2021.12.003 [Google Scholar]
  33. Kundi, B., El Morr, C., Gorman, R., & Dua, E.
    (2023) Artificial Intelligence and Bias: A scoping review. AI and Society, –.
     [Google Scholar]
  34. Leander, K. M., & Burriss, S. K.
    (2020) Critical literacy for a posthuman world: When people read, and become, with machines. British Journal of Educational Technology, (), –. 10.1111/bjet.12924
     https://doi.org/10.1111/bjet.12924 [Google Scholar]
  35. Leavy, P.
    (2017) Research Design: Quantitative, Qualitative, Mixed Methods, Arts-Based, and Community-Based Participatory Research Approaches. The Guilford Press.
     [Google Scholar]
  36. Levisen, C.
    (2019) Biases we live by: Anglocentrism in linguistics and cognitive sciences. Language Sciences, , 101173. 10.1016/j.langsci.2018.05.010
     https://doi.org/10.1016/j.langsci.2018.05.010 [Google Scholar]
  37. Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J.
    (2023) GPT detectors are biased against non-native English writers. Patterns, (). 10.1016/j.patter.2023.100779
     https://doi.org/10.1016/j.patter.2023.100779 [Google Scholar]
  38. Luccioni, A., & Viviano, J.
    (2021) What’s in the box? an analysis of undesirable content in the Common Crawl corpus. (Ed.),^(Eds.). Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 10.18653/v1/2021.acl‑short.24
     https://doi.org/10.18653/v1/2021.acl-short.24 [Google Scholar]
  39. Martin, J., Maton, K., & Doran, Y.
    (2019) Academic discourse: An inter-disciplinary dialogue (Accessing Academic Discourse (pp.–). Routledge. 10.4324/9780429280726
     https://doi.org/10.4324/9780429280726 [Google Scholar]
  40. Maton, K.
    (2014) Knowledge and knowers : towards a realist sociology of education. Routledge.
     [Google Scholar]
  41. Maton, K., & Chen, R. T.-H.
    (2020) Specialization codes: Knowledge, knowers and student success ((1 ed., pp.–). Routledge. 10.4324/9780429280726‑2
     https://doi.org/10.4324/9780429280726-2 [Google Scholar]
  42. McMillan, J., Beavis, A., & Jones, F. L.
    (2009) The AUSEI06: A new socioeconomic index for Australia. Journal of Sociology, (), –. 10.1177/1440783309103342
     https://doi.org/10.1177/1440783309103342 [Google Scholar]
  43. Mohamed, S., Png, M.-T., & Isaac, W.
    (2020) Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. Philosophy & Technology, , –. 10.1007/s13347‑020‑00405‑8
     https://doi.org/10.1007/s13347-020-00405-8 [Google Scholar]
  44. Müller, M.
    (2021) Worlding geography: From linguistic privilege to decolonial anywheres. Progress in Human Geography, (), –. 10.1177/0309132520979356
     https://doi.org/10.1177/0309132520979356 [Google Scholar]
  45. Norton, B.
    (2013) Identity and Language Learning: Extending the Conversation (2nd edition ed.). Multilingual Matters. 10.21832/9781783090563
     https://doi.org/10.21832/9781783090563 [Google Scholar]
  46. Rivera-Rideau, P., & Torres-Leschnik, J.
    (2019) The colors and flavors of my Puerto Rico: Mapping “despacito”’s crossovers. Journal of Popular Music Studies, (), –. 10.1525/jpms.2019.311009
     https://doi.org/10.1525/jpms.2019.311009 [Google Scholar]
  47. Rosenfeld, B., Imai, K., & Shapiro, J. N.
    (2016) An empirical validation study of popular survey methodologies for sensitive questions. American journal of political science, (), –. 10.1111/ajps.12205
     https://doi.org/10.1111/ajps.12205 [Google Scholar]
  48. Rubino, A.
    (2010) Multilingualism in Australia: Reflections on current and future research trends. Australian Review of Applied Linguistics, (), –. 10.2104/aral1017
     https://doi.org/10.2104/aral1017 [Google Scholar]
  49. Sandoval-Sánchez, A.
    (1999) José, can you see?: Latinos on and off Broadway. Univ of Wisconsin Press.
     [Google Scholar]
  50. Saúde, S., Raposo, M. A., Pereira, N., & Rodrigues, A. I.
    (2020) Resourcing an Ethical Global Issues Pedagogy With Secondary Teachers in Northern Europe ((pp. 47–66). IGI Global. 10.4018/978‑1‑7998‑4402‑0.ch003
     https://doi.org/10.4018/978-1-7998-4402-0.ch003 [Google Scholar]
  51. Shepherd, N.
    (2020) The grammar of decoloniality. InA. Deumert, A. Storch, & N. Shepherd (Eds.), Colonial and Decolonial Linguistics: Knowledges and Epistemes (pp.–). Oxford University Press. 10.1093/oso/9780198793205.003.0019
     https://doi.org/10.1093/oso/9780198793205.003.0019 [Google Scholar]
  52. Solnit, R.
    (2008) Men explain things to me (Facts didn’t get in their way). https://www.latimes.com/archives/la-xpm-2008-apr-13-op-solnit13-story.html
  53. Tuck, E., & Yang, K. W.
    (2012) Decolonization Is Not a Metaphor. Decolonization: Indigeneity, Education, & Society, (), –.
     [Google Scholar]
  54. van den Berg, G., & du Plessis, E.
    (2023) ChatGPT and generative AI: Possibilities for its contribution to lesson planning, critical thinking and openness in teacher education. Education sciences, (), . 10.3390/educsci13100998
     https://doi.org/10.3390/educsci13100998 [Google Scholar]
  55. Vandrick, S.
    (2009) Interrogating privilege: Reflections of a second language educator. University of Michigan Press. 10.3998/mpub.770457
     https://doi.org/10.3998/mpub.770457 [Google Scholar]
  56. Veronelli, G. A.
    (2015) The Coloniality of Language: Race, Expressivity, Power, and the Darker Side of Modernity. Wagadu: A Journal of Transnational Women’s & Gender Studies, .
     [Google Scholar]
  57. Zembylas, M.
    (2023) A decolonial approach to AI in higher education teaching and learning: Strategies for undoing the ethics of digital neocolonialism. Learning, Media and Technology, (), –. 10.1080/17439884.2021.2010094
     https://doi.org/10.1080/17439884.2021.2010094 [Google Scholar]
  58. Zou, J., & Schiebinger, L.
    (2018) AI can be sexist and racist — it’s time to make it fair. Nature (London), (), –. 10.1038/d41586‑018‑05707‑8
     https://doi.org/10.1038/d41586-018-05707-8 [Google Scholar]
/content/journals/10.1075/aral.24077.car
Loading
AIsplaining
John Benjamins ; https://doi.org/10.1075/aral.24077.car
/content/journals/10.1075/aral.24077.car
/content/journals/10.1075/aral.24077.car
Loading

Data & Media loading...

  • Article Type: Research Article
Keywords: identity ; coloniality ; language ; bias ; GenAI

Most Cited

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error