Article In:
Australian Review of Applied Linguistics: Online-First Articles

AIsplaining

Generative AI explains linguistic identities to me

ORCID logo | The University of Sydney
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.
Keywords: GenAI, language, identity, bias, coloniality
Article outline
  • 1.Introduction
  • 2.GenAI biases and epistemic oppression
  • 3.Methodology
  • 4.Analysis
    • 4.1GenAI explains dominant languages
      • Relationship to the world
      • Structuring of relationship to the world
      • Possibilities for the future
    • 4.2GenAI explains non-dominant languages
      • Relationship to the world
      • Structuring of relationship to the world
      • Possibilities for the future
  • 5.Discussion
  • 6.Conclusions
  • Notes
  • References
This content is being prepared for publication; it may be subject to changes.
References (58)
References
Accurso, K. (2015). Language dominance. Encyclopedia of diversity and social justice, 656–657.Google Scholar
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, 8 (5), 519–527. DOI logoGoogle Scholar
Aparicio, F. R., & Chávez-Silverman, S. (1997). Tropicalizations : transcultural representations of latinidad. Dartmouth College, University Press of New England.Google Scholar
Appleby, R. (2016). Researching privilege in language teacher identity. TESOL Quarterly, 50 (3), 755–768. DOI logoGoogle Scholar
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, 7 (1), 52–62. DOI logoGoogle Scholar
Bail, C. A. (2024). Can Generative AI improve social science? Proceedings of the National Academy of Sciences, 121 (21), e2314021121. DOI logoGoogle Scholar
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
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, 33 1, 1877–1901.Google Scholar
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
Carbajal-Carrera, B. (2023). Hierarchies among intertextual references: reading Reggaeton Ilustrado’s digital humour through the colonial matrix of power. Critical Discourse Studies, 21 (3), 341–360. DOI logoGoogle Scholar
Choudhury, M. (2023). Generative AI has a language problem. Nature human behaviour, 7 (11), 1802–1803. DOI logoGoogle Scholar
Clarence, S. (2016). Exploring the nature of disciplinary teaching and learning using Legitimation Code Theory Semantics. Teaching in Higher Education, 21 (2), 123–137. DOI logoGoogle Scholar
Clyne, M. (1992). Pluricentric languages. Different Norms in Different Nations, Mouton de Gruyter.Google Scholar
Coschignano, S., & Zanchi, C. (2023). Linguistic Means to Discursively Construct Dehumanization. Language, Expressivity and Cognition, 551. DOI logoGoogle Scholar
Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way (1st 2019. ed.). Springer International Publishing AG. DOI logoGoogle Scholar
Dixon-Román, E., Nichols, T. P., & Nyame-Mensah, A. (2020). The racializing forces of/in AI educational technologies. Learning, Media and Technology, 45 (3), 236–250. DOI logoGoogle Scholar
Ducar, C. (2019). The sound of silence: Spanish heritage textbooks’ treatment of language variation ((Vol. 211, pp. 347–368). Iberoamericana Vervuert. DOI logoGoogle Scholar
Falbo, A., & LaCroix, T. (2022). Est-ce que vous compute? Code-switching, cultural identity, and AI. Feminist Philosophy Quarterly, 8 ((3/4)). DOI logoGoogle Scholar
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, 14 (1), 1–20. DOI logoGoogle Scholar
Ferrara, E. (2024). GenAI against humanity: Nefarious applications of generative artificial intelligence and large language models. Journal of Computational Social Science, 1–21. DOI logoGoogle Scholar
Flores, N., & Rosa, J. (2015). Undoing Appropriateness: Raciolinguistic Ideologies and Language Diversity in Education. Harvard Educational Review, 85 (2), 149–171. DOI logoGoogle Scholar
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
Goddard, C. (2020). De-Anglicising humour studies. The European Journal of Humour Research, 8 (4), 48–58. DOI logoGoogle Scholar
Godwin-Jones, R. (2022). Partnering with AI: Intelligent writing assistance and instructed language learning. Language learning & technology, 26 (2), 5. DOI logoGoogle Scholar
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, 24 (5), 1465–1481. DOI logoGoogle Scholar
Harwell, D., Mayes, B., Walls, M., & Hashemi, S. (2001). The accent gap. The Washington Post, 19 July 2018.Google Scholar
Haslam, N. (2006). Dehumanization: An integrative review. Personality and social psychology review, 10 (3), 252–264. DOI logoGoogle Scholar
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, 89 (6), 937. DOI logoGoogle Scholar
Hidalgo, M. (2006). Socio-historical determinants in the survival of Mexican indigenous languages. CONTRIBUTIONS TO THE SOCIOLOGY OF LANGUAGE, 91 1, 87. DOI logoGoogle Scholar
Johnson, J. (2021). Internet penetration rate worldwide 2021, by region. Statista. [URL]
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, 34 1, 2611–2624.Google Scholar
Kteily, N. S., & Landry, A. P. (2022). Dehumanization: trends, insights, and challenges. Trends in cognitive sciences, 26 (3), 222–240. DOI logoGoogle Scholar
Kundi, B., El Morr, C., Gorman, R., & Dua, E. (2023). Artificial Intelligence and Bias: A scoping review. AI and Society, 199–215.Google Scholar
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, 51 (4), 1262–1276. DOI logoGoogle Scholar
Leavy, P. (2017). Research Design: Quantitative, Qualitative, Mixed Methods, Arts-Based, and Community-Based Participatory Research Approaches. The Guilford Press.Google Scholar
Levisen, C. (2019). Biases we live by: Anglocentrism in linguistics and cognitive sciences. Language Sciences, 76 1, 101173. DOI logoGoogle Scholar
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4 (7). DOI logoGoogle Scholar
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). DOI logo
Martin, J., Maton, K., & Doran, Y. (2019). Academic discourse: An inter-disciplinary dialogue (Accessing Academic Discourse (pp. 1–31). Routledge. DOI logoGoogle Scholar
Maton, K. (2014). Knowledge and knowers : towards a realist sociology of education. Routledge.Google Scholar
Maton, K., & Chen, R. T.-H. (2020). Specialization codes: Knowledge, knowers and student success ((1 ed., pp. 35–58). Routledge. DOI logoGoogle Scholar
McMillan, J., Beavis, A., & Jones, F. L. (2009). The AUSEI06: A new socioeconomic index for Australia. Journal of Sociology, 45 (2), 123–149. DOI logoGoogle Scholar
Mohamed, S., Png, M.-T., & Isaac, W. (2020). Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. Philosophy & Technology, 33 1, 659–684. DOI logoGoogle Scholar
Müller, M. (2021). Worlding geography: From linguistic privilege to decolonial anywheres. Progress in Human Geography, 45 (6), 1440–1466. DOI logoGoogle Scholar
Norton, B. (2013). Identity and Language Learning: Extending the Conversation (2nd edition ed.). Multilingual Matters. DOI logoGoogle Scholar
Rivera-Rideau, P., & Torres-Leschnik, J. (2019). The colors and flavors of my Puerto Rico: Mapping “despacito”’s crossovers. Journal of Popular Music Studies, 31 (1), 87–108. DOI logoGoogle Scholar
Rosenfeld, B., Imai, K., & Shapiro, J. N. (2016). An empirical validation study of popular survey methodologies for sensitive questions. American journal of political science, 60 (3), 783–802. DOI logoGoogle Scholar
Rubino, A. (2010). Multilingualism in Australia: Reflections on current and future research trends. Australian Review of Applied Linguistics, 33 (2), 17.11–17.21. DOI logoGoogle Scholar
Sandoval-Sánchez, A. (1999). José, can you see?: Latinos on and off Broadway. Univ of Wisconsin Press.Google Scholar
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. DOI logoGoogle Scholar
Shepherd, N. (2020). The grammar of decoloniality. In A. Deumert, A. Storch, & N. Shepherd (Eds.), Colonial and Decolonial Linguistics: Knowledges and Epistemes (pp. 303–324). Oxford University Press. DOI logoGoogle Scholar
Solnit, R. (2008). Men explain things to me (Facts didn’t get in their way). [URL]
Tuck, E., & Yang, K. W. (2012). Decolonization Is Not a Metaphor. Decolonization: Indigeneity, Education, & Society, 1 (1), 1–40.Google Scholar
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, 13 (10), 998. DOI logoGoogle Scholar
Vandrick, S. (2009). Interrogating privilege: Reflections of a second language educator. University of Michigan Press. DOI logoGoogle Scholar
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, 13 1.Google Scholar
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, 48 (1), 25–37. DOI logoGoogle Scholar
Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist — it’s time to make it fair. Nature (London), 559 (7714), 324–326. DOI logoGoogle Scholar