Is ChatGPT taking over the language classroom?

How language ideologies of large language models impact teaching and learning

Authors

DOI:

https://doi.org/10.25071/2564-2855.36

Keywords:

language ideologies, ChatGPT, large language models, artificial intelligence, natural language processing

Abstract

ChatGPT generated much dialogue on the implications of large language models (LLMs) for language teaching and learning. Since language teachers are uniquely positioned to teach metalinguistic awareness, they can support their learners’ understanding of how LLMs are shaped by language ideologies and how their outputs are indexical of social power. This awareness would help learners be more conscientious in using LLMs, deciding how to interact with them and adapt their outputs for their purposes. This article introduces LLMs as statistical systems that predict linguistic forms. It surfaces two language ideologies that have shaped their development: the belief in the separability of language from its social contexts and the belief in the value of larger text corpora. It also highlights some ideological effects including uneven language performance, text outputs that reflect biases, privacy violations, circulation of copyrighted materials, misinformation, and hallucinations. Some suggestions for mitigating these effects are offered.

References

Barnett, S. (2023, January 30). ChatGPT is making universities rethink plagiarism. Wired. https://www.wired.com/story/chatgpt-college-university-plagiarism/

Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5185–5198. https://doi.org/10.18653/V1/2020.ACL-MAIN.463

Bender, E., Gebru, T., McMillan-Major, A., Shmitchell, S., & Anonymous. (2021). On the dangers of stochastic parrots: can language models be too big? Conference on Fairness, Accountability, and Transparency (FAccT ’21), March 3-10, 2021, Virtual Event, Canada, 271–278. https://doi.org/10.1145/3442188.3445922

Bianchi, T. (2023). Regional distribution of desktop traffic to Reddit.com as of May 2022 by country. Statista. https://www.statista.com/statistics/325144/reddit-global-active-user-distribution/

Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. 30th Conference on Neural Information Processing Systems, 1–9. https://dl.acm.org/doi/10.5555/3157382.3157584

Broussard, M. (2018). Artificial unintelligence: How computers misunderstand the world. The MIT Press.

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems. https://arxiv.org/pdf/2005.14165.pdf

C, D., & J, P. (2023, March 14). ChatGPT and large language models: What’s the risk? National Cyber Security Centre UK. https://www.ncsc.gov.uk/blog-post/chatgpt-and-large-language-models-whats-the-risk

Castelle, M. (2018). The linguistic ideologies of deep abusive language classification. Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), 160–170. https://doi.org/10.18653/v1/w18-5120

Crawford, K. (2021). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.

Crawl, C. (2023, April 7). Want to use our data? Common Crawl. https://commoncrawl.org/the-data/

Dickson, B. (2022, August 30). LLMs have not learned our language - we’re trying to learn theirs. VentureBeat. https://venturebeat.com/ai/llms-have-not-learned-our-language-were-trying-to-learn-theirs

Dixon, S. (2022). Distribution of Reddit users worldwide as of January 2022, by gender. Statista. https://www.statista.com/statistics/1255182/distribution-of-users-on-reddit-worldwide-gender/

Drenik, G. (2023, January 11). Large language models will define artificial intelligence. Forbes. https://www.forbes.com/sites/garydrenik/2023/01/11/large-language-models-will-define-artificial-intelligence

EduKitchen. (2023, January 21). Chomsky on ChatGPT, education, Russia and the unvaccinated. [Video]. YouTube. https://youtu.be/IgxzcOugvEI

Eliot, L. (2023, February 26). Legal doomsday for generative AI ChatGPT if caught plagiarizing or infringing, warns AI ethics and AI law. Forbes. https://www.forbes.com/sites/lanceeliot/2023/02/26/legal-doomsday-for-generative-ai-chatgpt-if-caught-plagiarizing-or-infringing-warns-ai-ethics-and-ai-law

Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. Picador.

Fester-Seeger, M.-T., & Schneider, B. (2023). AI technology as interactional human culture: Language, data practice and social struggle. European University Viadrina Frankfurt (Oder).

Gal, S., & Irvine, J. T. (2019). Signs of difference: Language and ideology in social life. Cambridge University Press.

Godwin-Jones, R. (2021). Big data and language learning: Opportunities and challenges. Language Learning & Technology, 25(1), 4–19. http://hdl.handle.net/10125/44747

Hern, A. (2023, March 21). Google’s Bard chatbot launches in US and UK. The Guardian. https://www.theguardian.com/technology/2023/mar/21/googles-bard-chatbot-launches-in-us-and-uk

Hsu, T., & Thompson, S. A. (2023). Disinformation researchers raise alarms about A.I. Chatbots. The New York Times. https://www.nytimes.com/2023/02/08/technology/ai-chatbots-disinformation.html

Jasper. (2023, April 7). Meet Jasper. Jasper. https://www.jasper.ai/

Jha, S. (2023, March 28). Will ChatGPT take your job - and millions of others? Al Jazeera. https://www.aljazeera.com/features/2023/3/28/will-chatgpt-take-your-job-and-millions-of-others

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103. https://doi.org/10.1016/J.LINDIF.2023.102274

Kelly-Holmes, H. (2019). Multilingualism and technology: A review of developments in digital communication from monolingualism to idiolingualism. Annual Review of Applied Linguistics, 39, 24–39. https://doi.org/10.1017/S0267190519000102

Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications, 82, 3713–3744. https://doi.org/10.1007/S11042-022-13428-4

Lardinois, F. (2023, February 7). Microsoft launches the new Bing, with ChatGPT built in. TechCrunch. https://techcrunch.com/2023/02/07/microsoft-launches-the-new-bing-with-chatgpt-built-in/

Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2023). Dissociating language and thought in large language models: a cognitive perspective. 2023. https://arxiv.org/abs/2301.06627v1

Marche, S. (2022, December 6). The college essay is dead. The Atlantic. https://www.theatlantic.com/technology/archive/2022/12/chatgpt-ai-writing-college-student-essays/672371/

Milroy, J. (2001). Language ideologies and the consequences of standardization. Journal of Sociolinguistics, 5(4), 530–555. https://doi.org/10.1111/1467-9481.00163

Milroy, J., & Milroy, L. (2012). Authority in language: Investigating standard English (4th ed.). Routledge.

Noble, S. (2018). Algorithms of Oppression. NYU Press.

O’Neil, C. (2016). Weapons of math destruction. Crown Books.

Okerlund, J., Klasky, E., Middha, A., Kim, S., Rosenfeld, H., Kleinman, M., & Parthasarathy, S. (2022). What’s in the chatterbox? Large language models, why they matter, and what we should do about them. In University of Michigan Technology Assessment Project. https://stpp.fordschool.umich.edu/research/research-report/whats-in-the-chatterbox

OpenAI. (2022, November). Introducing ChatGPT. OpenAI. https://openai.com/blog/chatgpt

OpenAI. (2023a, March 14). GPT-4. https://openai.com/research/gpt-4

OpenAI. (2023b, March 14). Terms of use. OpenAI. https://openai.com/policies/terms-of-use

P, D. (2023, March 22). What we can learn from how ChatGPT handles data densities and treats users. Data & Society: Points. https://points.datasociety.net/what-we-can-learn-from-how-chatgpt-handles-data-densities-and-treats-users-f62b2cb222c6

Perrigo, B. (2023, January 18). Exclusive: OpenAI used Kenyan workers on less than $2 per hour to make ChatGPT less toxic. Time. https://time.com/6247678/openai-chatgpt-kenya-workers/

Schneider, B. (2020). Language and publics in a global digital world. What is linguistic citizenship in the 21st century? Studia Universitatis Babeș-Bolyai Studia Europaea, 65(2), 45–70. https://doi.org/10.24193/subbeuropaea.2020.2.03

Tripodi, F. (2021). Ms. Categorized: Gender, notability, and inequality on Wikipedia. New Media & Society. https://doi.org/10.1177/14614448211023772

Véliz, C. (2020). Privacy is power: Why and how you should take back control of your data. Penguin Random House.

W3Techs. (2023, April). Usage statistics of content languages for websites, April 2023. https://w3techs.com/technologies/overview/content_language

Wachter-Boettcher, S. (2017). Technically wrong: Sexist apps, biased algorithms, and other threats of toxic tech. In W.W. Norton & Company.

WebText. (2023). WebText. https://paperswithcode.com/dataset/webtext

West, M., Kraut, R., & Chew, H. E. (2019). I’d blush if I could: Closing gender divides in digital skills through education. https://unesdoc.unesco.org/ark:/48223/pf0000367416.page=1

Writesonic. (2023, April 7). Writesonic: Best AI writer, copywriting & paraphrasing tool. Writesonic. https://writesonic.com/

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Published

2024-02-16

How to Cite

Lau, M. (2024). Is ChatGPT taking over the language classroom? How language ideologies of large language models impact teaching and learning. Working Papers in Applied Linguistics and Linguistics at York, 4, 1–11. https://doi.org/10.25071/2564-2855.36

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