Is ChatGPT taking over the language classroom?
How language ideologies of large language models impact teaching and learning
DOI:
https://doi.org/10.25071/2564-2855.36Keywords:
language ideologies, ChatGPT, large language models, artificial intelligence, natural language processingAbstract
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/
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Mandy Lau
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NonCommercial — You may not use the material for commercial purposes.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.