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Draft:LLMs in Higher Education

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Large language models (LLM) in Higher Education

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Large language models (LLM) are systems that are “pretrained” on a vast body of natural-language texts (or corpora), following a particular model [1]

LLMs can serve a variety of purposes. The most well known are virtual assistants, where a model responds to an input (a prompt) and the output is a fluid and plausible sequence of human-like text, that is delivered without explicit programming. [2] A semblance of conversation between machine and human is generated.

In November, 2022,[3] the release of a generative pre-trained transformer (GPT) model powered by a LLM, created a series of shockwaves in the Higher Education Sector. [4][5] Depending on the boundaries that have been drawn,[6] these LLMs and GPTs have permitted natural language understanding and processing become fiercely discussed topic in scholarly discourse. [7]

What problems are LLMs expected to solve?

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LLMs and their production and generation of coherent text, that is indistinguishable from human writing, can increase the efficiency of teaching and research.[8] Chatbots can offer quick answers to common questions, they summarize and assist with literature reviews. They have also been shown to be useful as academic coaches [9] LLM's also represent a challenge for traditional assessment practices with their ability to generate essays or assignments in response to a prompt[10] leading to questions about academic integrity.[11]

The hype around LLM's has been extreme, [12] with many unable to decide whether AI is a blessing or curse.[13] The initial setting for the current gold rush is California, [14] Big tech companies in Silicon Valley have invested heaviliy in LLMs and launched the the conversational chatbot, chatGPT3.5 and other similar generative LLMS/AI technologies. The launch of DeepSeek by a Chinese company Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd[15] has led to "AI war" with US tech companies for a market share. [13]

Higher Education (HE) has been flooded with "AI-hype". Ed tech is seen as mission critical. Certain scholarly works have reported on the the benefits of LLMS, but others have highlighted the challenges and ethical concerns, such as data privacy, bias, "fake information", transparency, accountability, over-reliance and the digital divide.[16] Academics must be empowered with critical AI literacy skills to create ethical awareness among them in recognition of the implications and potential biases of AI technologies, whether they are practically feasible for the purpose and the critical reflection of their impact on the individual or society at large.[17]

Language teacher perspective

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The public advent of Large Language Models (LLMs) has presented both intriguing possibilities and significant challenges for language teachers. Concerns included academic integrity, and authenticity of students writing (assignments, essays, summaries, or even creative pieces) and the circumvention of the very learning processes.[18] Uncritical teachers have been cautioned about how learning might be circumvented by LLMs [19]

Language teachers have been forced to re-evaluate traditional assessment methods and reconsider the aspects of language proficiency they prioritize. Emphasis has shifted to a critical evaluation of information, the development of a unique voice and the revision of AI-generated content with AI literacy being integrated into the curriculum. [20]

LLMs offer valuable resources for generating practice materials, provide instant feedback on grammar and syntax and offer students an additional layer of support outside of class time. While not a substitute for nuanced human feedback, this immediate corrective capacity can be particularly useful for reinforcing basic language rules [21] while offering a personalized learning experience, tailored to individual student needs. [19]

Participating Scholars

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Their (current) positions

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Critical Sceptics Acknowledged Experts Curious Practitioners Committed Champions
Insert name Insert name Vanessa Willemse Johannes Cronje
Insert name Insert name Tony Mays Insert name
Insert name Insert name Insert name Insert name

Items

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References

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  1. ^ Mitchell, Melanie; Krakauer, David C. (2023-03-28). "The debate over understanding in AI's large language models". Proceedings of the National Academy of Sciences. 120 (13): e2215907120. arXiv:2210.13966. Bibcode:2023PNAS..12015907M. doi:10.1073/pnas.2215907120. PMC 10068812. PMID 36943882.
  2. ^ "Introduction to Large Language Models | Machine Learning".
  3. ^ Gorichanaz, Tim (2023-11-29). "ChatGPT turns 1: AI chatbot's success says as much about humans as technology". The Conversation. Retrieved 2025-03-20.
  4. ^ Milano, Silvia; McGrane, Joshua A.; Leonelli, Sabina (April 2023). "Large language models challenge the future of higher education". Nature Machine Intelligence. 5 (4): 333–334. doi:10.1038/s42256-023-00644-2. ISSN 2522-5839.
  5. ^ Kirwan, Adrian (October 2024). "ChatGPT and university teaching, learning and assessment: some initial reflections on teaching academic integrity in the age of Large Language Models". Irish Educational Studies. 43 (4): 1389–1406. doi:10.1080/03323315.2023.2284901. ISSN 0332-3315.
  6. ^ Gieryn, Thomas F. (1983). "Boundary-Work and the Demarcation of Science from Non-Science: Strains and Interests in Professional Ideologies of Scientists". American Sociological Review. 48 (6): 781–795. doi:10.2307/2095325. ISSN 0003-1224. JSTOR 2095325.
  7. ^ Lee, Jinsook; Hicke, Yann; Yu, Renzhe; Brooks, Christopher; Kizilcec, René F. (2024). "The life cycle of large language models in education: A framework for understanding sources of bias". British Journal of Educational Technology. 55 (5): 1982–2002. doi:10.1111/bjet.13505. ISSN 1467-8535.
  8. ^ Meyer, Jesse G.; Urbanowicz, Ryan J.; Martin, Patrick C. N.; O’Connor, Karen; Li, Ruowang; Peng, Pei-Chen; Bright, Tiffani J.; Tatonetti, Nicholas; Won, Kyoung Jae; Gonzalez-Hernandez, Graciela; Moore, Jason H. (2023-07-13). "ChatGPT and large language models in academia: opportunities and challenges". BioData Mining. 16 (1): 20. doi:10.1186/s13040-023-00339-9. ISSN 1756-0381. PMC 10339472. PMID 37443040.
  9. ^ Cronje, Johannes (2024). "Exploring the Role of ChatGPT as a Peer Coach for Developing Research Proposals: Feedback Quality, Prompts, and Student Reflection". Electronic Journal of e-Learning. 22 (2): 01–15. doi:10.34190/ejel.21.5.3042. ISSN 1479-4403.
  10. ^ Milano, Silvia; McGrane, Joshua A.; Leonelli, Sabina (April 2023). "Large language models challenge the future of higher education". Nature Machine Intelligence. 5 (4): 333–334. doi:10.1038/s42256-023-00644-2. ISSN 2522-5839.
  11. ^ Currie, Geoffrey M. (2023-09-01). "Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy?". Seminars in Nuclear Medicine. Preclinical. 53 (5): 719–730. doi:10.1053/j.semnuclmed.2023.04.008. ISSN 0001-2998.
  12. ^ Wyk, Micheal M. van; Adarkwah, Michael Agyemang; Amponsah, Samuel (2023-09-05). "Why All the Hype about ChatGPT? Academics' Views of a Chat-based Conversational Learning Strategy at an Open Distance e-Learning Institution". Open Praxis. 15 (3): 214–225. doi:10.55982/openpraxis.15.3.563. ISSN 1369-9997.
  13. ^ a b "Is ChatGPT an opportunity or a threat? Preventive strategies employed by academics related to a GenAI-based LLM at a faculty of education". Journal of Applied Learning and Teaching. 18 February 2024.
  14. ^ "War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education". Journal of Applied Learning & Teaching. 6 (1). 2023-04-25. doi:10.37074/jalt.2023.6.1.23. ISSN 2591-801X.
  15. ^ "Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co Ltd - Company Profile and News". Bloomberg.com. Archived from the original on 2025-02-14. Retrieved 2025-05-07.
  16. ^ Wyk, Micheal M. van (2025-04-15). "Student Teachers' Leveraging GenAI Tools for Academic Writing, Design, and Prompting in an ODeL Course". Open Praxis. 17 (1): 95–107. doi:10.55982/openpraxis.17.1.711. ISSN 1369-9997.
  17. ^ Wyk, Micheal van (12 February 2025). "Integration of GenAI tools by academics to humanise pedagogical spaces: An AI humanising pedagogical perspective". Journal of Applied Learning and Teaching.
  18. ^ Evangelista, Edmund De Leon (2025-01-01). "Ensuring academic integrity in the age of ChatGPT: Rethinking exam design, assessment strategies, and ethical AI policies in higher education". Contemporary Educational Technology. 17 (1): ep559. doi:10.30935/cedtech/15775. ISSN 1309-517X.
  19. ^ a b Suspitsina., T (2024). "Academic integrity in the age of ChatGPT: Challenges and perspectives". Higher Education Quarterly. 78 (2): 443–457.
  20. ^ Chen, X; Zou, D; Xie, H; Su, J (2024). "Integrating AI literacy into educational technology: A systematic review". Educational Technology & Society. 27 (1): 106–121.
  21. ^ Lee, J; Lee, H (2003). "The impact of ChatGPT on grammar learning in higher education". Journal of Applied Linguistics and Language Research. 10 (3): 150–162.
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