AI in Higher Education

Provides explainers and emerging trends in the rapidly-developing landscape of AI, with a focus on Large Language Models (LLMs), AI tools, AI literacy, and the ethical considerations of using (or not using) these tools.

AI Topics in Depth

Waters Library Workshops

The following recorded Waters Library Workshops relate to artificial intelligence:

December 2024 - Bias in Algorithms & AI
September 2024 - It's Not Magic: AI & Accessibility Tools
August 2024 - AI Detection Tools: Caution Warranted
November 2023 - Generative AI and Higher Education
June 2023 - Artificial Intelligence 101

General Resources

Lacy, L. (2024, May 22). What is an LMM and how does it relate to AI Chatbots? Let us explain. CNET.
Manning, C. (2022). Artificial intelligence definitions. Stanford University Human-Centered Artificial Intelligence.
Maslej, N., Fattorini, L., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., Maniyka, J., Ngo, H., Niebles, J. C., Parli, V., Shoham, Y., Wald, R., Clark, J., & Perrault, R. (2023 April). The AI Index 2023 Annual Report. AI Index Steering Committee, Institute for Human-Centered AI, Stanford University.
Maslej, N., Fattorini, L., Perrault, R., Parli, V., Reuel, A., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Niebles, J. C., Shoham, Y., Wald, R., & Clark, J. (2024 April) The AI Index 2024 Annual Report. AI Index Steering Committee, Institute for Human-Centered AI, Stanford University.
National Institute of Standards and Technology. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST Trustworthy and Responsible AI: NIST AI 600-1).
Pasick, A. (2023, March 27). Artificial intelligence glossary: Neural networks and other terms explained. New York Times.
- Institutional access link
Stryker, C. & Scapiccio, M. (2024, March 22). What is generative AI? IBM Think.
UNESCO (2022). Ethics of Artificial Intelligence. UNESCO.
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P. S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., Kenton, Z., Brown, S., Hawkins, W., Stepleton, T., Biles, C., Birhane, A., Haas, J., Rimell, L., Hendricks, L. A., Isaac, W., Legassick, S., Irving, G., & Gabriel, I. (2021, December 8). Ethical and social risks of harm from language models. DeepMind. [pre-print]

Training and Data

Blouin, L. (2023, March 6). AI's mysterious ‘black box’ problem, explained. University of Michigan-Dearborn News.
Bommasani, R., Creel, K., Kumar, A., Jurafsky, D., & Liang, P. (2022) Picking on the same person: Does algorithmic monoculture lead to outcome homogenization? arXiv.org. [pre-print]
Chiang, T. (2023, February 9). ChatGPT is a blurry JPEG of the web.The New Yorker.
Ferrer, X., Van Nuenen, T., Such, J. M., & Criado, N. (2020). Discovering and categorising language biases in Reddit. arXiv.org. [pre-print]
Isaacs-Thomas, B. (2023, January 12). How AI turns text into images. PBS News Hour.
Jain, S., Suriyakumar, V., Creel, K., & Wilson, A. (2024). Algorithmic pluralism: A structural approach to equal opportunity. arXiv.org. [pre-print]
Liu, L. T. (2020, January 21). When bias begets bias: A source of negative feedback in AI systems. Microsoft Research Blog.
Narla, A., Kuprel, B., Sarin, K., Novoa, R., & Ko, J. (2018, September 20). Automated classification of skin lesions: From pixels to practice. Journal of Investigative Dermatology, 138(10), 2108-2110.
- Institutional access link
Webl, J., Glaese, A., Uesato, J., Dathathri, S., Mellor, J., Hendricks, L. A., Anderson, K., Kohli, P., Choppin, B., & Huang, P.-S. (2021). Challenges in detoxifying language models. arXiv.org. [pre-print]

Bias

Abid, A., Farooqi, M., & Zou, J. (2021) Persistent anti-Muslim bias in large language models. arXiv.org. [pre-print]
Aïvodji, U., Arai, H., Fortineau, O., Gambs, S., Hara, S., & Tapp, A. (2019). Fairwashing: the risk of rationalization. [Conference proceeding] International Conference on Machine Learning, Long Beach, CA.
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016, May 23). Machine bias. ProPublica.
Barlas, P., Kyriakou, K., Guest, O., Kleanthous, S., and Otterbacher, J. (2020, December). To “see” is to stereotype: Image tagging algorithms, gender recognition, and the accuracy – fairness trade-off. [Conference proceeding.] ACM Computer-Human Interactions.
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March 1-10). On the dangers of stochastic parrots: Can language models be too big? 🦜 [Conference proceeding]. Conference on Fairness, Accountability, and Transparency (FAccT ’21), Virtual Event, Canada.
Buolamwini, J. & Gebru, T. (2018) Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research. Conference on Fairness, Accountability, and Transparency.
- See also: http://gendershades.org/
Chapman University. (n.d.) Bias in AI. Chapman University Artificial Intelligence (AI) Hub.
Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters.
Ellinghausen, M. (2023). How algorithms automate bias. In M. F. Winters & the Winters Group Team (Eds.) Racial justice at work: Practical solutions for systemic change (pp. 239–251). Berrett-Koehler Publishers.
- eBook: Institutional access link
Hao, K. (2018, November 28). Can you make an AI that isn’t ableist? MIT Technology Review.
Heikkilä, M. (2022, December 12). The viral AI avatar app Lensa undressed me—without my consent. MIT Technology Review.
Hofmann, V., Kalluri, P. R., Jurafsky, D., & King, S. (2024). AI generates covertly racist decisions about people based on their dialect. Nature 633(8028), 147–154.
- Institutional access link
IBM. (2023, October 16). Shedding light on AI bias with real world examples. IBM Think.
Nicoletti, L. & Bass, D. (2023, June 9). Humans are biased. Generative AI is even worse. Bloomberg.
Schwartz, O. (2019, April 15). Untold history of AI: Algorithmic bias was born in the 1980s. IEEE Spectrum.
Small, Z. (2023, July 4). Black artists say A.I. shows bias, with algorithms erasing their history. New York Times.
- Institutional access link
Yin, L., Alba, D., & Nicoletti, L. (2024, March 7). OpenAI GPT sorts resume names with racial bias, test shows. Bloomberg.
Zhang, X., Li, S., Hauer, B., Shi, N., & Kondrak, G. (2023, December 6-10). Don’t trust ChatGPT when your question is not in English: A study of multilingual abilities and types of LLMs. [Conference proceeding]. 2023 Conference on Empirical Methods in Natural Language Processing, Singapore.

Academic Integrity and Higher Education

Bowen, J. A., & Watson, C. E. (2024, May 2). AI detection is a business. But should it be faculty business? The Chronicle of Higher Education.
- Institutional access link
Clark, M. (2021, April 8). Students of color are getting flagged to their teachers because testing software can’t see them. The Verge.
Darby, F. (2023, June 27). 4 steps to help you plan for ChatGPT in your classroom. The Chronicle of Higher Education.
Duffy, C. & Uzquiano, K. (2023, July 11). Bot or not? How to tell when you’re reading something written by AI. CNN Business.
Feathers, T. (2021, March 2). Major universities are using race as a “high impact predictor” of student success. The Markup.
Fowler, G. A. (2023, August 14). What to do when you’re accused of AI cheating. The Washington Post.
Habibzadeh, F. (2023). GPTZero Performance in Identifying Artificial Intelligence-Generated Medical Texts: A Preliminary Study. Journal of Korean Medical Science, 38(38), e319–e319.
Ibrahim, H., Liu, F., Asim, R., Battu, B., Benabderrahmane, S., Alhafni, B., Adnan, W., Alhanai, T., AlShebli, B., Baghdadi, R., Bélanger, J. J., Beretta, E., Celik, K., Chaqfeh, M., Daqaq, M. F., Bernoussi, Z. E., Fougnie, D., Garcia de Soto, B., Gandolfi, A., … Zaki, Y. (2023). Perception, performance, and detectability of conversational artificial intelligence across 32 university courses. Scientific Reports, 13(1), 12187–12187.
- Institutional access link
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779–100779.
- Institutional access link
Mills, A. (2023, June 14). Using ChatGPT in the workplace: Strategies for faculty, staff, and administrators. AI in Education: Unleashing Creativity and Collaboration webinar for the University of Kent. [Video]
Office of Educational Technology. (n.d.) Artificial intelligence.
Perkins, M., Roe, J., Postma, D., McGraughran, J., & Hickerson, D. (2024). Detection of GPT-4 generated text in higher education: Combining academic judgement and software to identify generative AI misuse. Journal of Academic Ethics, 22(1), 89-113.
Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGraughran, J., & Khuat, H. Q. (2024). GenAI detection tools, adversarial techniques and implications for inclusivity in higher education. arXiv.org. [pre-print]
Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2024). Can AI-Generated Text be Reliably Detected? arXiv.org. [pre-print]
Scarfe, P., Watcham, K., Clarke, A., & Roesch, E. (2024). A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study. PLoS ONE 19(6): e0305354.
Stanford Center for Teaching and Learning. (n.d.) Teaching and Learning with Generative AI. [Google Doc]. Stanford University CTL.
- Note: Some parts of this document are intended for faculty at Stanford, but other parts offer general advice.

Environmental Impact

Li, P., Yang, J., Islam, M. A., & Ren, S. (2023, April 6). Making AI less “thirsty”: Uncovering and addressing the secret water footprint of AI models. arXiv.org [pre-print].
Luccioni, A. S., Jernite, Y., & Strubell, E. (2024, October 15). Power Hungry Processing: Watts Driving the Cost of AI Deployment? arXiv.org. [pre-print]
Luccioni, A. S., Viguier, S., Ligozat, A.L. (2022, November 3). Estimating the carbon footprint of BLOOM, a 176B parameter language model. arXiv.org [pre-print].
See also (linked under "General Resources"):
- p. 154-157 of Stanford's AI Index 2024 Annual Report
- p. 120-122 of Stanford's AI Index 2023 Annual Report

Human Labor

Dzieza, J. (2023, June 20). AI is a lot of work. The Verge.
Meaker, M. (2023, September 11). These prisoners are training AI. Wired.
Perrigo, B. (2023, January 18). OpenAI used Kenyan workers on less than $2 per hour to make ChatGPT less toxic. TIME

Last Updated: Nov 5, 2025 9:53 AM
URL: https://etamu.libguides.com/aiinhighered
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