3065-4793Journal of Diversity and Equity in Educational Development3065-4793eScholarship Publishing10.5070/jdeed.39915CommentariesHow Much Space Are We Willing to Sacrifice to Gen AI?le BlancSophiesleblanc@andrew.cmu.edu11Eberly Center, Carnegie Mellon University
The author has no conflicts of interest to disclose.
How much space are we willing to sacrifice to Gen AI? Aside from the (wonderful) Keynote speakers Drs. Z Nicolazzo and Amanda Tachine, none of the POD 2024 sessions were advertised to cover indigenous pedagogies or topics. Instead, what was not rare at POD 2024 were Gen AI sessions. I explore the following questions: How much space are we (as the POD community) willing to sacrifice to Gen AI? What is getting displaced from our conversations when so much revolves around Gen AI? In this commentary, I interrogate how our commitment to DEIJ values and practices may be misaligned with the space we give to Gen AI in our yearly conference through our conversations, our commitment to student learning, and our relationship building. We can write a different future where we think about what we want to take and what we want to leave from the dominant culture as called to us by our Keynote speakers.
Gen AIDEIJPOD conference
I came to POD 2024 looking forward to attending sessions on Indigenous Pedagogies in line with our POD values of “support[ing] members in advancing justice” and recognizing the value of “[d]iverse forms of evidence, a variety of inquiry methods” (POD Network, nd). Those sessions also help our community advance the meaning of our land acknowledgements by dedicating space and time to the contributions of Indigenous groups to our field. Back in 2023, there had been about five listed Indigenous Pedagogies sessions. However, in 2024, aside from the (wonderful) Keynote from Drs. Z. Nicolazzo and Amanda Tachine, none of the advertised sessions covered Indigenous pedagogies or topics. Instead, I encountered about 17 times more sessions with Generative Artificial Intelligence (Gen AI) in the title at POD 2024. Even in some sessions where Gen AI was not mentioned in the title or abstract, we still spent valuable time training educational developers on how to engineer useful prompts, or it turned out that presenters used Gen AI as a central mechanism for creating the content of the session.1 Given the scarcity of sessions central to the organization’s mission and the preponderance of Gen AI sessions, I wonder: How much space are we (as the POD community) willing to sacrifice to Gen AI? How does this space displace our efforts to improve student learning, to center prominent inclusive topics, and to create relationships in our communities?
When I think of space, I think of the space we make in our daily work to promote genuine learning for all our students. Gen AI developers promise us a tool, available at all hours, to accurately answer any question and explain any new topic in different ways. But that promise is currently a lie. Instead, Gen AI gives users the illusion of understanding a topic. Subject matter experts will find mistakes and missed nuances in explanations generated by Gen AI (for instance, the AI-generated metaphors of photosynthesis provided in Mollick and Mollick (2023) as good use of Gen AI were described by STEM colleagues as absolutely inaccurate and confusing, forcing the metaphor beyond accuracy and bringing in new concepts without defining them). And yet, many in the educational developer community suggest that students use Gen AI to get different explanations for concepts they do not understand (Bowen & Watson, 2024) without providing them the necessary knowledge to understand which parts of the output are erroneous. We tell students that Gen AI synthesizes the knowledge of the world to give them the best answer, but what they are actually getting is the most average answer.
When I think of space, I think of our conversations. Our annual POD conference is a chance to engage with many others in our field, to nurture and expand on current ideas. When we give so much weight to Gen AI, it sends a message that this should be an area of focus for our centers for teaching and learning (CTLs), likely at the expense of other important topics.2 Meanwhile, the inequities of Gen AI, such as its environmental impact (Ren, 2023), the biases in its output (Brown et al., 2020; Turk, 2023), and the lack of credit given to the actual human authors whose words and ideas it amalgamates (Lee et al., 2023), have been well documented. Some of the POD sessions I attended did acknowledge these issues, but after the acknowledgement, the conversation moved on to how to use the Gen AI tools. Isn’t that odd? Acknowledging harm and continuing to forge forward does not end or repair the harm. In fact, we are actively choosing to perpetuate the harm. When we give so much space in our conversations to a tool which is so much in conflict with our own stated values, we are walking a path of cognitive dissonance.
When I think of space, I also think of our relationships. Going back to the 2024 keynote, I invite you to take a moment and reflect: Is Gen AI helping you build relationships? When students are invited to research a topic they don’t understand by using Gen AI, instead of asking questions to their instructors, TAs, or friends, they are missing on key relationship building moments that help create that elusive networking (and those relationships could help them beyond the present). When we use Gen AI instead of published literature, we also lose our connection to the human authors who spent time and energy sharing their thoughts and knowledge, and the opportunities to communicate with them. Gen AI promoters want us to feel like we are indeed experiencing a relationship with a human when we use Gen AI. But let us not be fooled by the humanizing talk that gets repeated: Gen AI does not “hallucinate” (Lakhani, 2023), instead it gives erroneous outputs. Nor does it “think more carefully” when you enter a prompt such as “slow down, think more carefully” (Bowen & Watson, 2024)—It is just programmed to provide a different kind of answer. Take a moment and think about what those Gen AI developers gain by promoting this humanizing language. And what do we lose?
We, as a community, have a choice in how much space we are willing to give to Gen AI. None of the future of Gen AI is set in stone. None of the promises that have been made to us are bound to happen. We have agency. We have choices. Do we want to make space to build more relationships and to reflect on the mistakes of the past and present, or do we want to give more space to tools created by venture capitalists who promise us mirages of accuracy and efficiency while betting on the profits (Stadig, 2024; Wolverton, 2024) they will make from us? Drs. Z. Nicolazzo and Amanda Tachine asked us to think about what we want to keep from the dominant culture and what we want to leave. What will we keep? What will we leave? How will we write our future?
I counted the sessions offered Monday through Wednesday with Generative AI in the title. I did not review abstracts nor did I attend every session. Seventeen is likely to be an underestimate of the number of sessions involving Gen AI at POD 2024.
One recurring narrative around the need to address Gen AI is that students will use it whether we want it or not, making this a topic that is current and relevant. However, this narrative fails to acknowledge that we are making this future together and creating this self-fulfilling prophecy of the centrality of Gen AI by giving it so much space. We also legitimize the use of Gen AI by our students and colleagues when we dedicate so much attention to how to use it rather than the many ethical (beyond academic integrity) and equity issues that it presents. This contributes to this generalized fear of missing out, because “everyone else is using it.” There is much more to write but that would be its own op-ed.
Bowen, J. A., & Watson, C. E. (2024). Teaching with AI: A practical guide to a new era of human learning. Johns Hopkins University 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 (arXiv:2005.14165). arXiv. http://arxiv.org/abs/2005.14165Lakhani, K. (2023, July17). How can we counteract generative AI’s hallucinations?Digital Data Design Institute at Harvard. https://d3.harvard.edu/how-can-we-counteract-generative-ais-hallucinations/Lee, J., Le, T., Chen, J., & Lee, D. (2023). Do language models plagiarize?Proceedings of the ACM Web Conference 2023, USA, 3637–3647. 10.1145/3543507.3583199Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in classrooms: Five strategies, including prompts. SSRN Electronic Journal. 10.2139/ssrn.4391243Professional and Organizational Development Network. (n.d.). Vision, mission, and values. POD Network: Professional and Organizational Development Network in Higher Education. Retrieved December 5, 2024, from https://podnetwork.org/about/vision-mission-and-values/Ren, S. (2023, November30). How much water does AI consume? The public deserves to know. OECD.AI Policy Observatory. https://oecd.ai/en/wonk/how-much-water-does-ai-consumeStadig, D. (2024, November1). AI Monthly: Soaring costs of GenAI challenge short-term profitability. ING Think. https://think.ing.com/articles/ai-monthly-generative-ai-soaring-costs-challenge-short-term-profitability/Turk, V. (2023, October10). How AI reduces the world to stereotypes. Rest of World. https://restofworld.org/2023/ai-image-stereotypes/Wolverton, T. (2024, October20). Venture investors betting big on generative AI. San Francisco Examiner. https://www.sfexaminer.com/news/technology/venture-investors-bet-unprecedentedly-big-on-generative-ai/article_519aa5b4-8da4-11ef-ad43-2f29595002bc.html