To Hold Back the Rising Tide of Artificial Intelligence in Higher Education

Abstract

The disruptive nature of technology in higher education is not a new phenomenon, but the progressive drumbeats grow louder and louder amidst several chaos-inducing influences. A provocative piece in The Chronicle of Higher Education by Clay Shirky (2025) laments the decline of student success and engagement, which the writer sees as a vexing trend tied to generative artificial intelligence (AI). According to Shirky, “The fact that AI might help students learn is no guarantee it will help them learn.” In fact, the phenomenon may do the opposite. It may create the kind of maladaptive academic shortcutting and technology dependency that chips away at the ethical and practice criticality that nurses need to operate in today’s extraordinarily complex health care environments. Can the rising tides of negative and disruptive impacts of generative AI be held back? Electronic calculators in the 1970s threatened the learning of mathematics in higher education and beyond. Online learning grew in earnest in the 1990s and early 2000s, heralding the end of brick-and-mortar universities. Massive open online courses brought renewed warnings that they were the true harbinger of the end of higher education. Even the COVID-19 pandemic seemed to threaten the notion that we need campuses. Now enter AI, notably its generative flavor, into the fray. Barely recovered from the impacts of the pandemic, we reel in the transformative power AI brings, both positive and negative. In the span of one hour, I could conceivably scan textbooks, articles, or original essays, turning them from proprietary knowledge into digital fuel. I could feed these knowledge sources into any of a number of free AI applications that could convert them in seconds into infinite variations of presentations, infographics, podcasts, or graphic novels. To retain the appearance of authenticity, I could take a few minutes to train an AI avatar to use my face and voice to be the educational presence in all of that output. That voice-over presentation could even feature me mouthing the words, my voice almost perfectly matched but entirely fabricated by another set of applications that create a convincing educational avatar of me. But the rising tide does not need to wipe away everything we have built and believe in. In fact, the rising tide can lift all boats equally. To presume such a yes-or-no proposition is perhaps equally pollyannish, but as Shirky points out, the use of AI is not an all or nothing proposition in which students use it consistently for every assignment in every class. Rather, it is the option of default when stress, time, or other pressures drive one to a path of least resistance — just like any cheating strategy employed for just about any assignment since the dawn of naming “cheating” in education. It may seem impossible, insurmountable, to imagine that there is a path to engagement and motivation, and Shirky’s piece is definitely a dark warning about the decline of cognitive effort and increased dependence on technology in learning. The rising tides of generative AI threaten to wash away educational value like a tsunami of student learning malaise and further faculty frustration and discontent. The rising tides might also wipe away outdated and low-value learning experiences while lifting all boats, notably lifting learning options for all learners who may have been alienated by traditional lecture and poorly served by the cycle of cram, memorize, regurgitate, and forget. Radical technologies like generative AI require a radical rethinking of the how and why for nursing education. We have to want it. We have to work for it. We have to recognize what it brings in order to think deeper and be creative with technology and without it.

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