Is academic writing thinking, or simply the written output at the end of a research project? Morten Hansen argues that as large language models become better at producing academic copy educators should focus on the fundamentally developmental and human aspects of research writing.
My students, like many others, have noticed the power of artificial intelligence. Let me put it like this: student essays read well these days! But as a researcher of education technologies and their business models, and as a teacher, I can’t help but ask: what does the usage of automated reasoning do to the student experience, and to the human experience?
I view my role as an educator as being one of supporting students in becoming the person they want to become, before they necessarily know what that looks like. This is the emerging process of developing free and autonomous individuals through learning. At its heart sits a pedagogical paradox that has shaped the institution of education: autonomy for the individual can be reached through socialisation into larger institutional norms and knowledges. In higher learning, we do this by introducing students to bodies of knowledge that we read, write about, and debate. The cherry on the cake is all the interesting things students do outside of the classroom in sports clubs, student societies, employment, their living arrangements, and much more. You know, living!
At the risk of sounding cliché: it really is about the journey of becoming. Becoming a person better able to express and participate in a wider range of the human experience. Let me illustrate this with an example from a popular YouTube channel hosted by vocal coach Cheryl Porter, who films coaching sessions with her students. The sessions are performative and their production is steeped in the commercial logics of the attention economy. Some videos, for example, mirror the well-known format of James Corden’s Carpool Karaoke. But through all the performance and framing, you can also see a process of becoming. In the clip, 11-year-old Isabella is learning to deepen her vocal cord control in order to master Adele’s ballad Easy on me. Adele reportedly wrote the song as a way to process her divorce. In the clip, you see how they work on vibrato consistency and frequency, vocal breaks, and more.
What you see in the video is repetition on the part of the student, and guidance on part of the teacher. Through this learning, social bonds are created, experiences are formed, and both are in a process of becoming.
We can imagine a world where students of music can use software to digitally achieve similar outputs through digital techniques such as pitch corrections. Similar output, however, does not equal similar learning, and thus opportunities for becoming. Put another way, the session between Isabella and Cheryl matters as more than just an output. It matters that the student learns to sing by herself, with her own lungs and vocal cords because the process opens up opportunities for personal transformation. The student’s embodied knowledge of the art of composition will deepen, as will her personal relationship to the emotional states and enduring themes that the song conveys: learning how to do things by yourself shapes who you are, and who you want to become. As teachers we try to create these pockets of opportunity for our students. Yet creating these pockets in increasingly digitised, competitive, and global learning environments raises a series of implications.
the apparent efficiency of technology in a learning context does not simply come from their algorithmic elegancy and computing power, but is created by moving the pedagogical goalpost for the activity that it automates
We are living in a moment where wealthy technology firms are becoming better and better at using proprietary algorithms to automate ever more aspects of human expression: reading, writing, singing, joking. Through my research, it has become increasingly clear to me that the apparent efficiency of technology in a learning context does not simply come from their algorithmic elegancy and computing power, but is created by moving the pedagogical goalpost for the activity that it automates. In this case, the purpose of learning. For example, if software can make you sound like a good singer at the click of a button, then this will probably be cheaper and easier to do than the alternative, which is to actually teach you how to do it. In the same way, it would be much easier for me (and by extension cheaper for my employer) to only teach students how to write with Large Language Models (LLMs). This is of course important and useful. But the pedagogical purpose and therefore the value of prompting and editing LLM outputs is different from that of carefully crafting your own ideas: learning to write clearly means learning to think clearly. It is a key skill that can help elevate students from knowledge consumers to cutting-edge knowledge producers. As Helem Betham asks: why is writing “developmental, or how do we make it so? And what kind of people are developed through the writing we ask them to do?” As a bare minimum, I want to help students develop into learned individuals who are able to discern the rhetorical patterns, nudges, framings, and assumptions that are produced by the algorithms they use.
But as an educator, I want more for my students. Rather than debate whether there is room for generative AI at university, the real fault line should instead be whether education must prioritise the becoming of free, autonomous, capable, and productive people: ensuring that students master these new technologies should not come at the expense of higher learning as defined above. Just like there is value in practicing vocal breaks over and over again to achieve mastery, so is there value in learning to read an academic article multiple times and getting under the skin of another’s research project. There is value in carefully crafting an argument, in speaking with other students face-to-face, in finding refuge in a quiet corner in the library. These activities are valuable, not because we can put them on a LinkedIn profile or list them in a job application, but because they change who we are as people, and who we want to become. The magic of these activities’ resides in their fleeting, social, but also introspective nature.
Professor Gourlay unpacks such magic through ephemerality, seclusion, and copresence, which are aspects of academic practice that can make them hard to observe and track. In short, much of academic life should be characterised by seeing fleeting ideas come and go, sitting by yourself with a book, and engaging with peers in physical space with the certainty that the encounter is not recorded. These practices, Gourlay argues, are fugitive. The escape is not from the digital, but from the totalising, ubiquitous, and unfettered network connectivity that constantly infiltrates the learning pockets we create together. Speeding up what must be slowed down, freezing what must be flowing, and valorising what must be priceless.
Such assertions, inevitably, are grounded in normative judgments about the kind of education we should offer. My view on this is simple: learning institutions must both engage with but maintain independence from the society they seek to reproduce and transform. After all, the value of academic life lies in what it is doing to people and communities engaging in it: How it provides students with pockets of humanity that allow them to just be, and in doing so, help them in the process of becoming.
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A version of this post appeared on the King’s College London Digital Humanities blog, Pockets of humanity in an automated world: reflections from a teacher.
Image Credit: Google DeepMind via Unsplash.
