Maxwell Bogpene, PhD Student, University of British Columbia

This chapter explores what it means to write with integrity in the age of Generative AI, and as a student, it deeply resonated with my evolving understanding of what writing means in the context of my studies and research process. More and more, students like myself are turning to GenAI tools for help with polishing, paraphrasing, brainstorming, and summarizing large volumes of text. This growing reliance reflects what McNeil describes as a “transactional” approach to academic writing.

Reading the chapter and reflecting on my own use of GenAI, I’ve become increasingly concerned with the idea of outsourcing part of my thinking to a chatbot. When I allow the tool to take over tasks that are central to how I process and express ideas, I risk losing touch with the very skills I’m meant to be developing. One of the key takeaways from this chapter, for me, is the realization that I cannot surrender my knowledge production to GenAI because then it’s no longer truly mine to claim.

As a graduate student in training, this chapter serves as a reminder that writing is not just a product but a process that reflects and deepens my understanding of the field. And as McNeil rightly notes: “if I were training to be a chef, the act of choosing ingredients, knowing which ones to put together and how to make something that would be tasty, nutritious, or appealing, would all be conceptual, reflecting both foundational and higher-order understanding that I would need to demonstrate to satisfy the expectations of my culinary training” (7). In the same way, if I turn over too much of my learning process to GenAI, I risk weakening the very foundation of the intellectual and professional identity I am working hard to build. The chapter helped me reassess and question my own approach to using GenAI and believe many other students like myself will benefit from reading such a reflective piece.

Taiyebah Hussain, Undergraduate Student, University of British Columbia

This chapter reflects on the consequences of generative AI (GAI) pertaining to scholarly writing. McNeill explores the risk that GAI poses to academic integrity – writing is socially situated, meaning that it is dependent on specific audiences and contexts – by using GAI as a means to an end, it is possible that such contexts will be overlooked. An interesting example that McNeill points to in her chapter is the possible exclusion of already-marginalized voices in GAI responses. She writes:

“if the data set is restricted to digital-only sources, for example, it excludes older materials as well as primary sources such as archival records, and would likely omit sound files that might capture oral histories. If the LLM is trained only on English-language texts, such a restriction would similarly represent a limited, typically Eurocentric, knowledge set (e.g., Furze 2023).”

This passage suggests that as GAI tools are trained with biased texts, their usage gives way to the rise of similarly biased responses. GAI is indeed able to overlook the interests of marginalized communities. As the technology becomes more ubiquitous, it is important to promote data sovereignty as well as digital decolonization in interactions with GAI; this is the responsibility of academics. This includes understanding why we write, who we write for, the contexts in which we create and communicate our ideas. As writing is context-dependent, so should be the policies concerning GAI in higher education. It is important for University students to evaluate their use of GAI technology and determine whether that use is intentional and ethical and why.