Navigating the use of generative artificial intelligence in our day-to-day lives means the same questions arise for educators: Should I – or my students – use GenAI in this task and, if so, how, where and when? However, with the ethical dimensions of GenAI increasingly apparent, including risk for propagating bias, concerns of data privacy and environmental costs, we should also pose the question “why?”
Much of our current communication with students on GenAI tends to centre on what is “allowed” or “appropriate” (or not). While necessary, this rule-based framing can resemble nutritional advice: eat this, don’t eat that. We ask our learners to engage in intellectual processes equivalent to eating their greens even when these feel slow and effortful. It is no surprise then that our students often reach for pizza, ie, GenAI-facilitated work that feels faster, smoother and less problematic.
- Students are asking for AI guidance, not just policy
- Apply the principles of critical pedagogy to GenAI
- GenAI has not broken assessment. It has exposed it
But student behaviour does not occur in a vacuum. Dietary habits and technology use are both shaped by choice architecture – the subtle design features that influence our decision-making. We know what foods we are meant to eat, but when we go to the supermarket, invisible marketing tactics can influence our behaviour. Take, for example, the placement of attention-grabbing (and profitable) items at eye level.
When we “shop” for information on the internet, similar tactics are at play. Nowadays, a standard query in Google frequently foregrounds a GenAI-generated summary before listing traditional links. The GenAI response is positioned as the first and most frictionless option for information-seekers.
Likewise, supermarkets have zoning tactics that influence buyer behaviour. Essential supplies, like bread and milk, are placed in a “cold zone” at the back of the store, and shoppers must travel through a “hot zone” of high-margin products to reach these. Recently, Microsoft users have found themselves redirected away from the generic Office.com page to GenAI tool Copilot as an entry point to their workflow. What was once a neutral landing page offering multiple pathways – email, documents, shared files – is now a GenAI chatbot asking how it may help.
This foregrounding of GenAI is normalising the technology for our learners and influencing their online behaviour in important ways that impact learning. Emerging research on GenAI highlights the concept of cognitive offloading, the delegation of mental effort to external tools. Cognitive offloading is not new. Writing, calculators and search engines have long extended human cognition. Used strategically, such tools free working memory and enable higher-order thinking.
The concern is not offloading per se, but offloading that is neither considered nor deliberate. This brings us back to the question of “why?” We need to assist our students in understanding why they are using GenAI – is it to access information, speed up lower-order thinking tasks or, at times, to bypass the difficult analytical work altogether?
Our learners use GenAI for multiple, diverse reasons and sometimes “reaching for the pizza” is the result of real-world barriers. Some students are time-poor, balancing employment and caregiving responsibilities. Others are studying through an additional language. For others, under-confidence or low motivation can make “just put it into ChatGPT” seem like the sensible approach.
With all this in mind, what can educators do to help our learners engage with GenAI more mindfully? Here are some practical steps:
- Highlight to students how GenAI interacts with learning processes. Explain when GenAI may support higher-order thinking and when it may short-circuit foundational processes. Framing the issue in terms of growth and academic progress, rather than rule compliance, invites shared responsibility.
- Discuss recognised concerns with GenAI, such as its significant environmental impact, to ensure that students are ethically informed about when, and when not, to use it.
- Role model how you use GenAI. Be transparent about where you employ GenAI in your own work, such as to overcome the blank page effect or to refine phrasing, and where you deliberately avoid it. Articulating your rationale demonstrates that the question is not “GenAI or not”, but rather “GenAI for what purpose?”
- Normalise productive struggle. Deep learning is effortful and can feel uncomfortable. If a task is made entirely frictionless, it may not be sufficiently stretching for the learner. Making space in class to discuss difficulty, and encourage willingness to embrace this, can recalibrate expectations and effort.
- Design learning activities that harness GenAI and externalise thinking processes. Incorporate think-aloud projects, annotated drafts, and student-generated critiques of GenAI responses. Invite students to explain how they engaged with GenAI, what value it added and, conversely, what errors or issues it introduced.
- Recognise and address underlying drivers of GenAI misuse. If a substantial proportion of a cohort is relying heavily on GenAI in ways that undermine learning, investigate why. Ask your students what’s happening: Are deadlines clustered? Do assignments feel disconnected from real-world practice? Are particular students disproportionately affected? Better understanding of contextual issues can lead to practical solutions.
As we increasingly embrace GenAI as part of the higher education landscape, we must adapt to the opportunities and challenges of this new technology. As educators, we have an important role in helping students to better understand what they stand to gain – or lose – through GenAI use, and thus make more informed choices for their own learning.
Jenny Moffett is an educationalist at RCSI University of Medicine and Health Sciences.
If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.
comment