Oscar Koopman, associate professor in curriculum studies and vice dean of teaching and learning in the faculty of education at Stellenbosch University in South Africa, and Karen Joy Koopman, associate professor in commerce education in the faculty of education at the University of the Western Cape in South Africa, argue that the rise of artificial intelligence is forcing universities to rethink how learning, teaching and assessment are approached.
They suggest that the challenge facing higher education is not simply technological disruption. It is a deeper question about the purpose of teaching and how curriculum design can remain meaningful when artificial intelligence is capable of replicating many traditional academic tasks.
When Calls for Change Meet Academic Reality
Whenever discussions about transforming teaching and assessment arise in universities, a familiar response often emerges. It is rarely confrontational and frequently appears pragmatic. Many academics acknowledge that change is necessary, yet they quickly return to the daily pressures that shape their work.
In staffrooms and corridor conversations, lecturers often respond with concerns such as: “I am more concerned about my students passing”; “Our department must maintain throughputs”; and “How will these changes affect our accreditation requirements?” This is usually followed by the practical question: “What does this vision of transformation mean for the lecturer standing in front of a class on a Monday morning?”
According to the authors, this is not a question that should be dismissed. Instead, it represents one of the most important challenges in curriculum reform. If transformation cannot be translated into the everyday realities of teaching, it risks remaining an abstract aspiration rather than a meaningful shift in educational practice.
The lecturer who asks: “But what do I do on Monday morning?” is not resisting change. Rather, the question highlights a deeper issue within curriculum reform. It asks how a philosophical shift in education can be translated into a concrete pedagogical act. Ignoring the question does not weaken the argument for change. Instead, it makes the need to answer it even more urgent.
Pedagogical Courage in a Changing Academic Landscape
The authors argue that most academics already understand the underlying problems within current teaching systems. They recognise the difference between moments when real learning takes place and moments when content is simply delivered and reproduced.
They also recognise which assessments reveal genuine thinking and which only reward the appearance of thinking. The central question, therefore, is not whether problems exist. It is whether lecturers can act differently within systems that often reinforce traditional practices. Responding to disruption such as artificial intelligence requires what the authors describe as pedagogical courage. This is not the dramatic courage of sweeping institutional reforms. Instead, it is the quieter decision made within a single classroom or module to approach teaching differently.
It may involve shifting the central question of teaching from “Have my students acquired what I prescribed?” to “Are my students becoming themselves through this work?”
Pedagogical courage is not simply a personal attitude. It is expressed through everyday teaching practices. It can appear in the design of an assessment, the structure of a seminar, or the way a lecturer engages with student work.
Crucially, this kind of change often occurs before institutions formally adapt their policies. Educational revolutions rarely begin with official directives. They begin when individuals choose to view learning through a different lens.
Rethinking Assessment in the Age of AI
Assessment is where traditional educational assumptions become most visible. Historically, universities have asked a straightforward question when evaluating students: “Has the student acquired what we prescribed?” This approach focuses on the final product. It rewards the performance of learning rather than the process of thinking. Artificial intelligence has exposed the limitations of this model. By producing highly structured and polished responses, AI tools can replicate the appearance of academic work. As a result, they reveal that many traditional assessments were never truly measuring deep learning. The authors propose a different question for assessment: “Can I see this student’s presence in this work?”
They illustrate this idea through the way art experts authenticate paintings. When curators examine a possible Rembrandt, they do not focus only on technical skill or beauty. Instead, they search for traces of the artist’s presence in the painting process. This includes layered underpainting, revisions and evidence of struggle. These elements reveal the maker behind the work.
Student work can be approached in the same way. The goal is not simply to judge whether an essay is well organised or logically sound. Instead, educators might ask: “Can I see where this student got stuck? Where they changed their mind? What ethical weight they felt when they confronted a difficult idea?”
AI generated work struggles to meet this standard because it lacks evidence of personal engagement or intellectual journey. The issue is not detection but absence of presence.
In practical terms, this approach encourages assessments that document thinking over time. Students may show drafts, reflections and revisions rather than submitting a single final product. Oral defences can also play an important role. In such settings, students explain their reasoning and respond to questions in real time, demonstrating their understanding in their own words. These assessment methods are not entirely new. What changes is the purpose guiding them.
Designing Curriculum Around Real Intellectual Encounters
At the programme level, the authors suggest that curriculum design must begin with a different question. Many academic programmes start by identifying the attributes graduates should possess at the end of their studies. While useful, this approach assumes that educators already know exactly what students should become. The curriculum is then designed backwards from that predetermined outcome.
An alternative approach focuses on the intellectual challenges within each discipline. The guiding question becomes:
“What genuine problems does this discipline present to students, and how does genuine wrestling with these problems change who they are?”
This perspective leads to what the authors describe as phenomenological pedagogies. In studio apprenticeship models, for example, students engage with real problems that do not have predetermined answers. The lecturer acts not as a transmitter of information but as a practitioner demonstrating how to approach complex challenges.
Learning occurs through participation in this process rather than through passive reception of knowledge. Assessment in such contexts emphasises the development of thinking over time. Students demonstrate struggle, iteration and ethical reasoning as part of their work.
Phenomenological inquiry circles offer another example. In these settings, students explore questions connected to their own lived experiences, bringing objects or stories from their communities into academic discussion. These conversations do not necessarily aim for definitive conclusions. Instead, they create space for uncertainty and reflection.
Such approaches recognise that students often possess forms of knowledge shaped by language, culture, community and lived realities that traditional curricula struggle to acknowledge.
Embodied knowledge practices offer a similar shift. Some kinds of knowledge cannot be understood purely through reading or lectures. They must be experienced. Students might study ecology through gardening, geometry through traditional beadwork, or ethics through meaningful community engagement. These activities are not simply practical supplements to academic study. They open pathways to forms of understanding that conventional academic structures have often overlooked.
The Question That Remains for Higher Education
The expansion of artificial intelligence raises broader questions about the role of human work in society. As technology takes on more tasks previously associated with professional labour, universities may face a deeper challenge.
If societies have long defined human value through work, what happens when work itself changes dramatically?
Within universities, this question often appears in a simpler form. When lecturers ask, “What do I do?” they may also be asking whether their work continues to matter.
The authors suggest that it does. However, its significance depends on how teaching evolves. Meaningful change may not begin with institutional policies or strategic plans. It may begin with small decisions within classrooms. A single module can be redesigned. A single assessment can prioritise intellectual growth rather than performance. A single lecture can ask students not only what they know, but who they are becoming.
This is where transformation begins. Not in policy documents, but in everyday acts of teaching.
