What an AI benchmarking exercise in Asia reveals for HE

Generative artificial intelligence is reshaping the conditions under which knowledge is produced and learned in universities worldwide. It has moved into the everyday life of the university. Students generate essays and summaries with ease. Lecturers use it for feedback, course design, and structuring learning tasks. Universities develop policies and guidelines while practices shift within classrooms and across systems.

Recently, I engaged with three leading universities in Southeast Asia – in Indonesia, Malaysia and Singapore – to examine how institutions in the Global South are addressing this emerging issue. I focused on understanding how policy, infrastructure and pedagogical elements shape their approaches.

These visits formed part of a broader research effort to develop benchmark insights into institutional responses to AI in teaching and learning.

They also connect to a wider imperative in my work to understand how universities across the African continent engage technological change in ways that advance equity, deepen learning, and sustain their epistemic missions.

The question guiding this work is direct and far-reaching. How do universities respond to a shift that changes how knowledge is produced, engaged and recognised?

From disruption to reconfiguration

Across the Southeast Asian engagements, a shared recognition is evident. AI reshapes the foundations of teaching and learning. It affects one of the most established assumptions of university education, namely that written work provides a reliable indicator of student understanding.

Students now produce fluent and coherent outputs with minimal effort. Generative tools replicate academic forms with speed and ease. The issue, therefore, extends beyond academic integrity. It concerns how learning itself is recognised and how intellectual engagement becomes visible within academic work.

Universities are responding through adaptation. In one case, operating within a nationally coordinated system, policy frameworks structure institutional response and support gradual pedagogical adjustment.

In another, a globally positioned university has moved toward redesign, situating AI within the learning environment and reworking assessment around explanation, interpretation and justification. In a third, a policy-driven model supports a measured approach grounded in ethical principles and sustained through capacity-building initiatives.

These responses differ in emphasis and pace. They share a similar direction. Universities are moving toward reconfiguring the conditions of teaching and learning.

Assessment as a site of change

Assessment has become a central site where these shifts are being worked through. Written outputs now require new forms of interpretation. Universities are introducing oral components, in-class engagements, and iterative assessment practices that require students to explain their reasoning and account for their interpretations.

In some contexts, a distinction is drawn between supervised and unsupervised tasks, reflecting an understanding that AI forms part of contemporary learning environments. These approaches recognise that the presence of AI must be addressed through design rather than exclusion.

These developments remain in motion. Practices vary across disciplines and faculties, and experimentation continues. Within this variation, a pattern is emerging. Assessment is shifting toward an emphasis on process, reasoning and explanation. The focus rests on how students arrive at their responses and on the quality of their engagement with knowledge.

This signals a broader movement toward knowledge design. Learning involves interpretation, application and judgement, and students demonstrate understanding through explanation and justification.

Governance and pedagogy

Institutional policy provides an important framework for response. Universities have developed guidelines, ethical frameworks and training initiatives that create shared orientation and structure. These efforts support institutional engagement with AI and provide a basis for coordinated action.

At the level of practice, pedagogy assumes central importance. Lecturers interpret policy within disciplinary contexts and design learning tasks that reflect the conceptual demands of their fields. They structure assessment practices and shape learning environments in ways that engage students meaningfully with knowledge.

Academic judgement remains central in this process. It informs how AI is integrated into teaching and learning and guides the design of pedagogical practices. The central question, therefore, concerns pedagogy. Universities are called to design learning environments that sustain conceptual engagement and intellectual development.

A global challenge of inequality and epistemic access

The Southeast Asian cases provide insight into institutional adaptation. They also draw attention to the conditions that shape response across different contexts.

Higher education systems worldwide are characterised by significant differences in infrastructure, resources and institutional capacity. These differences influence how AI is adopted and integrated into teaching and learning.

Access to digital tools, levels of AI literacy, and the capacity to redesign curricula and assessments vary widely. These conditions shape how students engage with knowledge and how they experience learning.

A deeper concern arises in relation to epistemic inequality. This refers to unequal access to the processes through which knowledge is understood, produced and validated.

Students engage AI in different ways. Some use it to deepen engagement with knowledge, while others rely on it in ways that limit their interaction with underlying concepts. These patterns influence the development of intellectual capacities and shape access to knowledge itself.

For universities across the Global South, this challenge carries particular urgency. Longstanding inequalities in schooling, infrastructure and institutional resourcing shape how students encounter and use AI. These conditions call for deliberate pedagogical and institutional responses that support equitable engagement with knowledge.

[h]Reclaiming the pedagogical core

A central insight emerges from these engagements.

Universities require a pedagogical response that addresses the changing conditions of knowledge. Learning involves structured engagement with ideas and concepts. Tasks require interpretation, application and justification. Assessment focuses on reasoning and explanation, and pedagogy engages students in processes that make understanding visible.

These practices support conceptual development and strengthen the capacity for judgement. They enable students to engage with knowledge in ways that extend beyond surface-level interaction and contribute to deeper forms of understanding.

At a broader level, this work involves reaffirming the epistemic role of the university. Higher education cultivates forms of knowledge that enable students to participate meaningfully in intellectual and social life and to engage critically with the world.

A moment of decision

Generative artificial intelligence presents universities with a moment of decision that extends beyond technological adoption. Institutions shape their responses through policy, pedagogy and practice. The Southeast Asian cases show that adaptation is underway and that universities are engaging with the implications of AI through redesign processes that encompass teaching, learning and assessment.

The challenge for universities worldwide is how this work unfolds across diverse contexts while remaining attentive to the principles of equity, justice and meaningful engagement with knowledge. The stakes are significant. They concern the role of the university as a site of knowledge production, critical inquiry and social contribution.

Universities are called to recognise AI as part of the contemporary knowledge environment and to respond through pedagogical practices that sustain conceptual depth, judgement and participation in knowledge practices. The question before us is how we shape these responses to strengthen the intellectual and public purposes of higher education.

Universities now stand at a threshold where the future of knowledge is being reshaped in real time. Their task is to respond with clarity and purpose, ensuring that learning remains a site of depth, judgement and meaningful human engagement.

Aslam Fataar is a research and development professor in higher education transformation in the Department of Education Policy Studies at Stellenbosch University in South Africa.

This article is a commentary. Commentary articles are the opinions of the authors and do not necessarily reflect the views of University World News.