Did you miss Part 1 of this article series? Find it here and Part 2 here. This article is part 3 of this series on “Re-imagining Critical Thinking in Higher Education in the Age of Artificial Intelligence” . Follow me for details of all the parts.
While the debate surrounding artificial intelligence has largely been shaped by universities and technology companies in North America, Europe, and parts of Asia, its implications for Africa (Africa here is used as a persona to represent Africans) are arguably even more profound. The continent is in one of the most consequential periods in its history. It possesses the world’s youngest population, one of the fastest-growing digital economies, expanding internet connectivity, vibrant entrepreneurial ecosystems, and increasing investments in digital infrastructure. At the same time, Africa continues to confront complex development challenges requiring innovative, contextually relevant, and ethically grounded solutions. Artificial intelligence therefore provides an extraordinary opportunity and significant vulnerability.
The future of Africa will not be determined by whether the continent adopts artificial intelligence. Rather, it will depend on whether Africa develops the human capital capable of governing, adapting, innovating, and critically interrogating AI in ways that advance its own development priorities. The question is not whether Africa is using AI. The more important question is whether Africa will become a producer of AI-enabled knowledge and innovation or remain primarily a consumer of technologies designed elsewhere. This distinction should shape higher education policy across the continent. Africa has often experienced technological revolutions as an adopter rather than an architect. From industrial manufacturing and pharmaceutical production to advanced computing and digital platforms, much of the continent’s technological engagement has historically involved adapting innovations developed in other regions. Artificial intelligence presents an opportunity to rewrite this narrative. Unlike previous industrial revolutions that depended heavily on physical infrastructure and capital-intensive manufacturing, the AI revolution is driven substantially by knowledge, talent, creativity, entrepreneurship, research, data, and innovation ecosystems. These are precisely the assets that universities are uniquely positioned to develop.
Yet this opportunity can only be realised if higher education prioritises intellectual capability alongside technological competence. Producing graduates who know how to use AI applications is important, but it is insufficient. Africa requires graduates capable of designing AI systems responsive to African realities, interrogating algorithmic bias, addressing ethical challenges in multilingual and multicultural contexts, developing indigenous datasets, strengthening digital sovereignty, and applying AI to improve healthcare, agriculture, education, climate resilience, public administration, manufacturing, and governance. Such capabilities depend fundamentally on critical thinking rather than technological literacy alone. This imperative aligns closely with the aspirations articulated in the African Union’s Agenda 2063, the continent’s strategic framework for inclusive and sustainable development. Agenda 2063 envisions “The Africa We Want” as prosperous, integrated, peaceful, well-governed, and driven by its own citizens. Aspiration 1 calls for inclusive growth and sustainable development founded upon science, technology, innovation, and human capital. Aspiration 6 further emphasises that Africa’s development must be people-driven, particularly through investments in women and youth (African Union Commission, 2015). These aspirations recognise a fundamental truth: technology does not transform societies independently. People do.
Artificial intelligence should therefore be understood not as a technological issue but as a human development issue.
Every aspiration contained within Agenda 2063, from improving public health and strengthening democratic governance to accelerating industrialisation and enhancing educational quality, ultimately depends on citizens capable of analysing evidence, solving complex problems, collaborating across disciplines, exercising ethical leadership, and adapting continuously to changing circumstances. These capacities emerge from educational systems that cultivate inquiry rather than conformity, reasoning rather than memorisation, and innovation rather than imitation. Africa’s demographic profile makes this challenge especially urgent. According to the United Nations, the continent’s population is projected to double by 2050, with young people constituting the largest labour force in the world. This “youth dividend” has the potential to become Africa’s greatest comparative advantage, or its greatest missed opportunity. A youthful population alone does not generate economic transformation. It must be accompanied by high-quality education capable of producing adaptable, entrepreneurial, and critically minded graduates who can create employment rather than simply seek it.
Artificial intelligence has the potential to significantly accelerate this transformation. AI is already supporting smart agriculture for smallholder farmers, expanding access to healthcare through diagnostic tools, improving educational access in underserved communities, strengthen disaster preparedness, optimise transport systems, enhance financial inclusion, and improve public service delivery. Across Africa, innovators are already applying AI to local challenges, from disease surveillance and climate-smart agriculture to language technologies and financial technologies tailored to African markets. These developments demonstrate that Africa need not remain a passive recipient of global AI innovations.
However, there is also a risk that the continent becomes increasingly dependent on imported AI systems trained predominantly on data, languages, cultural assumptions, and governance frameworks originating outside Africa. Such dependence raises concerns regarding algorithmic bias, cultural representation, data governance, privacy, digital sovereignty, and economic inclusion. AI systems trained primarily on non-African datasets may fail to adequately represent African realities, resulting in technologies that reinforce rather than reduce existing inequalities. Universities therefore have a critical responsibility to cultivate graduates capable not only of deploying AI but also of questioning whose knowledge AI represents, whose values it embeds, whose interests it serves, and whose voices remain absent. This challenge extends beyond technology into governance itself. Across the continent, governments increasingly rely upon data-driven decision-making, digital public services, predictive analytics, and AI-supported policy tools. While these technologies promise greater efficiency, they also require public leaders capable of exercising independent judgment.
Algorithms may identify patterns, but they cannot determine justice. They may optimise resource allocation, but they cannot decide what constitutes fairness. They may generate predictions, but they cannot replace democratic accountability. The future of African governance therefore depends not on replacing human decision-makers with intelligent systems but on producing leaders capable of governing intelligently alongside them. Higher education occupies the centre of this transformation. Universities educate the engineers who will build digital infrastructure, the lawyers who will regulate AI, the teachers who will prepare future generations, the health professionals who will integrate AI into clinical practice, the entrepreneurs who will establish technology enterprises, and the policymakers who will shape national innovation ecosystems. If universities fail to protect critical thinking while embracing artificial intelligence, Africa risks producing technically proficient graduates who possess advanced digital skills but insufficient intellectual independence to lead complex societies.
Re-imagining critical thinking is therefore not a conservative attempt to preserve traditional education. It is a strategic investment in Africa’s long-term competitiveness within the global knowledge economy. Nations that combine technological capability with human intellectual excellence will lead the AI era. Those that prioritise technological adoption without equivalent investment in critical reasoning may remain dependent upon innovations produced elsewhere. The defining question for African universities is no longer whether artificial intelligence is used in higher education. It is whether higher education can produce graduates capable of ensuring that artificial intelligence serves Africa’s development rather than shaping Africa’s future according to priorities defined beyond the continent.
Kenya’s Artificial Intelligence Strategy and the Future of Higher Education
Among African countries, Kenya is one of the continent’s most ambitious proponents of digital transformation. Often referred to as the “Silicon Savannah,” Kenya has built an international reputation for technological innovation through pioneering developments such as mobile money, digital entrepreneurship, financial technology, and expanding innovation ecosystems. The launch of the KenyaNational Artificial Intelligence Strategy 2025–2030 signals the country’s intention to position itself as a regional leader in responsible AI adoption, research, innovation, and digital economic transformation (Ministry of Information, Communications and the Digital Economy [MICDE], 2025). The strategy represents a significant milestone in Kenya’s digital development agenda. It recognises artificial intelligence as an enabling technology capable of transforming sectors including agriculture, healthcare, manufacturing, education, public administration, climate resilience, and financial services. Importantly, it emphasises responsible AI governance, research and innovation, digital infrastructure, talent development, and public-private partnerships as essential pillars of national competitiveness. The strategy also aligns with Kenya’s broader Digital Superhighway initiative and Bottom-Up Economic Transformation Agenda, reflecting an understanding that future economic growth will increasingly depend upon knowledge-intensive industries and digital capabilities. Yet the success of this strategy ultimately depends upon higher education institutions more than others.
National AI strategies do not implement themselves. They require researchers capable of advancing scientific knowledge, graduates able to innovate responsibly, policymakers who understand both technology and ethics, educators who can prepare future generations, and entrepreneurs capable of translating research into societal impact. In other words, the effectiveness of Kenya’s AI strategy will depend not simply on digital infrastructure or technological investment but on the quality of higher education. This reality invites a broader policy conversation. Much attention has been devoted to ensuring that universities integrate artificial intelligence into teaching, research, and administration. Comparatively less attention has been given to whether universities are redesigning learning itself to ensure that graduates retain the cognitive capacities most needed in an AI-enabled economy. AI literacy is becoming increasingly important. However, AI literacy without critical thinking risks producing graduates who can operate intelligent technologies without fully understanding their limitations, assumptions, biases, or ethical implications.
Kenya’s Competency-Based Curriculum (CBC) offers an important point of reflection in this regard. The CBC seeks to shift education away from rote memorisation towards competencies such as critical thinking, communication, creativity, citizenship, collaboration, digital literacy, and problem-solving. These aspirations closely mirror the competencies identified globally as essential for the future workforce. However, unless universities intentionally build upon these foundations, there is a danger that students experience a discontinuity between competency-oriented school education and higher education assessment systems that continue to reward information reproduction rather than analytical reasoning. The emergence of generative AI makes this alignment even more important. If learners are encouraged to develop critical thinking throughout basic education only to encounter university assessment models that AI can complete with minimal human reasoning, the intended benefits of competency-based education may be significantly weakened. Universities therefore need not only adopt AI technologies but also reconsider how they evaluate learning outcomes in an era where information generation is increasingly automated.
The Commission for University Education (CUE) has an equally significant role to play. As Kenya’s quality assurance and regulatory body for university education, CUE has traditionally focused on programme accreditation, academic standards, institutional governance, and quality enhancement. Artificial intelligence introduces new dimensions that extend beyond these traditional functions. Future quality assurance frameworks should increasingly evaluate whether programmes deliberately cultivate higher-order cognitive competencies, ethical reasoning, interdisciplinary problem-solving, digital responsibility, and AI literacy alongside disciplinary knowledge. This does not necessarily require entirely new accreditation systems. Rather, it calls for an evolution in how educational quality is defined. In the AI era, excellent programmes will not simply demonstrate that students acquire knowledge; they will demonstrate that graduates can critically interrogate knowledge produced by both humans and machines.
Kenya’s expanding investment in research also presents significant opportunities. Through institutions such as the National Research Fund (NRF), the country has sought to strengthen research capacity, innovation, and knowledge production across universities. Artificial intelligence offers unprecedented opportunities to accelerate scientific discovery, analyse complex datasets, support interdisciplinary collaboration, and improve research productivity. However, these benefits must be accompanied by equally robust investments in research integrity, ethical AI governance, data stewardship, reproducibility, and critical methodological training. AI can accelerate research, but it cannot replace scientific judgment. Similarly, ongoing reforms within the Technical and Vocational Education and Training (TVET) sector recognise that future employment increasingly demands digital competencies alongside technical skills. Yet TVET institutions, like universities, must avoid equating digital competence with intellectual competence. Graduates entering advanced manufacturing, engineering, healthcare technologies, logistics, agriculture, and digital industries will require continuous problem-solving abilities because AI itself will continue evolving throughout their careers. The most valuable workforce will therefore be one capable of learning, adapting, questioning, and innovating rather than merely operating existing technologies.
Perhaps the greatest policy opportunity lies in positioning Kenya’s universities as continental leaders in responsible AI education. Rather than competing primarily through technological infrastructure, Kenyan universities could distinguish themselves by pioneering educational models that integrate AI with critical thinking, ethical leadership, interdisciplinary learning, African knowledge systems, and innovation for sustainable development. Such an approach would strengthen Kenya’s global competitiveness while contributing meaningfully to Africa’s broader knowledge economy. Importantly, this requires a shift in institutional mindset. Artificial intelligence should not be viewed as an additional subject to be incorporated into existing curricula. Instead, it should catalyse rethinking the broader purposes of university education. Every discipline, from medicine and engineering to law, education, business, agriculture, theology, and the humanities, must now ask the same fundamental question:
what uniquely human capabilities should graduates possess that artificial intelligence cannot replace?
The answer to that question should shape curriculum design, assessment, faculty development, research priorities, accreditation standards, and national higher education policy. Kenya has already demonstrated remarkable leadership in embracing digital innovation. The next phase of leadership will depend not on how rapidly universities adopt artificial intelligence, but on how effectively they protect and strengthen the human capacities that will determine whether artificial intelligence advances national development responsibly, ethically, and inclusively. In this respect, the country’s AI strategy should be understood not only as a technology policy but also as an opportunity to renew the fundamental mission of higher education itself.
Watch out for Part 4 of the article, which will explore how Universities Must Rethink Assessment as Artificial Intelligence Redefines Learning.
Author
Dr. Gilbert A. Ang’ana is the Dean of School of Leadership, Business, and Technology at Pan Africa Christian (PAC) University whose research focus is on values-based and collaborative leadership and governance in Africa. He is a leadership, governance and policy consultant, teacher and coach and the Founder of Accent Leadership Group and Accent Global Initiative (Think Tank)
Did you miss Part 1 of this article series? Find it here. This article is part 2 of 4 of the series. Follow me for details of all the parts.
Artificial Intelligence Is Changing the Nature of Learning, Not Just the Tools of Learning
The conversation about artificial intelligence in higher education has largely focused on what students can do with AI. A far more important question, however, is what AI is doing to students’ thinking. This distinction is critical because education is fundamentally concerned with cognitive development rather than task completion. Universities do not exist to help students complete assignments efficiently; they exist to cultivate the intellectual capabilities that enable graduates to solve problems independently, make sound judgments, adapt to uncertainty, and generate new knowledge, especially at postgraduate level throughout their lives. Artificial intelligence therefore demands more than a technological response; it demands a re-examination of how human learning actually occurs.
Learning is often misunderstood as the accumulation of information. Contemporary research in cognitive psychology, neuroscience, and the learning sciences paints a very different picture. Learning is an active biological and cognitive process in which the brain constructs, reorganizes, and strengthens neural pathways through sustained engagement with ideas. Knowledge is not transferred into the mind like files copied onto a computer; it is actively built through questioning, reasoning, reflection, feedback, and repeated application. This process requires mental effort. Indeed, the very struggle associated with learning is not a sign of failure but evidence that meaningful learning is taking place.
One of the most recent concepts explaining this phenomenon is what Robert and Elizabeth Bjork describe as “desirable difficulties.” Their research demonstrates that learning strategies requiring greater cognitive effort often produce stronger long-term understanding than approaches that make learning appear easy (Bjork & Bjork, 2011). Activities such as recalling information from memory, solving unfamiliar problems, explaining concepts to others, and applying knowledge in new contexts create productive challenges that strengthen learning. Students frequently perceive these activities as difficult because they demand sustained mental engagement. Yet neuroscience suggests that such effort promotes learning by reinforcing neural connections associated with memory retrieval and conceptual understanding.
This insight has profound implications for artificial intelligence. AI significantly reduces many of the desirable difficulties through which expertise develops. A student struggling to synthesize competing academic perspectives can now obtain a coherent synthesis within seconds. A learner wrestling with the structure of an argument can request an instantly polished essay. Programming errors can be corrected automatically. Mathematical solutions can be generated without requiring students to identify underlying principles. While these capabilities undoubtedly improve efficiency, they may also limit opportunities for the cognitive effort that transforms novice learners into independent thinkers.
The same argument emerges from another principle in educational psychology: retrieval practice. Brown, Roediger, and McDaniel (2014), drawing upon decades of empirical research, demonstrate that actively retrieving knowledge from memory produces substantially deeper learning than repeatedly reviewing information. The act of recalling concepts, solving problems without immediate assistance, and reconstructing knowledge strengthens long-term retention while improving learners’ ability to transfer understanding to unfamiliar situations. Put simply, students learn more effectively when they are required to think rather than just recognize information.
Artificial intelligence changes this equation. When answers become immediately available, learners may bypass retrieval altogether. Instead of reconstructing knowledge through deliberate reasoning, they increasingly consult AI systems capable of generating persuasive responses almost instantaneously. The immediate result is greater efficiency. The long-term consequence, however, may be weaker conceptual understanding because the cognitive processes responsible for strengthening learning are exercised less frequently.
Closely related is the concept of metacognition, the capacity to think about one’s own thinking. Effective learners continuously monitor what they know, identify gaps in understanding, evaluate the quality of their reasoning, and regulate their learning strategies accordingly. Metacognition enables individuals to distinguish genuine understanding from superficial familiarity. It is widely regarded as one of the strongest predictors of academic success and lifelong learning. Generative AI presents both opportunities and risks for metacognitive development. Used well, AI can support reflection by providing alternative explanations, generating feedback, or exposing learners to competing viewpoints. Used uncritically, however, it can create an illusion of understanding. Students may mistake AI-generated explanations for personal comprehension simply because the output appears coherent and convincing. They may recognize an argument without ever constructing it themselves. This distinction is subtle but fundamental.
Understanding produced by external systems should never be confused with understanding developed through internal cognitive effort.
The work of educational psychologist John Sweller further illuminates this challenge. Sweller’s Cognitive Load Theory argues that human working memory possesses limited capacity and that effective instructional design should reduce unnecessary mental demands while preserving the cognitive effort essential for learning (Sweller, 1988). Importantly, Sweller does not advocate eliminating intellectual challenge altogether. Rather, he distinguishes between cognitive load that interferes with learning and cognitive effort that promotes it. Artificial intelligence has enormous potential to reduce unproductive cognitive burdens. For example, by automating repetitive administrative tasks, summarizing large volumes of information, or providing immediate formative feedback. These applications can free learners to focus on higher-order reasoning. Problems arise, however, when AI eliminates the productive cognitive work that students themselves must perform to develop expertise.
Educational researchers Paul Kirschner and Carl Hendrick have similarly cautioned against misconceptions about learning that prioritise convenience over cognition. Their work consistently demonstrates that expertise develops through deliberate practice, sustained attention, feedback, and repeated engagement with increasingly complex problems rather than through passive exposure to information (Kirschner & Hendrick, 2020). Learning, they argue, cannot be outsourced. Technologies may support it, accelerate aspects of it, or enrich it, but they cannot replace the biological and cognitive processes through which understanding is constructed.
Perhaps the most directly relevant concept for the AI era is cognitive offloading. Risko and Gilbert (2016) define cognitive offloading as the use of physical actions or external technologies to reduce the mental effort required for cognitive tasks. Humans have always offloaded aspects of cognition. We write notes instead of memorizing everything, use calculators instead of performing lengthy arithmetic mentally, and rely on GPS rather than memorizing complex routes. These practices are not inherently problematic. Indeed, they often enhance productivity and allow cognitive resources to be redirected toward more sophisticated tasks. The challenge arises when cognitive offloading replaces rather than complements higher-order thinking. Artificial intelligence differs fundamentally from earlier technologies because it increasingly performs tasks involving analysis, evaluation, synthesis, writing, reasoning, and creative generation, functions that lie at the heart of university education.
The critical question is therefore no longer whether students should use external tools. Rather, it is which aspects of cognition should remain fundamentally human.
Recent analyses by the Organisation for Economic Co-operation and Development (OECD) reinforce this concern. The OECD argues that generative AI should not be viewed primarily as a substitute for human intelligence but as a technology requiring education systems to place greater emphasis on analytical thinking, critical reasoning, ethical judgment, collaboration, and creativity, precisely because these uniquely human competencies become more valuable as AI capabilities expand (OECD, 2024). The future graduate will not compete with artificial intelligence by memorizing more information than machines. Graduates will distinguish themselves through their capacity to evaluate AI outputs, challenge assumptions, navigate ambiguity, integrate diverse perspectives, and make decisions grounded in ethical and contextual understanding.
The implications are profound. Artificial intelligence is not just changing how students learn; it is changing what it means to learn. The challenge for higher education is therefore not preserving traditional teaching methods but ensuring that technological efficiency does not come at the expense of intellectual development. Universities must intentionally design learning experiences that preserve the cognitive effort necessary for critical thinking while harnessing AI’s extraordinary capacity to enhance teaching and research. The objective should not be frictionless learning. It should be meaningful learning.
The New Crisis Nobody Is Talking About – The Outsourcing of Human Thinking
Public debate about artificial intelligence in higher education has been dominated by one issue: plagiarism. Universities have invested heavily in AI detection software, revised academic integrity policies, redesigned assessment regulations, and debated whether students should be permitted to use generative AI in coursework. While these discussions are understandable, they may also be distracting institutions from a much deeper educational crisis.
The greatest threat posed by artificial intelligence is not that students will submit work they did not write. It is that they may gradually stop doing the thinking that universities exist to develop.
This distinction fundamentally changes the nature of the debate. Academic integrity has traditionally focused on authorship. Did the student produce the submitted work? Was the assignment completed honestly? Were appropriate sources acknowledged? These remain essential questions. Yet in the age of artificial intelligence, they are no longer sufficient. A student may fully disclose AI use, comply with institutional guidelines, submit technically original work, and still engage in remarkably little independent thinking. The assignment may satisfy formal requirements while failing to achieve the educational purpose for which it was designed. This possibility exposes a critical weakness in many current university policies. Institutions are concentrating on protecting the originality of academic products while paying comparatively little attention to protecting the originality of intellectual processes. Yet universities exist primarily to cultivate the latter.
Original writing is not the same as original thinking.
An essay can be original because it has never previously existed, while the intellectual work underpinning it has been largely performed by artificial intelligence. Conversely, a student may produce an entirely authentic argument that builds thoughtfully upon existing scholarship, demonstrating deep reasoning, careful judgment, and genuine intellectual growth. The educational value lies not simply in the finished document but in the quality of thinking that produced it. This distinction is perhaps the most important educational question raised by generative AI. Higher education has assessed written assignments as proxies for cognitive development. Essays, dissertations, laboratory reports, policy analyses, and research projects were never valuable simply because they produced text. They were valuable because the process of constructing them required students to analyse evidence, compare competing ideas, organize arguments, reflect critically, solve problems, and communicate reasoning. Writing served as evidence that thinking had occurred.
Artificial intelligence disrupts this relationship. Today, it is increasingly possible to generate sophisticated academic products without engaging fully in the intellectual processes they were designed to measure. The educational risk therefore lies not in the technology itself but in confusing the production of knowledge artifacts with the development of human understanding. This phenomenon might be described as cognitive outsourcing, the progressive transfer of higher-order intellectual responsibilities from human learners to intelligent systems. Unlike traditional cognitive offloading, which largely involved routine memory or computational tasks, cognitive outsourcing reaches into the very capacities that define university education: interpretation, synthesis, evaluation, critical reflection, creativity, and judgment.
The consequences may emerge gradually rather than dramatically. Students may become less comfortable grappling with ambiguity because AI rapidly supplies answers. They may invest less time evaluating competing arguments because AI conveniently synthesizes them. They may lose confidence in developing independent positions because machine-generated responses appear more articulate than their own early drafts. Over time, intellectual perseverance, the willingness to wrestle with difficult ideas may quietly decline. This concern finds support in broader research on expertise development. Experts differ from novices not because they possess more information alone, but because they have developed richer conceptual frameworks, stronger pattern recognition, more refined judgment, and greater capacity to navigate uncertainty. These qualities emerge through years of deliberate practice, reflection, feedback, and sustained engagement with complex problems. They cannot simply be downloaded or generated on demand.
Ironically, artificial intelligence makes independent thinking more valuable, not less.
As AI systems become increasingly capable of generating information, summarizing evidence, drafting reports, and automating routine analysis, human comparative advantage shifts toward those capacities machines cannot reliably replicate. Ethical reasoning. Contextual judgment. Intellectual humility. Curiosity. Moral imagination. The ability to ask questions no algorithm has been instructed to answer. The wisdom to know when evidence is insufficient. The courage to challenge consensus. These are not residual skills left over after technological progress. They are becoming the defining competencies of leadership in an AI-mediated world.
This has profound implications for universities across Africa. The continent’s greatest resource is not artificial intelligence itself but its people, the creativity, resilience, diversity, and intellectual potential of one of the world’s youngest populations. If higher education unintentionally encourages graduates to depend on AI for thinking rather than empowering them to think with AI, Africa risks becoming a consumer of algorithmic intelligence developed elsewhere rather than a producer of contextually grounded knowledge and innovation. The challenge is therefore much larger than regulating AI use in examinations or detecting machine-generated assignments. Universities must redesign educational systems around a more fundamental objective: ensuring that every encounter with artificial intelligence strengthens rather than substitutes the learner’s own reasoning.
The future will not belong to graduates who can just prompt AI more effectively than others. It will belong to those who can critically evaluate its outputs, identify its limitations, recognise its biases, integrate its insights with human experience, and exercise the wisdom to know when the machine is wrong. In that sense, the defining purpose of higher education has not changed. It has become even more important. The central question is no longer whether students are using artificial intelligence. The central question is whether artificial intelligence is still being used in ways that preserve, deepen, and ultimately expand humanity’s capacity to think.
Part 3 will explore why protecting critical thinking matters even more for Africa. Stay tuned and follow the conversation.
Author
Dr. Gilbert A. Ang’ana is the Dean of School of Leadership, Business, and Technology at Pan Africa Christian (PAC) University whose research focus is on values-based and collaborative leadership and governance in Africa. He is a leadership, governance and policy consultant, teacher and coach and the Founder of Accent Leadership Group and Accent Global Initiative (Think Tank)
This article represents Part 1 of a 4-part series of perspectives I share on this topic
Throughout history, every major technological revolution has transformed the way human beings access, create, and share knowledge. The invention of the printing press democratized learning by making books widely available. The Industrial Revolution reshaped education to prepare citizens for an emerging economic order. The advent of computers accelerated information processing beyond human capacity, while the internet fundamentally changed how knowledge is stored, accessed, and exchanged across the globe. Each of these innovations altered the educational landscape, but none fundamentally challenged the very nature of human cognition in the way that artificial intelligence now does.
Generative Artificial Intelligence (AI) is not simply another technology. It represents a profound shift in the relationship between humans and knowledge itself. Unlike previous technologies that expanded human access to information, AI increasingly performs intellectual tasks that were once considered uniquely human. It can generate essays, summarize research papers, write software code, solve complex mathematical problems, create policy analyses, translate languages, produce artistic works, and engage in sophisticated dialogue within seconds. For the first time in history, humanity has developed machines capable of producing coherent knowledge artifacts rather than just storing or transmitting information.
This distinction is critical. Search engines such as Google revolutionized information retrieval by helping people locate existing knowledge. Generative AI goes a step further; it creates new content that resembles human reasoning, often making it difficult to distinguish between machine-generated and human-generated thought. While these systems do not “think” in the philosophical sense, their outputs increasingly simulate many aspects of human cognition, raising profound questions about learning, expertise, creativity, authorship, and intellectual development.
The implications for higher education are both extraordinary and unsettling. Universities have embraced technological innovations throughout their history because they enhanced teaching, research, and scholarly communication. Artificial intelligence undoubtedly offers similar opportunities. It can personalize learning, improve accessibility for students with diverse needs, automate routine administrative tasks, accelerate scientific discovery, support interdisciplinary research, and expand educational opportunities to previously underserved communities (UNESCO, 2023). Properly deployed, AI has the potential to become one of the most transformative educational tools ever created.
Yet beneath this optimism lies a far more fundamental challenge that has received comparatively little attention. The central question confronting higher education is no longer whether universities should adopt artificial intelligence; they inevitably have, should and will. Nor is the primary concern whether students will use AI to complete assignments. Those debates, while important, focus on symptoms rather than the underlying issue. The more profound question is this: what happens to human intellectual development when technologies increasingly perform the cognitive work that they were designed to cultivate?
This is not a question about technology. It is a question about the future purpose of higher education.
For centuries, universities have existed as institutions dedicated to cultivating minds rather than simply transmitting information. Their enduring value has never resided in possessing knowledge unavailable elsewhere. Universities flourished long before libraries became widespread because they nurtured habits of disciplined inquiry, logical reasoning, ethical reflection, and scholarly debate. As access to information expanded through books, journals, and later the internet, the university’s purpose did not diminish. Instead, its role became even more important: helping learners distinguish reliable knowledge from speculation, evidence from opinion, and wisdom from mere information.
Artificial intelligence now compels universities to revisit this foundational purpose.
The debate surrounding AI has, in many respects, been framed too narrowly. Discussions often centre on plagiarism detection, academic misconduct, assessment redesign, or the technical integration of AI tools into classrooms. While these are legitimate concerns, they overlook a more consequential risk: the gradual outsourcing of human cognition. If students increasingly rely on AI to analyse, synthesise, evaluate, and generate ideas, they may complete academic tasks more efficiently while simultaneously engaging less deeply in the intellectual processes through which expertise develops. The greatest threat posed by AI to higher education is therefore not academic dishonesty. It is the possibility that universities are inadvertently producing graduates who possess unprecedented access to knowledge yet lack the capacity to think independently about it.
This challenge extends far beyond the classroom. Around the world, democratic societies are confronting growing volumes of misinformation, algorithmic bias, synthetic media, and AI-generated content that blur the boundaries between truth and fabrication. Citizens are increasingly required to evaluate competing claims, verify sources, interpret evidence, and make informed judgments in environments characterised by uncertainty and information overload. In such a world, the competitive advantage of individuals and nations will not depend solely on access to artificial intelligence. It will depend on cultivating citizens capable of questioning artificial intelligence itself.
Consequently, the future of higher education should not be framed as a choice between embracing AI and protecting traditional educational practices. That is a false dichotomy. Artificial intelligence is now an irreversible feature of contemporary society and will continue transforming education, research, industry, healthcare, governance, and public life. The real challenge is ensuring that AI amplifies rather than replaces the uniquely human capacities upon which democratic societies, ethical leadership, scientific progress, and sustainable development ultimately depend.
Re-imagining critical thinking, therefore, is not an argument against artificial intelligence. It is the condition necessary for artificial intelligence to serve humanity responsibly.
The University Has Never Been About Information
To understand why re-imagining critical thinking has become such an urgent imperative, we must first revisit a more fundamental question that has quietly shaped higher education for centuries:What is a university actually for?
This question is surprisingly easy to overlook in an era obsessed with employability, technological disruption, university rankings, salaries, and digital transformation. Increasingly, higher education is evaluated through metrics that emphasise economic returns, research outputs, innovation ecosystems, and workforce readiness. These outcomes undoubtedly matter. Universities contribute significantly to national development, scientific discovery, technological innovation, and economic competitiveness. Yet reducing higher education to these functions alone risks overlooking its deeper intellectual and civic purpose.
The defining characteristic of a university has never been its ability to provide information. Information has always existed beyond university walls. Before the printing press, knowledge circulated through oral traditions, monasteries, and scholarly communities. The invention of print dramatically expanded access to learning. Public libraries further democratized knowledge. The internet subsequently made billions of pages of information accessible within seconds, fundamentally altering how societies learn. Search engines then organised this vast digital landscape, enabling individuals to retrieve information almost instantaneously.
If universities existed primarily to distribute information, they would have become obsolete long before the emergence of artificial intelligence.
Instead, universities endured because their purpose has always extended beyond information itself. Their enduring contribution lies in transforming information into understanding, understanding into judgment, and judgment into wise action. Universities cultivate the intellectual virtues that enable individuals not only to know more, but to think more carefully, question more deeply, and act more responsibly.
This philosophical understanding of higher education has deep historical roots. In The Idea of a University, first published in 1852, John Henry Newman argued that the purpose of a university is not simply vocational preparation or the accumulation of specialised knowledge, but the formation of an “enlargement of mind.” Newman believed that higher education develops the capacity to view knowledge as an interconnected whole, fostering intellectual independence, sound judgment, and disciplined reasoning rather than narrow technical competence (Newman, 1852/1982). For Newman, education was valuable because it cultivated habits of thought that enabled individuals to discern truth amid complexity.
Nearly a century later, the American philosopher and educational reformer John Dewey expanded this by arguing that education is fundamentally a process of reflective inquiry rather than passive knowledge transmission. Learning, he maintained, occurs when individuals confront genuine problems, investigate evidence, test ideas, and revise their understanding through experience. Schools and universities therefore fulfil their democratic mission by nurturing citizens capable of thoughtful deliberation rather than unquestioning acceptance of authority (Dewey, 1916). Dewey’s philosophy remains remarkably relevant in an age where AI can instantly generate persuasive answers. The educational challenge is no longer helping students find information; it is ensuring they possess the intellectual habits necessary to interrogate it.
Similarly, Brazilian educator Paulo Freire rejected what he famously described as the “banking model” of education, in which teachers deposit information into passive learners. Instead, Freire envisioned education as a dialogical process that develops critical consciousness, the capacity to question social realities, challenge assumptions, and participate actively in transforming society (Freire, 1970). Education, in Freire’s view, is not measured by how much information students can reproduce, but by whether they develop the courage and ability to think critically about the world they inhabit.
More recently, higher education scholar Ronald Barnett has argued that universities must prepare graduates not just for known professions but for an increasingly uncertain world characterised by complexity, ambiguity, and rapid change. Barnett suggests that higher education should cultivate what he calls an “ecology of knowing,” where learners develop criticality, resilience, ethical responsibility, and the capacity to navigate supercomplexity rather than simply master established bodies of knowledge (Barnett, 2000; Barnett, 2012). His work is particularly instructive for the AI era because artificial intelligence excels at reproducing existing knowledge but cannot independently and effectively cultivate the moral judgment, intellectual humility, and contextual understanding required for wise human action.
Therefore, Newman, Dewey, Freire, and Barnett articulate a remarkably consistent philosophy despite writing across different centuries and contexts. They remind us that universities exist to cultivate qualities of mind rather than just repositories of information. A true graduate is distinguished not by possessing more facts than others, but by demonstrating stronger judgment, greater curiosity, deeper reasoning, ethical discernment, intellectual courage, and a lifelong commitment to learning.
These qualities have become even more valuable in the age of artificial intelligence. Today, virtually every university student carries in their pocket access to more information than entire research libraries contained only a generation ago. Increasingly, they also carry access to systems capable of summarising, analysing, translating, coding, drafting, and explaining that information almost instantly. Information is no longer scarce; it is abundant. Artificial intelligence is making knowledge generation increasingly abundant as well. The scarcity now lies elsewhere. It lies in wisdom. It lies in discernment. It lies in ethical judgment. It lies in the capacity to determine which evidence deserves trust, which arguments withstand scrutiny, which technologies serve humanity, and which should be challenged.
That is why re-imagining critical thinking has become the defining mission of higher education in the age of artificial intelligence. Universities are no longer the world’s primary custodians of information. Their enduring purpose is far more ambitious. They are the institutions entrusted with cultivating the intellectual character necessary for societies to use information, and now artificial intelligence, wisely.
The future relevance of the university will therefore not be determined by whether it adopts AI more rapidly than other institutions. It will be determined by whether it remains the place where human judgment, ethical reasoning, curiosity, and critical inquiry are deliberately nurtured in a world increasingly capable of automating knowledge production itself.
Let me stop here for Part 1 of this series of articles on this topic; follow my next Part 2 soon, which will deep dive into how Artificial Intelligence Is Changing the Nature of Learning, Not Just the Tools of Learning.
Author
Dr. Gilbert A. Ang’ana is the Dean of School of Leadership, Business, and Technology at Pan Africa Christian (PAC) University whose research focus is on values-based and collaborative leadership and governance in Africa. He is a leadership, governance and policy consultant, teacher and coach and the Founder of Accent Leadership Group and Accent Global Initiative (Think Tank)
