Category AI

By Dr. Gilbert A. Ang’ana

Did you miss the initial parts of this article series? Find Part 1 here, Part 2 here, and Part 3 here. This article is Part 4 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.

Universities Must Rethink Assessment Before Artificial Intelligence Redefines Learning

If artificial intelligence has exposed one major weakness within higher education, it is not the curriculum. It is assessment.

For generations, universities have relied on essays, examinations, research papers, laboratory reports, and dissertations as primary indicators of student learning. These assessment methods evolved within a world where demonstrating knowledge required students to independently locate information, organise arguments, synthesise evidence, and communicate their reasoning. The finished assignment served as a reasonably reliable proxy for the intellectual processes that produced it. Although imperfect, these approaches reflected an educational environment in which access to information was limited and the production of coherent academic work required substantial individual cognitive effort.

Artificial intelligence has fundamentally changed this assumption. Today, many conventional university assessments can be completed, at least in draft form, within minutes using generative AI. Essays can be generated instantly. Literature reviews can be synthesised. Computer code can be produced with remarkable speed and accuracy. Statistical analyses can be interpreted by AI-assisted software. Even policy briefs, reflective summaries, and research proposals can be drafted with increasing precision. Consequently, the educational challenge is no longer whether students possess the technical ability to use AI. That reality is already here. The challenge is whether existing assessment models continue to measure the intellectual capabilities universities claim to develop.

This moment requires universities to confront an uncomfortable but necessary truth. The AI revolution has not rendered higher education obsolete; it has rendered many traditional assessment practices insufficient.

The response should not be a return to handwritten examinations or blanket prohibitions on AI. Such measures may offer temporary reassurance but fail to address the deeper issue. Artificial intelligence is now part of professional practice across law, medicine, engineering, education, business, journalism, architecture, public administration, and scientific research. Universities should therefore prepare students to work responsibly with AI rather than pretend it does not exist. The central policy challenge is designing assessments in which the value lies not in producing answers but in demonstrating judgment, reasoning, creativity, ethical reflection, and contextual understanding.

Educational research has long argued that assessment shapes learning more powerfully than curriculum itself. John Biggs’ theory of constructive alignment emphasises that students organise their learning around what is assessed rather than what is merely taught (Biggs & Tang, 2011). If assessment rewards memorisation, students memorise. If it rewards reproduction, students reproduce. If it rewards critical inquiry, collaboration, reflection, and innovation, students gradually develop those capabilities. Assessment therefore sends a powerful message about what universities truly value. Artificial intelligence makes this insight more important than ever.

Rather than asking whether AI should be permitted in assessment, universities should begin by asking a more fundamental question: What forms of assessment best reveal human thinking in an AI-mediated world?

The answer lies in shifting from assessing products to assessing intellectual processes. One promising approach is the expanded use of oral examinations and oral defences. Long established within doctoral education, oral assessment requires students to explain, justify, defend, and extend their reasoning in real time. It becomes immediately evident whether learners genuinely understand the arguments they present, can respond to unfamiliar questions, and can integrate evidence under conditions of uncertainty. Artificial intelligence may assist students in preparation, but it cannot substitute for authentic intellectual engagement during scholarly dialogue. Research consistently suggests that oral assessment enhances conceptual understanding, communication skills, and higher-order reasoning while reducing opportunities for academic misconduct (Joughin, 2010).

Similarly, authentic assessment offers an increasingly relevant framework for AI-era universities. Authentic assessments require students to apply knowledge within realistic professional contexts rather than reproduce information in artificial academic settings (Villarroel et al., 2018). Instead of writing generic essays, students might develop public policy recommendations for local governments, design engineering solutions for community challenges, create business innovation strategies for emerging enterprises, prepare environmental impact assessments, or analyse complex healthcare cases. Such assessments value the quality of reasoning, stakeholder engagement, ethical decision-making, and contextual application over the production of text alone.

Policy simulations and scenario-based learning similarly cultivate capacities that artificial intelligence cannot independently replicate. Students confronted with evolving crises, whether climate emergencies, constitutional disputes, organisational failures, public health outbreaks, or diplomatic negotiations, must make decisions using incomplete information, competing priorities, and uncertain outcomes. These environments require judgment rather than information retrieval. They reveal how learners think rather than merely what they know. Higher education should also increasingly embrace live problem-solving assessments. These require students to analyse unfamiliar problems under supervised conditions, articulate their reasoning aloud, collaborate with peers, and adapt their thinking as new evidence emerges. Such assessments reflect the realities of contemporary professional practice, where success depends less on recalling predetermined answers than on responding thoughtfully to novel situations.

Another promising direction involves expanding the use of capstone projects that integrate knowledge across disciplines while addressing genuine societal challenges. Capstone experiences require sustained inquiry, collaboration, stakeholder engagement, iterative learning, and reflective practice over extended periods. Because they unfold through multiple stages involving supervision, peer feedback, field engagement, and revision, they make the learning journey itself visible rather than focusing exclusively on the final product. Portfolio assessment similarly deserves renewed attention. Rather than evaluating isolated assignments, portfolios document students’ intellectual growth over time through collections of projects, reflections, presentations, research outputs, creative work, and evidence of professional development. Properly designed portfolios encourage learners to demonstrate progression, integrate feedback, and critically reflect upon their own development. They also mirror lifelong learning practices increasingly expected within rapidly evolving professions.

One of the most underutilised yet powerful assessment approaches remains the reflective journal. Reflection is frequently misunderstood as a personal narrative rather than a rigorous intellectual exercise. In reality, structured reflective practice requires learners to evaluate decisions, analyse assumptions, identify limitations, integrate theory with experience, and plan future improvement. Donald Schön’s work on the reflective practitioner demonstrated decades ago that professional expertise develops not merely through experience itself but through disciplined reflection upon experience (Schön, 1983). In the AI era, reflective writing becomes even more valuable because it reveals how students interpret, question, and learn from interactions with intelligent technologies. Universities should also expand community-engaged learning and project-based assessment. Working with local communities, government agencies, civil society organisations, schools, hospitals, industries, and entrepreneurs exposes students to complex realities where problems rarely have single correct answers. Community-based assessment requires empathy, collaboration, ethical reasoning, contextual awareness, and adaptive problem-solving, qualities that remain deeply human despite rapid technological advancement.

Equally important is the growing need for interdisciplinary challenge-based learning. Artificial intelligence increasingly operates across disciplinary boundaries, affecting law, medicine, engineering, agriculture, education, economics, environmental science, theology, and public policy simultaneously. Universities should therefore create opportunities for students from different disciplines to collaborate on solving complex societal challenges. Such experiences cultivate systems thinking, communication, negotiation, and intellectual flexibility, competencies consistently identified by the OECD, UNESCO, and the World Economic Forum as essential for future graduates.

These recommendations do not imply abandoning essays altogether. Writing remains one of the most powerful vehicles for developing critical thought. Rather, essays should increasingly become one component within richer ecosystems of assessment that also evaluate oral reasoning, collaborative inquiry, ethical judgment, creativity, reflection, and practical problem-solving. The objective is not to eliminate traditional assessment but to rebalance it toward forms of evidence that more accurately capture human intellectual capability in an AI-enabled world.

The universities that thrive in the coming decades will not be those that most effectively police artificial intelligence. They will be those that most effectively redesign assessment around the capacities artificial intelligence cannot replace.

Ultimately, assessment reform is not about preserving academic integrity alone. It is about preserving intellectual integrity, the commitment to ensuring that every graduate has genuinely developed the habits of mind that higher education exists to cultivate.

Faculty Need AI Pedagogical Literacy, Not Just Digital Literacy

Artificial intelligence has generated widespread speculation about the future of academic work. Headlines frequently ask whether AI will replace lecturers, automate teaching, or render universities obsolete. Such questions are understandable but fundamentally misplaced. Throughout history, transformative technologies have consistently altered professional roles without eliminating the need for human expertise. Artificial intelligence is unlikely to replace university educators. It is, however, profoundly changing what it means to be one.

The future of higher education will depend less on whether lecturers can use artificial intelligence and more on whether they can teach students how to think critically in partnership with it.

This distinction marks one of the most important yet least discussed dimensions of AI policy in higher education. Across many universities, considerable attention has been devoted to helping faculty become digitally competent. Workshops increasingly introduce lecturers to generative AI platforms, automated marking tools, adaptive learning systems, AI-assisted research software, and digital content creation. While these initiatives are valuable, digital competence alone is insufficient. Knowing how to operate AI technologies is fundamentally different from understanding how AI reshapes learning, cognition, assessment, ethics, and intellectual development.

Universities therefore need to move beyond digital literacy towards what might be termed AI pedagogical literacy.

AI pedagogical literacy refers to educators’ capacity to integrate artificial intelligence into teaching in ways that strengthen rather than weaken learning. It requires understanding not only how AI functions technically but also how it influences cognitive development, student motivation, assessment validity, disciplinary knowledge, ethical reasoning, and academic integrity. Most importantly, it requires lecturers to redesign educational experiences so that AI becomes a catalyst for deeper inquiry rather than a shortcut around intellectual effort. This transformation fundamentally redefines the educator’s role. For centuries, lecturers have been regarded primarily as transmitters of disciplinary knowledge. Although this conception has long been challenged by contemporary educational theory, remnants of the transmission model remain evident within many university classrooms. Artificial intelligence accelerates the decline of this traditional role because students can now access information, summaries, examples, and even personalised tutoring almost instantly through intelligent systems. Information delivery is no longer the distinctive contribution of the university lecturer.

The educator’s comparative advantage now lies elsewhere. Lecturers become architects of learning rather than distributors of information. They become designers of intellectual experiences. Facilitators of inquiry. Mentors of critical reasoning. Cultivators of ethical judgment and developers of professional identity.

These responsibilities cannot be automated because they depend upon relational engagement, disciplinary wisdom, contextual understanding, and moral leadership. This shift also requires educators to model critical engagement with AI itself. Students should observe lecturers questioning AI-generated outputs, identifying inaccuracies, recognising algorithmic bias, evaluating evidence, acknowledging uncertainty, and demonstrating intellectual humility. In doing so, faculty communicate an essential lesson: artificial intelligence is an extraordinarily powerful cognitive partner, but it is not an unquestionable authority.

Equally important is the need for educators to explicitly teach students how to use AI responsibly. Universities should no longer assume that digital natives automatically possess AI literacy. In reality, many students know how to generate outputs but lack understanding of prompt design, verification strategies, source evaluation, ethical disclosure, bias detection, privacy implications, and responsible academic use. Faculty therefore become guides in helping students distinguish between productive AI collaboration and passive dependence. Professional development consequently becomes a strategic institutional priority rather than an optional activity. Universities should invest in sustained programmes enabling lecturers to understand cognitive science, assessment redesign, AI ethics, disciplinary applications of generative AI, learning analytics, and evidence-based pedagogical innovation. One-off workshops introducing AI tools will not be sufficient. Faculty require continuous opportunities to experiment, reflect, collaborate, and evaluate emerging educational practices within their own disciplines.

Institutional leadership has a corresponding responsibility. Universities should establish communities of practice where educators share successful AI-enabled teaching approaches, discuss ethical dilemmas, evaluate new technologies, and collectively develop institutional standards. Such collaborative learning cultures reduce uncertainty while encouraging innovation grounded in educational research rather than technological enthusiasm alone. AI pedagogical literacy must also become a component of academic quality assurance. Teaching excellence frameworks should increasingly recognise educators who successfully integrate AI while strengthening higher-order thinking, reflective practice, interdisciplinary collaboration, and authentic learning. Teaching awards, curriculum review processes, and educational leadership programmes should all acknowledge the growing importance of AI-informed pedagogy. Perhaps most important, faculty themselves must embrace a renewed understanding of expertise. In an era where students can instantly retrieve information, academic authority no longer rests primarily upon possessing knowledge unavailable elsewhere. Instead, authority increasingly derives from helping learners make sense of complexity, navigate ambiguity, exercise ethical judgment, connect theory with practice, and cultivate intellectual character.

Artificial intelligence therefore does not diminish the importance of university educators. It elevates it. The lecturer of the future will not be defined by the quantity of information they can deliver more effectively than an algorithm. They will be distinguished by their capacity to cultivate the qualities that no algorithm can teach independently: wisdom, discernment, curiosity, courage, integrity, empathy, intellectual humility, and the disciplined habit of critical inquiry. The universities that flourish in the age of artificial intelligence will not simply invest in smarter technologies. They will invest in smarter pedagogies and in educators capable of ensuring that every technological advance is matched by an equally ambitious investment in human intellectual development.

For if artificial intelligence is transforming the future of learning, it is the university lecturer who must ensure that learning remains profoundly and unapologetically human.

Thank you for following this series of articles in this critical conversation to here. The last bonus part 5 of the article, I will be sharing “Eight Policy Priorities for Re-imagining Critical Thinking in the Age of Artificial Intelligence”

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)

Why Re-imagining Critical Thinking Matters Even More for Africa

By Dr. Gilbert A. Ang’ana

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 Kenya National 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)