Kia in the Classroom: The Economics of Boldness in Teaching with AI

 By Richard Sebaggala

On September 3, 2025, a lecture hall at Simon Fraser University will host a moment that feels closer to science fiction than to the routines of academic life. Students will gather expecting a professor at the podium, but instead will find two figures waiting. One is Steve DiPaola, a familiar human presence, and beside him is Kia, a three-dimensional artificial intelligence rendered with startling realism.

The digital figure meets the audience with a direct gaze, smiles at the right moment, and speaks in measured tones that carry the authority of an academic voice. The class is no longer a monologue delivered by a single lecturer but a dialogue between flesh and code, a human mind and its synthetic counterpart. For students who grew up with animated avatars and digital companions, Kia may not appear entirely alien. What makes the moment extraordinary is that it unfolds within a university classroom, one of the last places where knowledge has been carefully guarded by human authority.

The arrival of Kia is not a cautious step in educational technology but an unmistakable act of boldness. Around the world, universities hesitate to integrate AI openly, unsettled by fears of plagiarism, shallow assignments, or the erosion of genuine intellectual effort. DiPaola has chosen a different path. Rather than shielding his students from the technology, he has brought it into the centre of the classroom as a living demonstration. The decision transforms the lecture hall into a theatre of inquiry, where the question is not whether AI exists but whether it can belong at the core of teaching. Economists would call this innovation under criticism, a dynamic that has accompanied every major technological shift since the age of mechanization.

History shows that new tools are rarely welcomed without resistance. The typewriter was once distrusted, the calculator dismissed as the death of numeracy, and the personal computer regarded as a passing fad. Those who pressed forward despite the doubts gained more than a reputation for daring. They accumulated knowledge that others lacked, learning where the tools succeeded and where they fell short. Uncertainty became capital. DiPaola’s decision to place Kia in the classroom follows this tradition. This idea is far from new; economists have long studied the strategic role of bold moves. The concept of first‑mover advantage, for instance, frames how being first in a market can confer durable benefits such as reputational surplus, learning gains, and control over resourcesBy facing skepticism now, he accepts reputational risk in exchange for insight. That willingness to trade risk for knowledge is what allows innovation to move.

What makes Kia disquieting is not the information it can process but the social space it inhabits. It gestures, pauses, and responds with the timing of a colleague. Professor DiPaola himself has admitted that, despite decades of teaching and research, he occasionally finds Kia explaining certain concepts more clearly than he can. That admission resonates with many of us who have discovered that AI sometimes performs better at tasks we once considered our strengths. The unsettling question follows: is Kia a substitute for the professor or a complement to him? If it substitutes, it competes with the teacher, offering lectures without fatigue, explanations without limit, and perhaps even performance with greater flair. If it complements, it enlarges the professor’s presence, leaving him to design, mentor, and evaluate while the AI carries the weight of repetition and performance.

DiPaola insists on the latter. Kia will not design the syllabus or grade assignments. Its role is that of a partner in dialogue, a provocateur, an intellectual sparring figure. The authority remains firmly human, while the AI performs more like a chorus in ancient drama: commenting, provoking, and enriching, but never directing. Economists would recognize this as the difference between substitution and complementarity. Calculators did not erase the work of teaching mathematics; they moved it toward problem-solving. Online databases did not make librarians unnecessary; they turned them into navigators of vast digital landscapes. In the same way, Kia does not erase the professor but reshapes the meaning of teaching.

If this experiment works, the classroom becomes more productive. Students gain a source of explanation that does not tire, while lectures acquire immediacy and theatrical power. A professor’s energy is finite, but a digital persona can sustain attention endlessly. Economists call this capital deepening: the process by which new tools increase the return on human effort. Just as tractors increased the yield of farmers, systems like Kia could raise the intellectual return of every hour spent in teaching. Productivity in education cannot be reduced to exam scores alone. It is better measured in comprehension that lasts and insights that endure. By animating concepts in real time, Kia may heighten those outcomes beyond what conventional lectures achieve.

The further horizon is less certain but more provocative. Other educators may attempt their own digital partners: an AI Socrates in philosophy, an AI judge in law, an AI diplomat in international relations. Universities may then institutionalize these figures, treating them as distinguishing assets, just as libraries or laboratories once defined reputation. “Come study with Professor X and EconAI” could become a marketing pitch. With time, the border between teaching and performance may fade. Lectures could evolve into choreographed dialogues where human and artificial voices weave together, and students may come to expect a form of intellectual theatre. The professor’s role would then shift decisively to mentorship, ethical judgment, and the cultivation of wisdom, qualities that resist automation.

The greater risk lies not in adopting such tools too early but in refusing them altogether. Universities that avoid experiments like Kia risk producing graduates unprepared for a world where artificial intelligence is embedded in every profession. Avoidance may seem prudent, yet it carries its own danger: irrelevance. The opportunity cost of inaction is high, which is what makes DiPaola’s decision consequential. By accepting visible dangers such as criticism, failure, or embarrassment, he seeks to prevent the greater invisible danger of an institution unprepared for its future.

The introduction of Kia will not end the debate about AI in education. Critics will argue that it reduces teaching to spectacle and weakens the authenticity of intellectual exchange. Supporters will answer that it enriches learning and mirrors the environment students will encounter in their lives and work. Both positions have weight, but what is certain is that the demonstration will alter the conversation. For the first time, a digital persona will stand on equal footing with a professor in a lecture, and the world will be forced to ask what that means.

The essential question is not whether Kia will surpass the professor but whether educators and universities are willing to design a partnership between human insight and artificial presence. History suggests that institutions willing to take that risk, to transform criticism into knowledge, are the ones that shape the trajectory of change. When Kia begins to speak before students, the trial will not only measure the capacity of an AI system. It will measure the courage of higher education itself.

From Scarcity to Abundance: Will Universities Survive the Age of AI?

By Richard Sebaggala

For centuries, higher education benefited from the scarcity of knowledge. Universities held the key to specialised information, and society paid a high price for the degrees and expertise that only these institutions could provide. Professors were the guardians of wisdom, lecture theatres the places where it was passed on, and libraries the guarded vaults of human progress. From an economic perspective, this was a textbook case of supply and demand: the supply of advanced knowledge was low, the demand from individuals and employers was high, and universities could command both prestige and price. Degrees acted as economic signals for scarce intellectual capital. This monopoly has disappeared. Artificial intelligence now produces literature reviews in seconds, explains complex theories on demand, and even designs experiments or business strategies that used to be hidden in the minds of experts. The supply curve of knowledge has shifted dramatically outwards, reducing scarcity and lowering the “price” of access to information to almost zero. Knowledge is no longer scarce. What is scarce is the ability to integrate, apply, and scrutinise AI-produced knowledge. In economic terms, the new scarce commodity is interpretability;  the human ability to assess, contextualise and create value from a wealth of data. The survival of universities will depend not on guarding data, but on how well they manage to integrate AI into teaching, research, and public engagement;  and that means faculty must lead the way.

 

Globally, the gap between student adoption and institutional readiness is widening. Nearly 80% of students are already using generative AI, but most have no structured support from their universities. Every semester without faculty readiness compounds what education strategist Dr Aviva Legatt calls “pedagogical infrastructure debt.” In economics, this resembles a rising cost curve: the longer an institution delays investing in AI capabilities, the higher the future cost of catching up, both financially and in terms of lost market share. We've seen this before. Learning management systems were universally used, but were mainly for administration rather than changing pedagogy. MOOCs promised democratic access but often delivered little more than repackaged lectures with low completion rates. In both cases, the opportunity costs were high, as universities gained efficiency but lost innovation and competitive differentiation to external platforms. There is much more at stake with AI. This is not just about content delivery, but also about how the next generation thinks, decides, and solves problems, and whether universities can maintain their comparative advantage in training graduates who offer unique value in a labour market transformed by automation.

 

While many leaders in higher education remain cautious or indifferent, it's a different story at some universities in Uganda. At Victoria University, Vice-Chancellor Dr Lawrence Muganga urges students to embrace AI rather than fear it. He warns that by 2030, many tasks that humans are trained to do today will be replaced by machines, and the most foolish advice would be to advise students to avoid AI. Under his leadership, the university has made AI literacy mandatory, set up a state-of-the-art AI lab, and started developing localised AI tools for the African context. Muganga’s approach treats AI not as a threat, but as a foundation for employability, entrepreneurship, and innovation—a practical example of the faculty-driven integration that Legatt believes is essential. In economic terms, this is a case of strategic first-mover advantage: by investing early in AI capabilities, Victoria University sets its 'product' (the graduates) apart from the competition in a competitive education market and potentially increases its value in the labour market.

 

The economic significance could not be clearer. McKinsey estimates that AI could add up to $23 trillion a year to the global economy by 2040, with the biggest gains going to countries and sectors that can reskill quickly. For Africa, where youth unemployment is high, integrating AI under the guidance of educators is not an option, but a competitive strategy. From a labour economics perspective, AI skills represent a form of human capital that yields high returns in terms of productivity and employability. From a macroeconomic perspective, widespread AI skills could shift a country’s production capabilities outwards so that more can be produced with the same input. It can bridge the employability gap, stimulate local innovation, and ensure that AI tools reflect local languages, cultures, and realities, rather than importing solutions that don't fit.

 

The era of knowledge scarcity is over, and universities that cling to their old role as gatekeepers will be left behind by alternative providers and self-taught, AI-powered learners. Classical economics teaches that scarcity determines value. Higher education once had a price because it controlled access to a limited resource. Now that AI has flattened the supply curve of information, the equilibrium point has shifted. The price, in this case, the willingness to pay for traditional knowledge transfer, will fall unless universities offer something that the market still values. That “something” is the ability to produce graduates who can create and apply new knowledge in a way that AI cannot. The advantage no longer lies in possessing the knowledge, but in the ability to interpret, apply, and gain insights that AI alone cannot deliver. In other words, the comparative advantage of universities must now lie in fostering the scarce capacity of human judgement in abundance. Faculties are the critical link that enables universities to move from monopolists in a scarce market to innovators in an abundant market. Globally, the warning signs are clear; locally, leaders like Muganga are proving what is possible. The question is whether others will follow before the opportunity passes.

What the Calculator Panic of the 1980s Can Teach Us About AI Today

By Richard Sebaggala

In 1986, a group of American math teachers took to the streets holding signs that read, “Ban Calculators in Classrooms.” They feared that these small electronic devices would strip students of the ability to perform basic calculations. If a machine could handle addition, subtraction, multiplication, and division, what incentive would students have to learn those skills at all? At the time, the concern felt genuine and even reasonable.

With the benefit of hindsight, the story unfolded quite differently. Within a decade, calculators were not only accepted but actively encouraged in classrooms across many countries. Standardized exams began permitting their use, textbook problems were redesigned to incorporate them, and teachers found that students could tackle more complex, multi-step problems once freed from the grind of manual computation. Far from destroying mathematical thinking, calculators shifted the focus toward problem-solving, modeling, and a deeper grasp of underlying concepts.

 

Almost forty years later, the same conversation is happening, but the technology has changed. Artificial intelligence tools such as ChatGPT, Avidnote, and Gemini can now generate essays, solve problems, and summarize complex ideas in seconds. Today's concern is familiar: that students will stop thinking for themselves because the machine can do the thinking for them. The parallel with the calculator debate is striking. In the 1980s, the worry was that calculators would erase basic arithmetic skills; today, it is that AI will erode the capacity for critical and independent thought. In both cases, the tool itself is not the real problem. What matters is how it is introduced, how it is used, and how deeply it is woven into the learning process.

In economics, this recurring pattern is well understood through the study of general-purpose technologies, which are transformations such as electricity, the internet, and now AI, whose applications cut across multiple industries and fundamentally alter productivity potential. History shows that these technologies almost always meet initial resistance because they unsettle existing skills, workflows, and even the identities of entire professions. Yet, once institutions adjust and complementary innovations emerge, such as new teaching methods, updated regulations, or redesigned curricula, the long-run productivity gains become undeniable. In Africa, the mobile phone offers a clear example. Initially dismissed as a luxury, it became a platform for innovations like mobile money, which transformed financial inclusion, market access, and small business operations across the continent. The calculator did not diminish mathematical thinking; it reshaped it, shifting effort from mechanical tasks to higher-order reasoning. AI holds the same potential, but only if education systems are willing to reimagine how learning is structured around it.

 

When calculators entered the classroom, they prompted a shift in teaching and assessment. Teachers began creating problems where the calculator was useful, but understanding was still essential. Tests required not only the correct answer but also evidence of the reasoning behind it. The arrival of AI demands a similar change. Students can be taught to use AI for tasks such as brainstorming, structuring arguments, or refining drafts, but they should still be held accountable for evaluating and improving the output. Assessments can reward transparency in how AI is used and the quality of judgment applied to its suggestions.

This is where metacognition becomes essential. Metacognition is simply thinking about one's own thinking. In economics, we often speak of comparative advantage: doing what you do best while letting others handle the rest. AI shifts the boundaries of that calculation. The risk is that by outsourcing too much of our cognitive work, we weaken the very skills we need to make sense of the world. If universities fail to train students to integrate AI into their own reasoning, graduates may not only face economic disadvantages but may also experience a deeper sense of psychological displacement, feeling out of place in settings where AI competence is assumed.

Metacognition keeps us in control. It allows us to question the assumptions behind AI-generated answers, spot gaps in reasoning, align outputs with our goals, and know when to override automation in favor of deeper understanding. It is like applying the economist’s habit of examining incentives, not to markets, but to our own minds and to the machine’s mind.

Consider two graduate research students assigned to write a literature review. Both have access to the same AI tools. The first pastes the topic into the system, accepts the generated text without question, and drops it straight into the draft. The result is neat and coherent, with plenty of references, but some of the citations are fabricated, important regional studies are missing, and the structure is generic. Because the student never interrogates the output, the gaps remain. The supervisor flags the work as shallow and overly dependent on AI.

The second student uses AI to produce an initial outline and a list of possible sources. They then ask the tool follow-up questions: "What is this evidence based on? Are there African studies on the subject? Which perspectives might be missing?" They verify each reference, read key sources, and restructure the review to balance global theory with local findings. The final paper is richer, more original, and meets the highest academic standards. The difference lies in metacognition, not only thinking about one's own reasoning but also critically evaluating the machine's reasoning. Over time, this approach strengthens analytical skills and turns AI into a genuine thinking partner rather than a shortcut.

The real opportunity is to treat AI as a thinking accelerator. It can take over repetitive work like drafting, summarizing, and running quick computations so that human effort can be directed toward framing the right questions, challenging assumptions, and making judgments that depend on values and context. History shows that those who learn to work with transformative tools, rather than resist them, gain the advantage. The calculator era offers a clear lesson for our time: instead of banning the tool and sometimes focusing on who has used it or not, we should teach the skill of using it wisely and thinking about our thinking while we do so.

"You're Safe!": What This Joke Really Says About AI and the Future of Education

By Richard Sebaggala

Conversations about AI have become increasingly divided. Some see it as a breakthrough that will transform every sector, education included. Others still treat it as overblown or irrelevant to their day-to-day work. Most people are simply exhausted by the constant updates, ethical dilemmas, and uncertainty. This split has left many universities stuck, circling around the topic without moving forward in any meaningful way.

A recent WhatsApp exchange I saw was both humorous and unsettling: "Artificial intelligence cannot take your job if your job has never needed intelligence." The reply was, "I don't understand..." and the answer came back, "You're safe!" The joke's quiet truth is that if your work relies on knowledge, judgment, and problem-solving, then AI is already capable of doing parts of it. And the parts it replaces may be the very ones that once gave your job value.

For many of us, including lecturers, researchers, and analysts, our core productivity has come from how efficiently we produce or communicate knowledge. But AI is changing the way that knowledge is generated and shared. Tasks like reviewing literature, coding data, summarizing papers, and grading assignments are no longer things only humans can do. Tools like Elicit, Avidnote ai, and GPT-based platforms now handle many of these tasks faster, and in some cases, better.

Some universities are already moving ahead. Arizona State University has partnered with OpenAI to embed ChatGPT into coursework, research, and even administrative work. The University of Helsinki’s "Elements of AI" course has attracted learners from around the world and built a new foundation for digital literacy. These aren't theoretical exercises; they're practical steps that show what's possible when institutions stop hesitating.

I’ve seen individual lecturers using ChatGPT and Avidnote to draft student feedback, which frees up time for more direct engagement. Others are introducing AI tools like Perplexity and avidnote to help students refine their research questions and build better arguments. These are not just efficiency hacks; they’re shifts in how academic work is done.

Yet many universities remain stuck in observation mode. Meanwhile, the labour market is already changing. Companies like Klarna and IBM have openly said that AI is helping them reduce staffing costs. When AI can write reports, summarise meetings, or process data in seconds, the demand for certain types of graduate jobs will shrink. If universities fail to update what they offer, the value of a degree may start to fall. We're already seeing signs of a skills revaluation in the market.

This shift isn’t without complications. AI also brings new problems that institutions can’t ignore. Equity is one of them. Access to reliable AI tools and internet connections is far from universal. If only well-funded institutions can afford high-quality access and training, the digital divide will only widen. Universities need to think about how they support all learners, not just the privileged few.

There’s also the question of academic integrity. If students can complete assignments using generative AI, then we need to rethink how we assess learning. What kinds of skills are we really measuring? It’s time to move away from assignments that test simple recall and toward those that build judgment, ethical reasoning, and the ability to engage with complexity.

Data privacy matters too. Many AI platforms store and learn from user input. That means student data could be exposed if universities aren’t careful. Before rolling out AI tools at scale, institutions need clear, transparent policies for how data is collected, stored, and protected.

And then there’s bias. AI tools reflect the data they’re trained on, and that data often carries hidden assumptions. Without proper understanding, students may mistake bias for truth. Educators have a role to play in teaching not just how to use these tools, but how to question them.

These are serious concerns, but they are not reasons to stall. They are reasons to move forward thoughtfully. Just as we had to learn how to teach with the internet and digital platforms, we now need to learn how to teach with AI. Delaying action only increases the cost of catching up later.

What matters most now is how we prepare students for the labour market they’re entering. The safest jobs will be those that rely on adaptability, creativity, and ethical thinking traits that are harder to automate. Routine tasks will become commodities. What will set graduates apart is their ability to ask good questions, work across disciplines, and collaborate effectively with technology.

These changes are no longer hypothetical. They’re happening. Institutions that embrace this moment will continue to be relevant. Those that don’t may struggle to recover their footing when the changes become impossible to ignore.

Universities must lead, not lag. The time for think pieces and committee formation has passed. We need curriculum updates, collaborative investment in training, and national plans that ensure no institution is left behind. The early adopters will shape the new rules. Everyone else will follow or be left out.

That WhatsApp joke made us laugh, but its warning was real. AI is changing how the world defines intelligence and value. If education wants to stay meaningful, it has to change with it. We cannot afford to wait.