Universities are Dinosaurs?
How Higher Education Risks Extinction in the Age of AI — And How We Can Evolve.
“The real task of education is not to fill minds with facts, but to light a fire of inquiry.” — Socrates (attributed)
In every great extinction event, the warning signs were there — just ignored.
Today, universities might face their own KT extinction moment. The impactor is not a rock from space, but a technology: artificial intelligence. And unless higher education evolves rapidly, it risks going the way of the dinosaurs — slow, proud, and extinct.
This metaphor struck me back when I was studying for my MSc in Plant Sciences. I was learning about Rubisco — the most abundant protein on Earth, essential to photosynthesis, present in every green plant cell. Yet despite its abundance, Rubisco is remarkably inefficient. Evolution kept Rubisco around not because it was the best solution, but because it was good enough for the stable environments plants inhabited. Nature tolerates suboptimal performance when the pace of change is slow and the pressure to adapt is low.
Today’s universities face a very different reality. Unlike Rubisco, they are being thrust into a rapidly shifting environment - one where "good enough" is no longer enough. Abundant, vital, but slow to evolve, they now stand at a tipping point where adaptation is not optional - it is existential.
In every technological revolution, there are missteps.
The printing press spread both wisdom and propaganda.
The internet democratized knowledge — and amplified division.
And today, as artificial intelligence integrates into higher education, we are once again witnessing the double-edged nature of innovation.
The truth is, we won’t fail because AI is too smart. We will fail because we weren’t wise enough.
Last month, I was invited to San Salvador, where I gave a series of talks and workshops on the integration of AI into higher education. Hosted by the Honorary Consulate of El Salvador in Israel, I had the opportunity to engage with university leaders, faculty, students, and policymakers.
It was the students’ questions — sharp, courageous, and future-facing — that encouraged me to continue my personal exploration of AI’s role in reshaping higher education.
Universities, under pressure to modernize, rushed to deploy AI: automated grading, predictive admissions algorithms, AI-powered proctoring systems. But instead of deepening learning, many of these early experiments exposed and amplified the systemic weaknesses already festering inside academia: bias, surveillance, inequality, a shallow understanding of human development.
As historian Yuval Noah Harari reminds us,
“Technology is never deterministic. Its impact depends on how we use it.”
The early failures of AI in universities are not technical glitches — they are philosophical failures. They reveal how little thought many institutions gave to fairness, trust, creativity, and human dignity before jumping into the future.
If education is truly about cultivating human potential, then the first wave of AI integration is a stark reminder of how easily that mission can be undermined — when tools are adopted without rethinking the deeper purpose of learning itself.
Before we look forward, we must first understand how and where things went wrong.
Rethinking Deployment: A New Ethical Model for AI in Higher Education
Before rushing to integrate AI technologies into educational ecosystems, institutions must pause and rethink the fundamental process. AI is not a plug-and-play solution; it is a systemic intervention that shapes not just how we teach, but who we become as a learning society.
A simple but powerful model can guide universities through this transformation:
“You want to deploy AI in education?” Start here: Ethics → Transparency → Co-Design with Students. If not? STOP. Rethink.”
Ethics First:
Every AI deployment must begin with a robust ethical framework. Universities must articulate clear principles: fairness, inclusivity, privacy, dignity, and human flourishing. Without this foundation, even the most sophisticated technologies risk perpetuating harm or exacerbating inequalities.
Transparency Always:
Students, faculty, and stakeholders must know how AI systems function, what data they collect, how decisions are made, and how accountability is ensured. Transparency transforms AI from a black box into a shared tool of trust and learning.
Co-Design with Students:
Education is not a service delivered to passive recipients; it is a collaborative ecosystem. Students must not merely be subjects of AI experimentation-they must be co-creators. Participatory design processes that involve learners in shaping AI tools lead to higher relevance, acceptance, and alignment with educational values.
If these steps cannot be fulfilled, the message is clear:
“STOP. Rethink.”
Deploying AI without an ethical and participatory foundation is not modernization-it is institutional negligence. Universities that aspire to thrive in the next era must not only innovate faster; they must innovate wiser.
Case Studies: Where It Went Wrong
Before we can dream boldly of the future, we must first reckon honestly with the present.
The early waves of AI integration into higher education have revealed a brutal and uncomfortable truth: without ethics, foresight, and humility, innovation can do more harm than good.
Like the dinosaurs facing a sudden extinction event, universities that move too slowly-or that remain blind to their own structural biases-are not just lagging behind. They are positioning themselves for extinction.
The collapse will not come because AI is too intelligent. It will come because institutions failed to ask the right questions when it mattered most.
In 2020, the UK’s Ofqual agency deployed an algorithm to predict final exam grades during COVID-19 disruptions (). Intended to bring fairness and objectivity, it instead downgraded thousands of students from disadvantaged schools while protecting those from elite institutions. Public outrage forced the government to abandon the system. The lesson was stark: AI does not remove human bias; it mirrors and magnifies it unless explicitly designed otherwise. An algorithm is only as fair as its inputs.
Around the same time, universities scrambling to preserve academic integrity during remote learning turned to AI-driven proctoring tools like Proctorio and ExamSoft (). These systems, designed to monitor keystrokes, eye movements, and ambient noise, unleashed a wave of protests from students and faculty alike. Privacy was violated. Racial and socioeconomic biases were exposed. Anxiety skyrocketed. The backlash revealed a fundamental principle: you cannot teach trust through surveillance. Education rooted in suspicion breeds resentment, not resilience.
Even in less visible arenas, cracks were showing. Studies from MIT demonstrated that AI essay graders, adopted by several U.S. school districts, could be easily fooled by students using complex vocabulary and formulaic structures-even if their arguments were incoherent (MIT Technology Review, 2019). The machine rewarded style over substance. The lesson was simple but profound: AI cannot measure depth. Only humans can nurture true understanding.
In the admissions process, where the stakes are even higher, some universities began experimenting with predictive algorithms to aid in selection (Reuters, 2020). These tools promised objectivity but instead encoded and perpetuated historical injustices related to race, income, and geography. By training on flawed historical data, AI systems risked cementing exclusion rather than dismantling it. The truth became undeniable: if you train on injustice, you replicate injustice.
And finally, in an act of institutional panic, many universities and school districts moved to ban ChatGPT and other generative AI tools in 2023 (). They sought to prevent cheating, but only succeeded in driving innovation underground. Students adapted anyway, learning faster and more creatively outside official channels. The real failure was not technical, but pedagogical: banning innovation does not stop it. It merely disconnects institutions from the future.
Across all these cases, one pattern is clear. Technology does not save institutions from their own shortcomings. Values must come first. When machines decide, humans must first decide the values. And when they fail to do so, the cost is measured not in broken systems, but in broken trust-and broken futures. For readers wanting to dive deeper, detailed case reports on the and the highlight the complexities and consequences of these early missteps.
Universities stand today at a crossroads.
They can either learn from these early failures, embracing ethics, transparency, and co-creation as their foundation-or they can continue along the slow, blind path toward irrelevance.
The choice is urgent. The clock is ticking.
Learning from Failure
Before AI can truly transform higher education for the better, we must confront its early missteps with clarity and courage. Across campuses and institutions globally, rushed deployments, ethical blind spots, and overreliance on automated systems revealed the deep gap between technological capability and educational wisdom.
Below is a timeline of key incidents from 2020 to 2023 — a living map of cautionary tales. Each case highlights what happens when innovation outpaces foresight — and why building ethical, human-centered AI systems is not optional, but essential.
An overview of key incidents where AI integration backfired, highlighting lessons learned for future educational resilience.
Root Cause Analysis: Why We Failed
If artificial intelligence is a mirror, then its early use in higher education reflected something far more unsettling than technical immaturity. It revealed deep, systemic fractures — fractures that threaten the very soul of education if left unresolved.
When universities first welcomed AI onto their campuses, they treated it as a tool for efficiency rather than a catalyst for reimagining learning. Automation was applied to grading, surveillance, and administration, but rarely to the deeper mission of cultivating wisdom, creativity, or ethical courage. AI became a shortcut — a way to process students faster, not to help them grow deeper. As scholar Ruha Benjamin warned, “When we outsource responsibility to algorithms, we also automate injustice.” Instead of liberating education, technology risked entrenching its oldest biases in new digital forms.
In the rush to modernize, universities optimized what they could easily measure: grades, attendance records, time-on-task. Yet education’s most profound outcomes — empathy, ethical reasoning, original thought — defy neat quantification. Data dashboards could not capture the messy, relational work of becoming human. Projects like have long demonstrated that creativity, curiosity, and moral reasoning can be nurtured and assessed, but they require different methods: portfolios, reflections, conversations. In mistaking what was measurable for what was meaningful, institutions hollowed out the purpose of learning itself.
The adoption of AI-driven proctoring and monitoring tools exposed an even deeper fracture: a culture of suspicion. Students were treated not as apprentices of knowledge but as potential cheats to be surveilled and punished. AI flagged different cultural behaviors, neurodivergent traits, and even skin tones as “anomalies.” The psychological safety essential for learning eroded under constant scrutiny. Research from the shows that surveillance-heavy environments diminish trust, stifle innovation, and disproportionately harm marginalized students. Education rooted in suspicion cannot grow minds; it only cages them.
Underlying all these failures was a more profound shortcoming: the abandonment of long-term thinking. Most institutions deployed AI reactively — rushing tools into classrooms during crises like COVID-19, chasing enrollment numbers without building systemic resilience. They forgot that true innovation demands foresight, not panic. As ethicist Wendell Wallach reminds us, “Ethics must be designed into AI, not bolted on afterward.” True resilience lies not in reacting faster, but in imagining farther — decades into the future, not just to the next enrollment cycle. For institutions seeking to embed ethical foresight into AI initiatives, offers a foundational guide on how to design values into technical systems.
The path forward is clear. Universities must move beyond technical fixes and commit to ethical re-foundation. Initiatives like Oxford’s Ethical AI in Education project show that it is possible to design educational systems where technology amplifies human dignity, not diminishes it. It demands more than tools. It demands a new philosophy of learning — one rooted in creativity, trust, and responsibility.
If we fail to make this shift, we risk repeating the fate of the dinosaurs: magnificent, powerful, but ultimately undone not by external forces alone — but by their inability to evolve when it mattered most.
A New Blueprint: Lessons Learned and Next Steps
The initial challenges of integrating artificial intelligence (AI) into higher education have provided valuable insights, paving the way for more thoughtful and effective implementations. Recognizing these lessons, several institutions and platforms have embarked on initiatives that harness AI to enhance learning experiences, uphold ethical standards, and broaden access to education.
Pioneering Institutions Embracing AI
If the first wave of AI adoption in higher education revealed the dangers of rushing without reflection, the next wave offers hope. Across the world, a new generation of institutions is demonstrating how AI, when deployed thoughtfully, can expand access, deepen learning, and strengthen human potential.
The lesson from early failures was clear: AI must not be treated as a shortcut for efficiency, but as a catalyst for a richer, more inclusive educational experience. The pioneers leading this shift show what it looks like to get it right — and what others must learn as they catch up.
At, AI ethics isn’t an afterthought — it is embedded into the curriculum. Students learn not just to engineer algorithms, but to understand their societal impacts. Through initiatives like, MIT also opens doors for underrepresented groups to enter the field, blending technical mastery with social consciousness.
In Hong Kong, has created an Education and Generative AI Fund to support faculty in reimagining courses with AI. Rather than policing AI use, HKUST fosters open dialogue about its creative possibilities and ethical boundaries, setting a global model for how institutions can move beyond fear-based reactions to thoughtful integration.
Meanwhile, the demonstrates AI’s power to sustain learning in times of crisis. Amid the disruptions of war, it rapidly shifted to AI-supported flexible learning systems, enabling soldiers and displaced students to continue their education remotely. In moments when traditional models would have collapsed, AI-enabled resilience kept education alive.
Minerva University offers another radical rethinking. At Minerva, AI tools don’t grade students — they help track critical thinking, ethical reasoning, and real-world problem-solving across disciplines. Students are assessed on how they think, not just what they memorize. Minerva proves that with intentional design, AI can measure the traditionally “unmeasurable.”
The course shows that AI literacy should not be a privilege. By offering free, accessible education to over a million learners worldwide, Helsinki redefines who gets to participate in shaping our AI future. Education, they remind us, must be a public good.
Arizona State University embraces AI’s creative possibilities through collaborations like, which builds immersive, AI-enhanced learning environments. At the same time, ASU’s partnership with OpenAI researchers to explore responsible prompt engineering ensures that curiosity is nurtured within ethical bounds.
At, AI education is not about technical skill alone. It is about foresight, responsibility, and understanding the profound human questions that AI forces us to confront. Cambridge’s approach reminds us that the future of AI education must be interdisciplinary, philosophical, and global.
Across these diverse examples, a common pattern emerges: the institutions succeeding with AI are those that design for empowerment, embed ethics early, expand access, and cultivate critical, creative human intelligence. They remind us that the real future of education is not about AI replacing teachers or grading faster — it’s about humans and AI learning together to build a wiser, more resilient civilization.
The next steps are clear. The question is: will more universities follow before it’s too late?
Resources like MIT OpenCourseWare’s Ethics of AI course and offer practical starting points for institutions building responsible AI curricula.
The age of passive education is ending.
Those who fail to integrate AI with wisdom will become relics of a forgotten era — but those who design boldly, ethically, and inclusively will shape the foundations of a new civilization.
AI Platforms Supporting Ethical Education
The early integration of artificial intelligence (AI) into educational settings revealed significant challenges, particularly concerning ethics and academic integrity. However, these initial setbacks catalyzed the emergence of AI platforms deliberately designed to support ethical education — emphasizing critical thinking, inclusivity, and responsible use.
One notable example is, which employs a Socratic approach to prompting students with reflective questions rather than providing direct answers. Institutions such as Northeastern University, the London School of Economics, and Champlain College have partnered with Claude to integrate this methodology into their curricula, aiming to enhance student learning while preserving academic integrity.
Similarly, offers free, comprehensive AI education programs globally. Designed to build ethical literacy alongside technical skills, the Academy provides interactive workshops and digital resources, emphasizing crucial topics like data bias and privacy. Strategic collaborations with institutions like demonstrate how partnerships between AI developers and academia can expand access to responsible AI education.
Beyond higher education, AI platforms are making significant strides in K–12 environments., powered by Anthropic’s Claude, assists educators in streamlining lesson planning, generating personalized feedback, and fostering ethical AI use within classrooms. By supporting rather than replacing human instruction, tools like MagicSchool encourage ethical learning habits from an early age.
Another example is, which leverages AI to transform lectures and course materials into personalized study aids. Their AI tutor, Spark.E, operates in over 20 languages, providing on-demand academic support and demonstrating how AI can make high-quality education more accessible on a global scale.
is pioneering the development of AI-powered digital twins of professors, offering personalized and continuous support to students. These innovations have significantly boosted student engagement and academic performance, showcasing how AI can enhance education without compromising ethical standards.
Institutions seeking to build more transparent and responsible AI infrastructures can also turn to open-source initiatives like the, which provides practical guidelines for embedding transparency, accountability, and human-centered values into AI deployments.
These efforts collectively signal a pivotal shift: moving from the reactive adoption of AI tools to a strategic integration that places ethics, inclusivity, and critical thinking at the heart of educational innovation. By learning from early challenges and adopting these new models, institutions can ensure that students are not only prepared to coexist with AI, but to leverage it consciously and creatively for the betterment of society.
“Ethical education doesn’t automate students; it empowers them to think, question, and lead.”
Call to Action:
Education has always been about the future. Now more than ever, that future demands a reawakening of purpose. Institutions must move beyond adopting AI reactively. They must design new ecosystems where intelligence, ethics, and imagination co-evolve. The next generation is not asking for better machines. They are asking for wiser, more human systems.
In the words of Sir Ken Robinson:
“Creativity is as important in education as literacy. And we should treat it with the same status.”
The same is true today of ethical AI literacy.
If we want the next chapter of higher education to be worthy of the term education at all — it’s time to rewrite the script.
“Ethical thinking must precede technological deployment.” — Wendell Wallach, Moral Machines
Checklist: Is Your Institution AI-Ready for the Future?
✅ Action Step
🔲 Establish an AI-Ethics Council with diverse stakeholders
🔲 Integrate ethical foresight into all AI projects
🔲 Redesign assessments to value creativity, collaboration, and critical thinking
🔲 Shift AI from surveillance to mentorship roles
🔲 Co-create student-led AI governance guidelines
🔲 Offer mandatory AI literacy courses (tech + ethics) across disciplines
🔲 Publish annual AI Impact Transparency Reports
If your institution can’t check at least 5 boxes yet — you’re not preparing for the future. You’re outsourcing it.
Inspiration (and not ticking boxes):
Several institutions are already proving that a thoughtful, ethical approach to AI integration is not only possible — it’s transformative. At Stanford University, the Center for Ethics in Society has partnered with the Institute for Human-Centered AI to embed ethics into technical AI training across disciplines. also offers a compelling model of student-led governance and interdisciplinary collaboration on AI ethics, empowering students to shape not just how AI is used, but how it is governed. At the Hong Kong University of Science and Technology (HKUST), faculty and students co-developed a comprehensive AI code of conduct after a year of workshops and public debates. Meanwhile, the launched AI-powered virtual courses during the 2023 conflict, ensuring that students serving in the military could continue learning without compromising academic standards. These institutions show that ethical readiness is not about having perfect systems. It’s about having the courage to lead, experiment, and learn transparently. Building an AI-ready institution is not about ticking boxes. It’s about reweaving the fabric of trust, creativity, and resilience that higher education was meant to cultivate. The examples above are not outliers — they are early signals. Pioneers who understood that ethics is not a constraint on innovation, but its highest form. They remind us that true leadership in the AI era will belong to those who can combine technical mastery with deep human insight. The question is no longer whether AI will shape education. It already is. The real question is: will you shape it consciously — or be shaped by it blindly?
Time to Rewrite the Script
Higher education stands at a fork in the road.
One path continues the same way: treating AI as a tool to automate outdated systems, leading to deeper alienation, inequality, and irrelevance. The other path sees AI not as a crutch for a broken model, but as a catalyst for human flourishing.
The early failures of AI in universities are not the end of the story. They are the first drafts. They show us — urgently and vividly — what must change:
- AI must be designed into education, not bolted onto it.
- Ethics must be lived, not legislated after the fact.
- Students must be partners, not products of the system.
The future demands not just new technologies — it demands new relationships: to learning, to wisdom, and to each other. Let’s ensure that our educational systems honor that mission they were created in the first place.
If you want to predict the future of AI in education, first ask: What kind of humans are we trying to create?
