Table of Contents
Universities must stop treating AI as a software upgrade — and start becoming public laboratories for trust, equity, and human agency.
Ai &Society ● Higher Education ● Policy Perspectives
“The task of higher education is no longer to deliver content but to protect human agency in a data-driven society.”
Higher education is standing in an ambiguous moment. Knowledge has never been more available, yet understanding feels fragile. Artificial intelligence can summarize a library in seconds, but it cannot decide what that knowledge should mean for a community, a democracy, or a single student trying to find their place in the world. That tension is where universities must now operate.
For many years, universities were treated as gateways to information and credentials. Today information has escaped the gates. What remains inside the campus is more delicate: the ability to question, to interpret, and take responsibility for decisions shaped by machines.
I came to this conviction not only from policy rooms in Tel Aviv, London, or Delhi, but from villages in Maharashtra, where we worked with ten thousand farmers on AI literacy. The project aimed to teach communities how to use digital tools for crop decisions and market access. The technology improved because the community corrected it. That experience reshaped how I understand universities. They should become translation hubs where knowledge travels in both directions — from laboratories to fields and back again.
The Policy Imperative
Too often universities adopt AI as if it were only a technical upgrade. The deeper question is what kind of graduates they aim to shape. Accreditation systems should reward institutions that reduce inequality and strengthen local economies. Governance structures must bring computer scientists together with ethicists, social workers, and students who live with the consequences of these systems. Universities hold vast amounts of personal data, and should model the responsible AI principles aligned with global standards such as the OECD and GPAI. Campuses should function as public laboratories for trust.
The Informal Economy And The Missing Curriculum
The misalignment between universities and the informal economy is increasingly visible. In India, most people work outside formal contracts, yet degrees still speak mainly to office based pathways. When a street vendor or a small farmer cannot see herself in the curriculum, higher education loses its social purpose. The Nashik programme relied on multilingual systems such asBhashini and on local facilitators who translated algorithms into everyday decisions. It showed that prior learning and vernacular knowledge deserve academic recognition.
Lifelong learning must move beyond a slogan. It should function as a system that allows entry at multiple stages, especially for those who never had access to formal education.
“When a street vendor or a small farmer cannot see herself in the curriculum, higher education loses its social purpose.”
Leadership And Social Resilience
Leadership will determine whether that doorway opens. The university leader of today operates less as an administrator and more a bridge between systems. Israel’s collaboration between its Employment Service and universities offers one example of how academic institutions can connect directly to national reskilling strategies. These models position campuses as anchors of social resilience rather than isolated institutions competing for rankings.
Preparing students for unknown jobs requires humility. Technical skills will continue to evolve, while the ability to frame problems and reason ethically remains essential. Classrooms should become spaces of human–AI collaboration where assessment values the reasoning process. When curricula expire every few years, curiosity becomes the most reliable foundation.
Intersectionality And The Ethics Of Inclusion
Technology does not enter neutral environments. Gender, language, and caste shape who benefits from AI long before an algorithm is deployment even begins. Universities need mechanisms similar to ethics boards that examine who is included and who is excluded. During the farmer project, women used the tools differently because access to phones and credit was unequal. Education that ignores these realities will reproduce them.
Global alignment remains important, yet it must reflect diversity. The world does not need a single universal curriculum designed far from lived experience. It needs shared principles that can adapt across contexts. India’s investment in indigenous AI models and Israel’s work on ethics and employment offers complementary approaches. Academic diplomacy can connect these efforts and reduce dependency risks.
Four Steps To Begin
If I could redesign one institution tomorrow, I would begin with four actions. Every student would study data citizenship and AI ethics. Community problem-solving would count toward academic credit. Learners would participate in data cooperatives that give them agency over their digital presence. Universities would work closely with employment system to track real labour market transitions.
“Success will not be measured by how many tasks machines perform, but by how confidently humans can disagree with them.”
A Modest And Radical Hope
Some argue universities are losing relevance. That may happen if campuses continue to operate as certification systems. Yet society still needs spaces for reflection, disagreement, and intellectual risk. A university remains one of the few places where a student can question both an algorithm and an authority figure at the same time.
My hope for the coming decade is both modest and ambitious. I imagine a law student in Tel Aviv, a coder in Bangalore, and a farmer in Nashik each able to engage with AI mentor in their own language while staying rooted in their realities. Higher education should strengthen that confidence. If it succeeds, the university will become even more essential in the age of AI — it will become more necessary than ever.
References
- OECD. (2024). OECD AI Principles. Organisation for Economic Co-operation and Development. https://oecd.ai/en/ai-principles
- GPAI. (2024). Global Partnership on Artificial Intelligence: Work Programmes and Reports. https://gpai.ai
- Ministry of Electronics and Information Technology, Government of India. (2023). Bhashini: National Language Translation Mission. https://bhashini.gov.in
- UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. United Nations Educational, Scientific and Cultural Organization.
- World Bank. (2022). The Human Capital Project: Education in the Digital Age. World Bank Group.
- Israel Employment Service & Consortium of Universities. (2023). National Reskilling for the Digital Economy: A Collaborative Framework. Jerusalem: Government of Israel.
- Crenshaw, K. (1989). Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine. University of Chicago Legal Forum, 1989(1), 139–167.
- Marginson, S. (2022). Global rankings and the geopolitics of higher education. Routledge.
- Hazelkorn, E. (2015). Rankings and the reshaping of higher education: The battle for world-class excellence (2nd ed.). Palgrave Macmillan.
- Ministry of Education, Government of India. (2020). National Education Policy 2020. Government of India.
About the Author
Ms. Maya Sherman is Innovation Attaché at the Embassy of Israel in New Delhi. A multilingual technology researcher, policy advisor, and project leader, she brings over a decade of experience across AI governance, ethics, digital transformation, and emerging technology policy. Maya has led large-scale, multi-stakeholder programmes on responsible AI, climate technology, AI literacy, and digital inclusion, advising senior policymakers, startups, and global institutions. She is known for her work on human-centred AI deployment in informal and rural economies, bridging the gap between cutting-edge research and on-the-ground impact across government, academia, and international organisations.