Universities After AI: Emerging Framework for AI Transformation of Higher Education

Pavel Luksha – GEF / AlmaU / WUC

2 October 2026

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Our context for the CA+ Rector Club: Central Asia currently has a very favorable position in higher education

“Late comers”

“Regional dividend”

• Most universities established in the last 30 years
• Templates for creating a good university have been refined since post-war higher education massification
• Credentials & ranking systems set clear standards

• Demographic dividend: growing working population (+20 Mil in next 30 years), one of largest shares of young population: 35% vs 16% in Europe
• Urbanization dividend: regional share of urban population will grow to 61%, >65% increase in next 30 years, urban population in Tajikistan grows +2.5% (one of the world’s highest)
• Modernization dividend: 6-8% GDP growth, ICT sector grows x3-4 times faster (e.g. Uzbekistan: +24% in 2025)

But in the next 5-7 years it can be seriously undermined

The paradox (and the irony) of our times

Universities are among humanity's most resilient institutions. For 200 years they were the pinnacle of the Enlightenment project — the place where reason was institutionalized, knowledge systematized, expertise certified. �And now the thing they built — artificial intelligence — is coming back to threaten them.

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Organizations / economies as systems �of tasks that can be gradually �automated by agent solutions

Agency = ability to act autonomously�(reach human level of autonomy) �in specific tasks

AI autonomous agents are getting ready to take over significant portions of professional cognitive work

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Cognitive labor is exposed across all sectors

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Youth & early career professionals are most impacted

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Situation of Central Asian higher education?

• The majority of graduate jobs that CA universities prepare for are in high exposure zone: financial services (accounting, banking etc.), marketing, IT, office administration, foreign language teachers�
• Migration opportunities are closing: Russian economy is increasingly destabilized by the war and sanctions, migration laws are tightening in new regions including the EU and East Asia

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• Demographic collision: the largest junior workforce in CA history will enter the markets as opportunities for entry level cognitive jobs will begin to close in 2027-30

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Fundamentally, AI transforms the cognitive infrastructure of the society. Knowledge – and ability to create it – will no be longer a deficit

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Universities cannot keep their familiar course of action: observing & gradually adapting to a new technology

AI ADAPTATION

Universities

AI

Universities

AI TRANSFORMATION

Economy & society

Economy

AI

VS

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Achilles and the Tortoise: if you try to prepare for what you see now – you are already late

AI CAPABILITY CYCLE

EMPLOYER DEMAND CYCLE

UNIVERSITY PROGRAM CYCLE

GOVERNMENT POLICY CYCLE

5-10 years

6-12 mo

12-18 mo

2-4 years

The irony of our times: �AI talking to AI using human bodies?

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A great hypocrisy – or a timebomb?

• Students will keep on using AI – because it is easier
• Students will keep on using AI – because their jobs and their life will force them to do it anyway
• Students that will not be ready to use AI in their job – will be considered by employers as incompetent
• Students that will use AI – will find it increasingly meaningless to attend lectures and conduct routine exercises that underlie the existing curriculum

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Universities that are not ready to admit that AI is here, it will not go away, and it will actively penetrate existing education and will compete with it – are wasting their precious time and competitiveness

“Ladder of competence”: how does AI change the game of higher education?

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Average �graduate�level

“AI boost”

competence building through pre-AI education

Task 1: accelerated development of �“baseline professional competences” that allow humans to remain �adequate to AI capacities

Task 2: development of “super competences” for AI-augmented work

Mid- to senior�professional �level

Human-AI collaboration

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From Training (graduates for known labor market) �to Formation (capable humans for uncertain world)

Metacognition and critical reflection (AI-inclusive)

Hybrid human-AI fluency

Systems thinking

Ethical integrity

Relational & communicative intelligence

Psychological resilience �& self-regulation

Identity integration

NEW �“UNIQUELY HUMAN”�CURRICULUM�CORE

Shifting our focus of attention

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AI ADAPTATION

AI TRANSFORMATION

• Fighting or limiting AI use by students wholesale (integrity theme rises here)
• Constrained formats like “we have an AI lab”
• AI-related issues handed over to IT department

• Changing pedagogy and assessment to make “integrity problem” largely irrelevant
• AI-related transformation is the catalyst of the complete university reform
• AI transformation has direct sponsorship and control from president / rector level

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The real transformation is not about AI – it is about meaning of education (and research) in the AI era

• What can symbiotic intelligence (Human+AI) do – something more just “only AI” and “only human”?�What are some unique human capabilities that will become increasingly valuable in the AI era?
• How can we redesign our curriculum and learning environments to form such capabilities? How can our assessment systems track them? How can our faculty be ready to deliver?
• What is the meaning of education in the age of transformation and uncertainty? Why – and for what world – are we learning?�What kind of future do we want to create? �(And how do these answers alter our understanding of the necessary capabilities, curriculum, learning environment, assessment, and faculty profiles?)

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AI Dividend

LEVEL 1: DEPLOY / OPTIMIZE / DELEGATE

Remove routine tasks (documentation, teaching assistance, etc)

LEVEL 2: INNOVATE

New pedagogy (personalization, mentoring, …)

LEVEL 3: TRANSFORM

New system for Human+AI reality (incl purpose, assessment, …)

AI Dividend

Logic of “Universities After AI” (1)

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Looking from the “now”: the “extrapolated” future

Pros: evidence based. �Cons: in rapidly moving contexts, extrapolation is limited, action situational

The majority of current frameworks (e.g. QS AI Capabilities) are in this zone

Looking from “emerging futures”: the “backcasted” future that we aim for

Pros: focus on objectives not just trends, taking strategic actions. �Cons: risk of “betting on wrong futures”

Universities After AI aims to explore “what happens when AI is fully deployed”

Logic of “Universities After AI” (2)

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What kind of future do we want to serve? �What is our mission & unique contribution?

What kind of human being we want to help form for this future?

Which curriculum & pedagogy are required? �What assessment can validate it? �What faculty & learning infrastructure can provide it?

What economic & funding mechanisms do we require?

What partners & ecosystem can make it work?

What governance & organizational model can support it?

Entry point: rapid self-diagnostic

YES = the shift is embedded in �normal university practice

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PILOT = an institutionally supported �practice is already running, but only �in limited scope

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NO = discussion / strategy only, �or isolated experiments by �individual enthusiasts

Are we…

• changing our mission
• revising our target graduate profile
• adjusting curriculum & pedagogy
• rethinking assessment
• shifting the role of our faculty
• redesigning research & knowledge creation
• modifying our key admin processes
• etc

?

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Where are we heading: �the landscape of emerging university archetypes