TO LEARN USING AI, YOU MUST GO DOWN, NOT UP!

Education 2047 #Blog 56 (02 DEC 2025) 

 

AI Changed Learning's Direction in 2022. Education Is Still Going UP.

 

Here's what happens when you actually learn using AI:

You encounter a problem. You ask AI for help. It doesn't tell you to "first study the fundamentals for six months." It doesn't say "complete prerequisites A, B, and C." It gives you what you need right now. You drill DOWN into the specific knowledge required. You apply it immediately. When you need to go deeper, you drill further DOWN.

This is DOWN learning. And with AI, it's not optional—it's inevitable.

For centuries, we climbed UP the ladder of knowledge: start at the bottom with basics, work your way up through prerequisites, eventually reach the summit where you can finally apply what you've learned. Books and teachers made this the only viable path.

But AI doesn't work that way. AI enables—no, forces—a fundamental reversal: Learning goes DOWN, not UP.

This isn't a pedagogical preference. It's the inevitable direction that AI imposes. Try to learn with AI while maintaining traditional UP structures, and you'll fail. The architecture doesn't fit.

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A Simple Demonstration

Scenario: You want to build a mobile app.

Traditional UP Approach:

1. Introduction to Programming course (12 weeks)
2. Data Structures and Algorithms (12 weeks)
3. Mobile Development Fundamentals (12 weeks)
4. UI Design (8 weeks)
5. Database Management (8 weeks)
6. Finally, after 64 weeks, attempt to build your app

What Actually Happens with AI:

1. Day 1: "Help me build a fitness tracking app"
2. AI generates starter code
3. You see code you don't understand: "What does this function do?"
4. AI explains in context of YOUR app
5. You want a feature: "How do I let users log workouts?"
6. AI shows database code, you ask: "Why this approach?"
7. AI explains database concepts AS NEEDED for YOUR feature
8. Week 1: Working basic app
9. Weeks 2-8: Iteratively add features, drilling DOWN as you encounter needs

The Fundamental Incompatibility:

AI won't wait for prerequisites. AI can't prevent you from accessing "advanced" concepts. AI doesn't care about your "level"—it explains anything at your comprehension level. AI starts from YOUR problem, not from a predetermined curriculum.

Trying to enforce UP learning with AI available is like insisting people walk when cars exist.

This is why AI forces DOWN learning. Not because DOWN is better (though it is), but because AI makes UP learning architecturally impossible to maintain.

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The Traditional UP Paradigm (Now Obsolete)

The Structure:

• Foundation → Intermediate → Advanced → Application
• Prerequisites → Concepts → Applications
• Theory → Practice

The Logic: When books were scarce and teachers were gatekeepers, students had no choice but to systematically accumulate knowledge. You couldn't skip to what you needed—you had no way to access it.

Example: Traditional mathematics education requires 15 years (Grade 1 through college) before students apply math to real problems.

The Challenge:

• Time-intensive (years before application)
• Motivation delayed ("learn this now, you'll need it someday")
• Context missing (abstract concepts without application)
• Much learned material never used

This model worked when it was the only option. But it was never optimal—just necessary.

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The AI-Age Reality: DOWN Learning (Because AI Forces It)

The Structure AI Imposes:

• Problem/Question → Relevant Concepts → Specific Details → Foundations (as needed)
• Application → Principles → Theory (just-in-time)
• Practice → Theory (contextual)

What Makes DOWN Faster and More Effective:

When you learn something because you need it to solve a problem you're facing right now, your brain encodes it differently. Research shows:

• Lectures: 5-10% retained after 2 weeks
• Practice by doing: 75% retained
• Immediate use: 90% retained

Example: Coding bootcamps (12 weeks, problem-first) compete successfully with 4-year CS degrees (theory-first). Employers hiring bootcamp graduates alongside degree holders proves demonstrated capability matters more than credential accumulation.

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Why AI Makes UP Learning Impossible

Traditional UP education assumes you'll eventually need what you're learning. But AI breaks this assumption:

With AI available:

• Stockpiling knowledge "just in case" makes no sense (AI has all knowledge instantly)
• Learning things you don't currently need makes no sense (AI explains when you do need them)
• Sequential prerequisites make no sense (AI explains advanced concepts at your level)
• Standardized pacing makes no sense (AI adapts instantly)

UP learning was designed for knowledge scarcity. AI creates knowledge abundance.

The absurdity becomes clear in this dialogue:

• Teacher: "Don't use AI, memorize these formulas first"
• Student: "But AI can derive any formula I need..."
• Teacher: "That's cheating! You must learn foundations first"
• Student: "But AI explains foundations in context when I need them..."

The conversation reveals the incompatibility. UP education protects a learning architecture that AI made obsolete.

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What This Means: Faculty Must Transform

If learning goes DOWN, faculty roles must fundamentally change:

Traditional Faculty (UP Model):

• Content deliverer through lectures
• Knowledge gatekeeper controlling access
• Examiner testing retention
• Curriculum enforcer moving students UP standardized ladder

AI-Age Faculty (DOWN Model):

• Challenge architect designing authentic problems requiring DOWN exploration
• Learning navigator guiding students to resources (not being sole resource)
• Competency validator assessing capability through evidence
• Experience designer creating environments for self-determined learning
• Metacognitive coach teaching HOW to learn, not WHAT to learn

The elevation, not diminution: Designing effective challenges that require drilling DOWN is more sophisticated than delivering content. Assessing competency portfolios requires more skill than grading multiple-choice exams.

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A Concrete Example: Medical Education

Even in medicine—with its strict safety requirements—DOWN learning works with appropriate modifications.

Traditional Approach: Year 1-2: Learn all anatomy, physiology, pharmacology before seeing patients Year 3-5: Clinical rotations applying what was learned (much forgotten by then)

DOWN Approach with Safety Guardrails:

Week 1: Student assigned simulated patient with chest pain, shortness of breath, dizziness.

• Students immediately ask: "What could cause these symptoms?"
• They drill DOWN into cardiac anatomy because they need it now to diagnose
• They learn EKG interpretation because the patient needs it
• They study relevant medications in context of treatment
• AI tutor provides just-in-time explanations
• Faculty guides clinical reasoning: "What's your differential? Why?"

Week 2-3: Case evolves, patient needs ongoing care

• Students drill deeper into cardiac risk factors, prevention
• Each student explores different aspects based on interest
• Faculty validates competency through observed performance

Critical Difference: Faculty strategically sequences 50 cases that collectively cover all required knowledge. Each case enables DOWN drilling, but the sequence ensures comprehensive coverage. Simulation provides safety. Supervised progressive autonomy ensures standards.

Evidence it works: Problem-Based Learning (McMaster University since 1969) shows students learn better from cases than lectures, with equal or better exam performance and superior clinical skills.

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The Implications

For Students:

• Learn faster (2-3 years vs 4 years for many programs)
• Retain more (learning in context of use)
• Demonstrate actual capability (portfolio of work vs transcript of courses)
• Graduate employment-ready (solving real problems from day one)

For Institutions:

• Must redesign around challenges, not courses
• Must assess through competency evidence, not exams (which AI can complete)
• Must develop faculty as experience architects
• Must create infrastructure: innovation labs, industry partnerships, portfolio platforms

For Assessment: Traditional exams are obsolete (AI completes them). Instead:

• Portfolio of authentic work with evidence
• Performance in complex challenges
• Peer and expert validation
• Reflection on learning process

For Credentials:

• Demonstrated capability > accumulated credits
• Evidence of what you can do > record of what you studied
• Time-to-competency becomes variable based on performance
• Some finish in 2 years, some need 4—all demonstrate same final capabilities

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The Global Stakes

This transformation isn't optional. Countries embracing DOWN learning in the next decade will gain competitive advantages that risk-averse systems cannot match.

Why it matters:

• Knowledge work is now global—capability matters more than location
• Employers hire based on what you can do, not where you studied
• AI democratizes access to learning—anyone can drill DOWN from anywhere
• Speed matters—those building capability faster deploy economic value sooner

Evidence from the market:

• Employers accepting coding bootcamp graduates alongside CS degree holders
• Companies valuing GitHub portfolios over GPAs
• Remote work making capability demonstration essential
• Industry certifications (AWS, Google, Microsoft) sometimes valued over academic credentials

The signal is clear: Demonstrated competency matters more than traditional credentials.

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Conclusion: Accept What AI Already Dictates

For centuries, we climbed UP the ladder of learning, accumulating knowledge in hopes we'd someday use it. This made sense when knowledge was scarce.

But AI made knowledge abundant and instantly accessible. Clinging to UP structures isn't just inefficient—it's incompatible with the tool we now have.

To learn using AI, you must go DOWN, not UP. This isn't a suggestion. It's how AI learning actually works:

• AI won't make you wait for prerequisites
• AI can't enforce sequential curricula
• AI doesn't do standardized pacing
• AI starts wherever your problem is and drills DOWN from there

The transformation this requires is fundamental:

• Faculty must become experience architects, not content deliverers
• Institutions must organize around challenges, not courses
• Assessment must validate capability, not test recall
• Credentials must evidence competency, not certify time served

Most importantly, we must recognize that education's goal isn't academic progression (climbing someone else's ladder) but cognitive elevation (building capability to tackle any challenge by drilling DOWN into what's needed).

AI changed learning's direction when ChatGPT launched in 2022. Education is still going UP. Every day we maintain this mismatch, we fall further behind.

The question isn't whether to embrace DOWN learning. AI made that choice for us three years ago.

The question is: How quickly will we accept what AI already dictates?

The tools that force DOWN learning are already in every student's pocket. Education can evolve to work with these tools, or become increasingly irrelevant by fighting them.

AI learns DOWN. We must too.

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For nations, institutions, and individuals: The sunset on UP education is complete. The sunrise on DOWN learning has begun. The only question remaining is whether you'll embrace it now or waste another decade in denial.

 

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About the author 

Dr. Neeraj Saxena is the Pro-Chancellor of JIS University, Kolkata, where he blends decades of educational leadership with foresight into how technology reshapes learning. With a legacy that includes key roles at AICTE and TIFAC, he has helped transform futuristic blueprints into real-world educational reforms across India.

As co-author of Technology Vision 2035: Education Roadmap, Dr. Saxena has long argued that the trajectory of education must shift—not upward through conventional ladders, but downward into deeper dimensions of understanding. His work now focuses on exploring how AI can catalyze that descent—into intuition, imagination, and insight—rather than amplify surface-level academic escalation.

Through his initiative Education2047, he advocates for dismantling industrial-age models and rebuilding learning ecosystems that are fluid, problem-based, and self-directed. His ideas call for replacing rigidity with reflection, syllabi with systems thinking, and replacing metrics of speed with depth of experience.

His earlier article, “The Age of Reversals: When Everything We Know About Education Turns Upside Down”, highlighted how we must shift from climbing the ladder of knowledge to digging deeper into insight, wisdom, and values. That shift now anchors his ongoing work under Education2047 — a call to reimagine learning itself through heutagogy, peer-led ecosystems, and learner agency.

“AI will only deepen our humanity if we redesign learning spaces to develop it,” he says. “For me, that’s not a professional obligation— it’s a generational one.”

This blog is his invitation to join that deeper conversation.

 

Previous (55) blogs

§   The Teacher Unchained: How AI Restores Education's Human Heart 

§   The Sandbox Imperative: Why AI-age Learning Demands New Spaces 

§   What should Students Actually Learn when AI knows everything? 

§   The Four Quadrants that Explain everything Wrong (or Right) about Higher Education 

     §  Teaching Teachers to Think: Redesigning Secondary Education for Higher Cognitive Learning

·    §  The Quiet Revolution: How Everyday Practices Can Transform Higher Education for the AI Age

·    §  Books and Learning 2047: From Sacred Texts to Fading Relevance

·    §  RebuildingTrust in Education: AI-based Transcript Revolution

           §  The Centenary Disappointment Awaits: Teachers' Choice Between Evolution and Extinction

§ Breaking Industrial Cages: Society6.0's Path to Educational Liberation by 2035

§ Decoding Human Potential: Why Grades Are Failing Our Future

§ Ancient Wisdom, Digital Age: What Dronachatya Knew About Teaching With AI

§ Will Universities Survive the Age of AI and BCI ?

§ From Factories of Marks to Foundries of Character:  Indian Higher Education in the AI Age

§ Breaking the Silos: Remagining Universities without Subjects (PART II)

§ Breaking the Silos: Reimagining Universities without Subjects (PART I)

§ Designed to Label, Doomed to Lose: Rethinking a System that Fails its Learners

§ The Missing Catalyst: Peer Learning as the Core of Educational Transformation

§ The Great Educational Reversal: Responding to AI's New Role in Learning

§ Liquidating Cognitive Stagnation in UG Education- The 'SPRINT' Model Blueprint for Change

§ Architects of Viksit Bharat: Why Universities must Recognize Achievement over Graduation

§ The Digital Macaulay: A Modern Threat to Indian Higher Education

§ Why Instant Information Demands a Fundamental Rethink of Education Systems?

§ From Pedagogy to AI-Driven Heutagogy: Redefining Leadership in Universities

§ NEP 2020: Can India’s Education Policy Keep Pace with the FLEXPER Revolution?

§ The Liberating Manifesto: Empowering Faculty to Break Traditional Boundaries

§ From Memory to Creativity: Rejigging Grading & Assessment for 21st Century Higher Education

§ Accreditation and Ranking in Indian Academia: Adapting to New Learning Paradigms

§ Reimagining Education: FLEXPER Learning as a Path beyond Age-based Classrooms

§ Broken by Design: The Worrying State of Secondary Education in India

§ Rethinking Learning: A World Without Curriculum, Classes, Nor Exams

§ Empowering Learners: Heutagogical Strategies for Indian Higher Education

§ Heutagogy: The Future of Learning, Rendering Traditional Education Obsolete

§ The Forgotten Half: Learning from Fallen Ideas through the Metaphor of Dakshinayana

§ 3+1 Mistakes in the Indian Higher Education System

§ Weathering the Technological Storm: The Impact of Internet and AI on Education 

§  The High Cost of Success: Examining the Dark Side of India's Coaching Culture

§  Navigating the Flaws: A Journey into the Depths of India's Educational Framework

§  From Knowledge to Experience: Transforming Credentialing to Future-Proof Careers

§  Futuristic Frameworks- Rethinking Teacher Training For Learner-Centric Education

§  Unveiling New Markers of India's Education-2047

§  Redefining Doctoral Education with Independent Research Paths

§  Elevating Teachers for India's Amrit Kaal

§  Re-engineering Educational Systems for Maximizing Learning

§  'Rubricating' Education for Better Learning Outcomes

§  Indiscipline in Disciplines for Multidisciplinary Education!

§  Re'class'ification of Learning for the New Normal

§  Reconfiguring Education as 'APP' Learning

§  Rejigging Universities with a COVID moment

§  Reimagining Engineering Education for 'Techcelerating' Times

§  Uprighting STEM Education with 7x24 Lab

§  Dismantling Macaulay's Schools with 'Online' Support

§  Moving Towards Education Without Examinations

§  Disruptive Technologies in Education and Challenges in its Governance