Building the World’s Next Education System with AI

By Sina Meraji

Every learner knows the frustration of studying hard, collecting grades, and still feeling unprepared for the world of work. That frustration is not a personal failure. It reflects the design of an education system that prioritizes content over connection. What students and professionals need most is not more lectures, but more innovative ways to link knowledge with the right people, the right challenges, and the right opportunities.

What comes next breaks down why traditional models fall short, how AI is already enabling personalization, which lightweight curriculum approaches are proving effective, and why marketplace-inspired models like Learning Loop point to the future. The aim is to give leaders, innovators, and policymakers a fresh framework for thinking about education. The quality of matches should measure the value it creates, not the quantity of content it delivers.

Why Current Education Models Cannot Keep Up

As 2020 unfolded, the COVID-19 outbreak disrupted classrooms worldwide, forcing schools and universities to experiment with remote teaching rapidly. The sudden shift exposed the unpreparedness of many education systems to adapt, revealing structural weaknesses that had existed long before the crisis.

The gap between what education provides and what the workforce demands has already been widening. The World Economic Forum’s 2018 Future of Jobs Report projected that 54% of employees in large companies would require significant reskilling and upskilling by 2025. McKinsey & Company estimated in 2017 that up to 375 million workers, around 14% of the global workforce, might need to switch occupational categories by 2030.

These figures represent millions of people at risk of being left behind in the future of work if education remains tied to fixed curricula and traditional degrees.

The Building Blocks of the Next Education System

The next education system will not emerge from incremental reforms to existing institutions. It will be built as a new architecture, where people, content, and opportunities are treated as the system’s foundations rather than separate silos. Artificial intelligence provides the connective tissue, enabling these elements to interact continuously and adapt to shifting demands.

This shift reframes education from a content-heavy pipeline to a responsive network. Instead of measuring success by the number of courses delivered, progress will be judged by how effectively the system connects learners to the right inputs at the right time. With these building blocks in place, AI enables the design of an environment that is lightweight, scalable, and capable of supporting lifelong learning.

AI as the Connector and Infrastructure

Education reform should not be reduced to policies or bureaucracy. It requires practical tools that adapt to learners in real time. This is where AI offers real value: creating data-driven learning models that continuously match learners to the right people, content, and opportunities. This shift is essential, given the growing need for re-education, as individuals will need to return to learning repeatedly throughout their careers to adapt to rapidly changing roles.

Figure 1. AI as Education’s Matchmaker: Learners are connected through AI to three critical elements - mentors and collaborators, relevant content, and real-world opportunities.

People to People

Learners progress fastest when connected with mentors, collaborators, or peers who expand their perspective. AI can analyze learner profiles and goals to recommend meaningful human connections, scaling what once happened only in small student-led communities.

People to Content

The challenge is not producing more content, but ensuring relevance. Duolingo uses AI to personalize language learning for millions, adjusting lessons on the fly. Recently, Coursera launched CourseMatch, which mapped 2.6 million on-campus courses across 1,800 institutions to equivalent online options in more than 100 languages. This demonstrates how AI can support adaptable pathways that move faster than traditional syllabi.

People to Opportunities

Education matters most when it leads to work and growth. Walmart’s Live Better U, launched in 2018, allowed employees to pursue degrees at minimal cost through Guild Education. This is a concrete example of how companies can contribute directly to reskilling on a large scale.

Real-World Prototypes Already at Work

Several existing initiatives illustrate how marketplace logic is already in motion:

• Peer-to-Peer University (P2PU): community-led study groups built around open resources.
  
  
• edX MicroMasters: modular, career-relevant credentials with both academic and industry recognition.
  
  
• Employer-backed programs: Live Better U shows how companies can underwrite reskilling at scale.
  
  
• Squirrel AI in China: adaptive tutoring that creates personalized learning pathways while keeping teachers engaged.

How Learners Progress in the New Model

Access to learning is only the beginning. Growth happens through progression.

Figure 2. Learner Progression Model: AI can support learners as they move from cautious beginnings to inspired participation and ultimately to leadership, providing timely matches at each stage.

In many communities, learners follow a familiar path. They start cautiously, hesitant to step into new challenges. Encouragement from a peer, a mentor, or even a small success gives them confidence to try again. With repetition, they build competence and ultimately become leaders who guide others.

This cautious-to-inspired leader progression shows why timely matching matters. AI can detect where learners are in this journey and recommend the next step, whether that is a new project, a peer with shared interests, or a mentor who can provide guidance. The marketplace model strengthens this natural cycle of growth.

Extending Matchmaking Beyond The Classroom

The same marketplace logic applies in professional environments.

Learning Loop is a peer-to-peer model where small groups tackle one challenge at a time, building insights and capabilities through ongoing cycles. This approach shows how reskilling with AI can extend beyond formal education, supporting lifelong adaptability in the workforce.

What Leaders And Innovators Should Do Next

For policymakers and university leaders:

• Redefine outcomes. Track matches to mentors, projects, and opportunities, not just enrollment or completion rates.
  
  
• Recognize flexible pathways. Accredit micro-credentials, modular programs, and peer-to-peer models that meet quality standards.
  
  
• Support learner mobility. Develop portable records so progress follows individuals across institutions and careers.

For founders and edtech innovators:

• Design adaptive systems. Build platforms that enable learner signals to trigger new matches and ongoing learning loops.
  
  
• Keep humans central. Use AI to guide learners toward mentors and peers, not to replace them.
  
  
• Align with employers. Integrate education directly with mobility in the labor market.

The Decade Ahead: Building the Next Education System

The 2020s will determine whether education continues as a static content-delivery system or transforms into a connected system of opportunities. Signs of change are already visible. Adaptive consumer apps personalize practice at scale. Universities are using AI to map courses to online equivalents within days. Employers are making significant investments in reskilling their employees. Peer networks and modular credentials are gaining momentum.

The quality of matches should be evaluated by the education it creates. Viewed this way, AI is not hype but a practical infrastructure for connecting learning to real outcomes. The next education system will be built not as rigid institutions but as living networks, where billions of learners can find their place, their collaborators, and their future in real time.

About the Author

Sina Meraji is an entrepreneur and researcher working at the intersection of artificial intelligence, education, and the future of work. He has presented his ideas at international platforms, including TEDx and the International Association of Universities, and has developed peer-driven, data-informed models such as Learning Loop. His work focuses on designing education systems that connect learners with the people, content, and opportunities needed to realize their potential.