A scalable work-based learning model for an AI-ready workforce

How 220 Youth Leadership is helping districts expand access and prepare students for a changing world of work

A work-based learning expectation is only as strong as the delivery model behind it.

State leaders are increasingly confronting three connected challenges at once: work-based learning demand is rising faster than employer placements can scale, artificial intelligence (AI) is changing the skills students need before they enter the workforce, and districts often lack the staffing, transportation, resources, and employer networks to solve this one school at a time.

As states place greater emphasis on career readiness, work-based learning is expected to play a larger role by serving  more students, documenting more outcomes, and connecting more directly to workforce priorities. Traditional internships and employer-hosted placements remain valuable, but they depend on local employer capacity and willingness, transportation, student schedules, staff coordination, documentation systems, and the availability of roles that align to student interests. This creates a practical gap between what policy increasingly expects and what many schools and communities can deliver through employer placements alone.

Artificial intelligence makes that gap more urgent. The World Economic Forum reports that AI and information processing technologies are expected to be the most transformative technology trend for businesses by 2030, with 86% of surveyed employers expecting those technologies to transform their business. The same report identifies AI and big data, networks and cybersecurity, and technological literacy among the fastest-growing skills, and finds that 39% of workers’ existing skill sets are expected to be transformed or become outdated between 2025 and 2030.

220 Youth Leadership’s scalable WBL model is already proving a different approach in Indiana. Instead of waiting for enough businesses to host enough students, schools are implementing a scalable, simulation-based model where students complete workplace-style projects, receive feedback from professionals, document hours, earn credit, gain certificates,  and build evidence of readiness. This delivery model  also gives students the option to practice critical  AI use in context. Students do not just need tool exposure; they need practice deciding when AI is useful, when human review is required, how to verify outputs, how to protect sensitive information, how to revise work, and how to explain the reasoning behind a final product.

Indiana provides the proof point. For the 2025-2026 academic year, 220 Youth Leadership is currently serving more than 1,700 students across more than 30 Indiana districts, verifying over 4,000 work-based learning hours, and delivering measurable readiness gains. Ninety-five percent of participating students report increased confidence in achieving professional goals, and student satisfaction scores are above 4.4 out of 5.

Why this matters now

Employers increasingly value demonstrated experience and applied skills. The World Economic Forum reports that 81% of employers plan to prioritize evaluation of work experience when assessing talent, compared with 43% prioritizing completion of a university degree. The same report identifies skill gaps as the largest barrier to business transformation, with 63% of employers naming them as a major barrier and 85% planning to prioritize upskilling their workforce (World Economic Forum, 2025).

AI is accelerating these shifts across the nation. PwC reports that skills sought by employers for AI-exposed jobs are changing 66% faster than skills sought for other jobs. PwC also reports a 56% wage premium for workers with AI skills compared with workers in the same jobs without AI skills (PwC, 2025). 

These findings make AI readiness a workforce-readiness issue, not a narrow technology issue. Students across the majority of business functions will encounter work shaped by AI. The relevant skill is not simply knowing how to use a tool, but also knowing how to apply a tool within a task, communicate effectively, evaluate the result, and remain accountable for the final work.

Many students are already comfortable with technology, but comfort does not necessarily translate into workplace competence. The World Economic Forum and Cognizant note that young people may be adept at using technology for communication and information retrieval while still facing challenges applying more advanced skills such as data analysis, coding, and the ethical considerations surrounding AI. The report points to a need for curricula that move beyond foundational skills and engage learners in more complex and creative uses of technology (World Economic Forum and Cognizant, 2025).

That distinction matters for work-based learning. A student may know how to ask AI for an answer without knowing whether the answer is accurate, appropriate, ethical, or useful. A student may know how to generate text without knowing how to revise it for audience, evidence, tone, and purpose. 220’s model gives students a structured and safe place to practice those skills before they enter college, employment, or local work-based learning placements.

The early talent pipeline is changing

Entry-level work has always played a learning role. Junior employees build judgment by completing routine tasks, observing experienced workers, receiving feedback, and gradually taking on more complex responsibilities.

MIT researcher Andrew McAfee has warned that automating too much entry-level work can weaken that learning pathway because junior roles often function like apprenticeships. His concern is practical: if early-career workers do not get opportunities to do the work, they may lose the path that teaches them how to advance (Harvard Business Review, 2026).

This has direct relevance for high school students. If entry-level roles become more automated, more selective, or more dependent on prior experience with AI-enabled tools, students will need earlier opportunities to practice workplace tasks in a structured environment. Work-based learning can help fill that gap, but only if it can reach enough students.

A scalable WBL model gives students a place to build early professional habits before they have to compete for internships, college opportunities, or jobs. They can practice routine and complex tasks, receive feedback, revise their work, and learn how to use AI without handing over responsibility for the result.

The access issue

AI readiness will not reach students evenly by accident. The same access patterns that shape traditional work-based learning can also shape who gets meaningful practice with AI-enabled work: transportation, scheduling, local employer capacity, technology access, professional networks, and the availability of adults who can give feedback on real work.

Jobs for the Future makes the broader equity issue clear. Its work-based learning principles paper notes that although the benefits of work-based learning are clear, they have “accrued primarily to the most highly educated and socially connected segments of the U.S. population.” It also argues that expanding access requires removing barriers and embedding work-based learning into broader career pathways so more students can build academic, technical, and professional skills (Jobs for the Future, 2016).

Strada’s 2025 research points in the same direction from the student side. Students are increasingly using work-based learning to gain experience and skills for a specific career, and the most valuable experiences are the ones that help them build technical skills and expand their professional networks (Strada Education Foundation, 2025).

If applied AI learning depends on informal access, family networks, or the availability of a local employer placement, many students will miss the chance to practice. 220’s model brings that practice into a structured WBL environment. Students can participate remotely, complete rotations, receive professional feedback, document hours, and practice AI-enabled work without needing a separate local employer placement.

The implementation issue

Scaling work-based learning is not just a matter of student interest. It requires partnerships, coordination, documentation, feedback, and support. Jobs for the Future emphasizes that successful work-based learning depends on collaboration among employers, educators, workforce systems, intermediaries, and community partners. It also notes that broad-based partnerships can reduce the demands on each partner while supporting more sustainable work-based learning experiences (Jobs for the Future, 2016).

That is the implementation problem 220 helps solve. The company serves as the employer partner, giving districts a structured way to expand work-based learning without having to build every opportunity one employer placement at a time. Students work through simulated departments, complete project-based assignments, submit deliverables, meet remotely with the 220 team, and receive feedback from real professionals. Districts gain a more efficient operating model for delivering tracked work-based learning hours while students gain access to workplace-style experience that can include responsible AI use. The return on investment is not limited to student participation alone. It also includes avoided implementation costs, lower coordination burden, reduced transportation friction, stronger alignment to readiness requirements, the ability to serve more students without rebuilding the model for each new placement, and broader access for students who might otherwise be excluded by logistics or limited employer capacity.

This structure also supports direct funding alignment. In Indiana, 220’s work-based learning aligns with existing Academic Performance Grant and Career and Technical Education funding structures when programs are coded, staffed, documented, and reported appropriately. At 75 hours, one qualifying student may represent up to $2,994 in combined state-aligned funding . At current scale, 1,739 active students represent up to roughly $5.2 million in potential value when eligibility, completion, coding, and reporting requirements are met. Taken together, that makes scalable work-based learning not only a readiness strategy, but a more sustainable and higher-value model for districts seeking to expand access.

What this means for education and workforce systems

The next generation of work-based learning has to do more than place students near work. It has to help students practice the work.

That means giving students structured opportunities to use tools responsibly, interpret information, communicate decisions, respond to feedback, revise their thinking, and build evidence that they are ready for what comes next. In an AI-transformed economy, those habits matter as much as exposure itself. Students need to learn how work happens, how judgment develops, and how technology fits into a professional standard of quality.

A scalable model matters because this kind of skill development cannot depend on chance access to the right employer, the right schedule, the right transportation, or the right professional network. If work-based learning is going to prepare more students for a changing workforce, the experience has to be designed so that meaningful practice is available consistently, not only when a traditional placement happens to be ideal.