The technology market is facing a convergence of challenges that are reshaping how products are built, sold, and adopted. Artificial intelligence is redefining education at its core transforming learning formats and redefining which skills matter in today’s economy. At the same time, Generation Z is entering the workforce in full force. This cohort differs markedly from previous generations: sustained concentration is harder to achieve, attention must be continuously earned, and traditional learning models are increasingly losing effectiveness.
Layered onto this is a labor market that demands constant reskilling, alongside an unprecedented volume of high-quality information available for free. For IT and EdTech products, the bar is higher than ever: value must be communicated with absolute clarity to persuade users to choose a paid product over open alternatives.
We discussed these challenges with Anastasiia Prokopenko, a leader in EdTech product development at TripleTen, top-3 bootcamps in the U.S. Today, she is responsible for four of the company’s most popular programs, including large-scale, development-heavy initiatives that sit at the intersection of education, technology, and business.
In this conversation, we explore how modern EdTech products evolve under pressure — and what it takes to build learning systems that remain relevant, scalable, and impactful.
AI-related skills are evolving extremely fast, and the shelf life of many roles is shrinking. How does this change the way education products should be designed today?
Anastasiia Prokopenko:
Yes, this shift changes a lot.
Previously, we designed courses starting from a profession. For example, in web development e.g. in 2018–2022, the role was relatively stable. We analyzed market demand, identified a clear skill set (HTML, CSS, JavaScript, React, basic backend), and built a fixed 6–9 month curriculum with defined projects and did minor curriculum improvements after. Because the profession didn’t change quickly, we could spend 6+ months designing, testing, and validating the program.
This approach no longer works. Today, we can’t start from a profession – we start from a domain instead. Take automation as an example. Instead of designing an “automation specialist,” we define a skill set first: process mapping, prompt engineering, API logic, no-code workflows, and basic scripting. These skills apply across many roles, not one job title.
The reason is simple: roles and requirements now change in months, not years, as new tools appear. The same is true for tools themselves. Instead of committing to one platform (e.g. Zapier or Make), we focus on transferable skills – how automation works, how workflows are structured, and how to quickly adapt to new tools.
Thus, the goal has shifted from teaching a profession or a tool to teaching durable, transferable skillset.
When markets move this quickly, traditional benchmarks often disappear. Programs are built for roles that may only fully emerge months later. How do you approach designing education products under this kind of uncertainty?
Anastasiia Prokopenko:
That’s a great question, because uncertainty has become the default state. A good example is the AI Automation Program at TripleTen. It was built almost entirely without benchmarks there were no direct competitors, no established job titles, and no proven curricula to reference. I led the market and role research, analyzing LinkedIn and Indeed postings, interviewing automation practitioners, and mapping around ten adjacent roles to understand where a new profession could realistically emerge.
Based on that work, I made the decision to define “AI Automation Specialist” as a standalone role and build the curriculum around it. At the time, this role did not exist at scale. What happened next was particularly telling: as our graduates entered the job market, employers began using this title more consistently, and similar roles started appearing across companies. In that sense, the program didn’t just respond to demand it actively helped shape and formalize a new profession.
Moreover, shortly after we went live, other tech bootcamps our main competitors, such as Coding Temple and Careerist launched programs with very similar positioning, names, and curricula, and some even retained Nebius AI Studio in their tool sets. This was a strong validation of the direction we had taken, but it also reinforced a critical lesson: in fast-moving AI markets, differentiation based purely on content or tools is extremely fragile. What really matters is the ability to identify emerging demand early, translate it into a coherent role definition, and execute faster than the market can stabilize.
That mindset is what allowed us to launch the program in just three months the fastest curriculum release in the company’s history and to design for adaptability rather than permanence.
As the complexity of the job market and required skill sets increases, education products increasingly sit at the intersection of pedagogy, technology, and business. How do you approach this balance when building and scaling products?
Anastasiia Prokopenko:
You’re right – this balance has become critical. One of the most non-standard decisions I made was insisting on a fully no-code format while still teaching real AI automation. This unlocked a large group of experienced professionals with no coding background who feel vulnerable to AI but are unwilling to spend months on heavy programming. From a business perspective, this expanded the addressable market; pedagogically, it shifted the focus to AI reasoning, system design, and automation logic – skills that remain relevant as tools evolve. Thus, what made the product outstanding was that we effectively defined the first no-code AI automation professional, creating a category that did not previously exist on the market.
On the technology side, I personally led a market-first collaboration of three strong technology products within a single educational program. First, Nebius AI Studio was integrated into the platform and learning experience, giving students free tokens and access to multiple models so they could learn model selection and trade-offs hands-on. Second, I initiated a content partnership with JetBrains, integrating video-based materials into an otherwise text- and assignment-driven curriculum to support different learning styles. These integrations were combined with our own platform, creating a unified learning experience rather than isolated tools.
Rapid change often forces companies to abandon approaches that worked for years. Can you recall a moment when rethinking an established direction became unavoidable?
Anastasiia Prokopenko:
Yes, absolutely. Initially, the AI Automation Program was positioned purely as a reskilling pathway for people leaving at-risk roles. But early applicant profiles and behavioral data told a different story. I pushed to reposition it as both a reskilling and upskilling product. This was not an obvious move internally, but it changed everything from messaging to curriculum depth. As a result, the first cohort became the largest in the company’s history and attracted many professionals with over ten years of industry experience. That shift also unlocked a completely new product category for the company: AI upskilling programs.
What impact did this have on the business and the broader product strategy?
Anastasiia Prokopenko:
The results validated the direction on multiple levels. The program became the fastest-growing product in the portfolio, with higher engagement and significantly higher revenue per student than other programs. But its impact went well beyond the performance of a single launch.
In practice, the AI Automation Program became the starting point for a broader relaunch of the entire TripleTen portfolio. The core AI modules developed for this program were reused and adapted across other tracks, which allowed us to integrate AI capabilities into existing programs much faster than building everything from scratch. This directly informed the launch of new programs such as AI Software Engineering and AI and Machine Learning, and led to the addition of AI-related modules across nearly all offerings.
As a result, AI shifted from being treated as a standalone specialization to becoming a foundational layer across the company’s education products. From a product strategy perspective, this changed how we thought about curriculum design moving from isolated programs to a modular, reusable system that could evolve with the market.
For me, this reinforced that product leadership today is about making high-stakes decisions under uncertainty that not only deliver short-term growth, but also create long-term structural advantages for the business.
Another major challenge for EdTech today is the sheer amount of high-quality educational content available for free. How do education companies survive as a business in this environment? Why do people still pay?
Anastasiia Prokopenko:
That’s a very important point. Content alone no longer has standalone value anyone can access lectures, tutorials, or documentation for free. What people actually pay for today is everything around the content: expert guidance, structured learning paths, accountability, and outcomes.
For example, in my work, I focus on building systems where learning is supported end to end. That includes experienced tutors I hired from Google, SIXT, Meta who provide high-quality feedback, career coaches who help translate skills into real job opportunities, and strong peer communities.
On top of that, the platform itself plays a critical role. I deliberately worked to ensure the platform became an active learning system, not just a content host. Unlike other platforms, which are largely consumption-based, our platform is built around learning analytics and deeply embedded AI features that support students while they are actually doing the work. I initiated and led the project to move all core student projects onto the platform, creating the data foundation required for AI-driven functionality – features such as AI voice tutor for asking questions in context, in-project hints when students get stuck, the ability to ask the platform to explain reviewer comments in plain language, and AI-powered search across project content. These capabilities are triggered by real student behavior and learning data, not offered as standalone tools.
Together with flexibility features I also drove such as optional lessons and adaptable learning paths this work laid the foundation for true personalization: timely feedback, contextual guidance, and systems that respond to individual progress. As a result, the platform doesn’t just deliver content it actively helps students learn, and leading this transformation was a core part of my contribution.
You mentioned structure and outcomes. At the same time, junior tech roles now require broader skill sets and much faster readiness. Why do part-time learning models increasingly fail in this context?
Anastasiia Prokopenko:
Yes, this is exactly where many models break down. The main constraint today isn’t motivation it’s structure. When learning is stretched thin over many months, even strong students struggle to maintain momentum while juggling work and life. From a product perspective, I could clearly see that long part-time formats create systemic friction: progress slows, feedback loops weaken, and learners lose focus before reaching job-ready competence.
How did this insight translate into concrete changes in your programs?
Anastasiia Prokopenko:
It pushed me to rethink the format itself. While working on our part-time software engineering program, I analyzed progress patterns, refunds, and pacing data and realized that flexibility which we previously saw as a strength had become a liability. That insight led me to explore whether intensity and focus could outperform flexibility, especially for learners who needed a full career reset rather than incremental skill accumulation.
What did this shift lead to in practice?
Anastasiia Prokopenko:
It led to the launch of our Software Engineering Full-Time program the first full-time format in the company’s history (that was further followed by Cyber Security Full-Time program launch). I led the transition from a 9.5-month part-time program to a four-month intensive experience. This was not a simple content change: I redesigned the delivery model, redefined instructor roles, and rebuilt the learning cadence to support a much more structured and time-bound experience.
In doing so, I intentionally combined the best of two worlds. From academia, I brought learning design principles structuring complex material, explaining difficult concepts clearly, enforcing meaningful deadlines, and building coherent cognitive progression. From EdTech, I incorporated adaptability, strong practice orientation, learning analytics, and rapid iteration based on real learner behavior.
This synthesis resulted in an innovative educational model. It is neither a typical EdTech course nor a traditional academic program, but something more: an institutional-grade learning system that is rigorous, practical, and adaptive at the same time. Creating and scaling such hybrid educational institutions ones that combine academic depth with technological and product-driven agility is not only my professional contribution, but also my long-term passion and the direction I want to develop further.
Supporting such an intensive format must have required technical changes. What did that involve?
Anastasiia Prokopenko:
Yes, definitely. One key decision was introducing optional lessons on the platform. Pedagogically, this reduced cognitive overload while preserving depth for stronger students. Technically, it required new platform logic conditional lesson unlocking and skip functionality. I worked closely with engineers to define these requirements so the feature could later be reused across other programs. This was a clear example of how pedagogy, product, and engineering need to work together.
How did you assess whether this new format was successful?
Anastasiia Prokopenko:
The difference was very clear. Compared to the part-time format, on-time progress and completion were several times higher, refunds dropped roughly threefold, and revenue per student increased almost three times. That confirmed our hypothesis: in fast-moving tech markets, intensity and structure often outperform long-term flexibility.
Finally, many platforms talk about adaptive learning and personalization, but struggle to implement it. Why is that?
Anastasiia Prokopenko:
This is another major challenge. Personalization is often treated as a content problem, not an infrastructure problem. Many platforms still rely on static learning paths, which makes real adaptation impossible. Without visibility into how students actually work especially on complex tasks personalization remains theoretical.
How did you approach this challenge in practice?
Anastasiia Prokopenko:
I focused on student projects, which are the most information-rich part of learning. At the time, projects were completed outside the platform, so we had no insight into where students struggled. I led an initiative to move projects directly onto the platform, which required significant technical work but gave us real-time learning data.
That, in turn, made it possible to introduce AI-powered support inside projects. Learners now get instant help even when human tutors aren’t available, while tutors can focus on higher-level guidance. The result was faster progress, higher submission rates, and a scalable foundation for adaptive learning.
The conversation with Anastasiia reflects a broader shift underway across education and technology. As AI accelerates change in the job market, the winners will not be those who simply add new content or tools, but those who rethink learning as a system combining speed, structure, and adaptability at scale. Education products are increasingly becoming platforms, formats are becoming strategic decisions, and learning itself is turning into a continuous, data-informed process rather than a one-time experience. In this landscape, the ability to design under uncertainty, align pedagogy with technology, and deliver real outcomes is no longer optional it defines who will shape the next generation of education and work.
