From learners to leaders: reflections on the Upbeat train-the-trainer model

The Upbeat project aims to upskill young immigrants in their entrepreneurs’ journey by using AI technologies. To amplify reach, Upbeat uses a train-the-trainer (TTT) model where a small group of coaches is trained intensively, and they in turn deliver the curriculum to young entrepreneurs.

In addition to developing AI tools, the main role of Haaga-Helia in Upbeat is to deliver a pedagogically well-structured learning programme, which takes into account both the skills of the trainers and the skills of the learners. Initially this was achieved by getting to know all trainers as well as by creating learner profiles to consider very different basic skills.

Getting to know the trainers

The project partners from the StartUp Refugees and Estonian Refugee Council provided both their own trainers to deliver the curriculum created with them in a series of workshops. Haaga-Helia created learning materials which both delivering parties were allowed to finetune to their individual purposes.

All trainers had their unique strengths. Estonian coach, Ilya Vasilyev came in with strong tech and AI fluency, being a tech startup cofounder himself. He was able to include valuable insights into how entrepreneurs utilize different AI tools and also to present his own startup as a example of AI first approach in things like vibe coding and quick prototyping. He manifested that what matters is not just teaching AI but living with AI as a thinking partner in design and business.

The Finnish trainers from Startup Refugees had very different know-how on AI. Muntasar Al-hamad, a business adviser, and Marina Berzina, from a creative background both began with limited AI exposure. They were eager explorers of common generative AI tools, but lacked the technical knowledge and wider applicability of Ai tools in business development. Through the TTT model, both grew to use AI in business ideation, market research, business materials creation and many other crucial stages of starting a new business.

The trainers’ starting points differed widely, which reaffirmed that also the TTT must flex to individual learning curves and context.

Connecting practice and theory

Research on train-the-trainer methods remind us that knowledge transfer is not automatic. The trainers’ different starting points illustrate what Wisshak, Schäfer & van Waveren (2025) describe in their offer-and-use model: trainers do not simply receive knowledge, but interpret and apply it differently depending on their context and prior expertise.

A recurring lesson is that effective TTT fosters ripple effects: when trainers experience authentic growth, their new skills spread outward through their learners and institutions (Servey 2020). AI itself reshapes what training looks like. As Spitzer, Kühl & Goutier (2022) note, human-AI collaboration can accelerate knowledge transfer by enabling novices to experiment quickly with expert-like outputs. In Upbeat, trainers and participants alike experienced this first-hand creating pitch decks, visual assets, or prototype websites in minutes instead of weeks.

Finally, the design of the training matters. Recent work in entrepreneurship education suggests that AI-empowered scaffolds can support learners in complex tasks like business plan development (Zhu & Luo 2025). By giving trainers access to such scaffolds, Upbeat enabled them to shift from teaching static ‘how-to’ content toward facilitating exploration, problem-solving, and reflection. These studies reinforce the notion that TTT is more than a one-time workshop as it requires ongoing support, alignment with context, and mechanisms to maintain momentum.

Upbeat’s lasting impact

The Upbeat journey didn’t end with the learning programme, where more than a hundred young entrepreneurs learned critical business skills through AI. Its legacy continues in how the trainers think, teach and innovate. One of the lessons is that trainers often begin with very different levels of prior knowledge, thus, TTT programs must offer branching paths or differentiated pacing.

Transitioning from theory to real-world use is rarely smooth. The most effective path was through scaffolded practice, iterative coaching, and continuous feedback loops, rather than relying on a single intensive workshop to do it all. Without refresher sessions and shared repositories of prompts and case studies momentum often dissipates.

As Lambebo & Abegaz (2025) remind us, entrepreneurship training succeeds when its design truly nurtures entrepreneurial thinking. While knowledge gains are easy to document, the real test lies in long-term behavioral change. The most powerful outcome is not tool mastery but a mindset shift: trainers who view AI as a collaborator become more adaptive, creative, and resilient in their teaching.

In Upbeat, the TTT model allowed three very different trainers to embody this principle transforming not only how they teach but also how their learners dream, experiment, and build.

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