The new way of learning: why the classroom training day has had its day
A training day feels like learning but plays out like an event: a single moment, a burst of energy, and then silence. Yet skills aren’t built in a single day; they develop through repetition in the actual work. This piece explores this new approach to learning—learning in the flow of work, spaced out and practiced—and explains why AI role-playing finally makes this shift practical for soft skills.
The problem with the training day
The classic approach is well known: a day off the schedule, an external trainer, a conference room, and a satisfaction score at the end. The problem lies not in the day’s content, but in its format. Learning is treated as an event with a clear beginning and end, whereas behavior changes based on what happens *after* that day. As early as 1885, Hermann Ebbinghaus demonstrated via his “forgetting curve” just how quickly knowledge fades without repetition; many representations of his work show that a significant portion is lost within a week. A one-off training day, therefore, fights against the very nature of human memory.
Then there is the structure of learning itself. The well-known 70-20-10 model—popularized by Charles Jennings, among others—estimates that people acquire roughly ten percent of their knowledge through formal training, twenty percent from and with others, and seventy percent by doing the work itself. If this holds true, many organizations are investing the lion’s share of their training budgets into the smallest component of learning. A training day is not worthless, but it is overrated when it stands alone.
There is also a blind spot regarding how we measure training success. A satisfaction score at the end of the day reflects the experience, not behavior a month later. People leave feeling enthusiastic, yet the old patterns often return within weeks. This is not because the day was poor, but simply because a one-time spike does not create a habit. Anyone serious about learning measures not whether it was enjoyable, but whether it actually stuck.
What ‘new learning’ means
The new approach to learning shifts the focus from the event to the process. Instead of a single training day each year, it creates a continuous flow of short learning moments woven into the work itself—an approach referred to in the literature as learning in the flow of work. Learning is no longer an interruption to work, but an integral part of it: brief, relevant at the moment it matters, and repeated often enough to stick.
For knowledge acquisition, this has already become common practice. We look up an explanation when we need it, watch a short video, or read an article. But for soft skills—such as communication, giving feedback, leadership, and handling resistance—simply looking something up is not enough. You do not learn how to handle a difficult conversation by reading about it; you learn by having the conversation. Until recently, practicing these kinds of behaviors was difficult to integrate into the flow of work. That is exactly what is changing now.
What the new way of learning is not
“New learning” is often confused with digitization. Recording a classroom training session and offering it as a video module, or lining up a series of e-learning courses, feels modern but changes little at the core. It remains a matter of knowledge transfer—just on a screen instead of in a classroom. The employee watches, clicks through, and forgets just as quickly as they would in a live session.
The difference lies not in the medium, but in the learning method. New learning is about repeatedly practicing behavior and receiving feedback, not about consuming content. Watching a video is not practice; holding a conversation is. Confusing the two means digitizing the old problem rather than solving it.
Soft skills: practice, don’t just acquire knowledge
The science regarding skill acquisition is unequivocal on this point. Research into “deliberate practice” by K. Anders Ericsson (*Psychological Review*, 1993) shows that expertise stems not from experience per se, but from focused, repetitive practice involving immediate feedback and increasing levels of difficulty. A pianist improves not through a single masterclass, but through daily, focused practice. The same applies to conversational skills—an uncomfortable truth for one-day training courses: without repetition and feedback, little actually changes.
Traditional role-playing acknowledged this—practicing with an actor or colleague is more valuable than listening to theory. However, it does not scale well. Actors are expensive and in short supply; the exercise usually takes place only once; and many people find practicing in front of a group uncomfortable. Consequently, the experience remained an isolated highlight rather than becoming a habit. The repetition required for genuine learning was missing.
AI role-playing makes the new way of learning practical.
This is where technology bridges the gap. With an AI role-play, someone practices a conversation with an AI Avatar that responds realistically: becoming curt, pushing back, and adapting in ways that go far beyond a scripted interaction. Because these practice sessions can take place anytime, without anyone watching, and as often as needed, they fit naturally into the flow of work in a way that a one-day training course never could. Just ten minutes between two meetings is enough for a valuable practice session.
The impact is tangible. A sales team that replaces its annual training day with one short practice session each week does not practice once per quarter, but ten times—each conversation slightly different, with immediate feedback. It is not the intensity of a single day that drives improvement, but the cumulative effect of many small learning moments. This is exactly the shift predicted by both the forgetting curve and the principles of deliberate practice.
Practice does not stop with the conversation itself. After every session, participants receive feedback on specific behaviors—such as conversation structure and asking follow-up questions—linked directly to what they just did. This creates the combination that deliberate practice requires: repetition, progressively increasing challenge, and immediate feedback. AI training, AI Avatar training, and role-playing exercises are transformed from one-off workshops into an ongoing practice routine. This is also where reinforcement finally becomes a natural part of the process: embedded in a consistent learning rhythm.
In addition, every practice session is evaluated against individual competencies. This makes the new approach to learning a natural fit for competency-based training: the focus shifts from completing courses to developing skills that can be measured and tracked over time. Personal development becomes concrete and meaningful, rather than simply another box checked on a training attendance list.
What This Requires from Organizations
The new approach to learning is less about purchasing another training program and more about adopting a different habit: shifting from standalone soft skills training to an ongoing practice routine. It aligns naturally with competency-based training, where the focus is on developing skills rather than completing courses. A few key shifts make the difference:
- Shift from events to rhythm. Replace the ambition of one major training day with short, frequent practice sessions. It is not about learning less, but about distributing learning differently.
- Make practice the norm. People only practice new behaviors when it feels safe to make mistakes. An environment without an audience or judgment lowers that barrier and encourages experimentation.
- Focus on observable behavior. Measure development by what people demonstrate rather than by attendance, and use competency-based feedback as the foundation for coaching conversations.
This does not make the trainer obsolete. Instead, the trainer’s role shifts from delivering knowledge to designing and facilitating meaningful practice. They provide direction, add context, and lead the conversations that AI cannot. The repetitive practice is automated, while the human contribution becomes even more valuable.
Perhaps the greatest benefit is that this new approach solves an old problem. The gap between knowing and doing, between the training room and the workplace, was never caused by poor training. It was a matter of format. By embedding learning into everyday work and making behavioral practice a continuous part of the job, that gap finally begins to close.
Frequently Asked Questions
What is the new approach to learning?
The new approach to learning shifts the focus from one-off training days to continuous learning embedded in everyday work—short, relevant, and repeated over time. Instead of transferring knowledge in a single event, it emphasizes practicing skills consistently so they are retained and applied more effectively.
What is learning in the flow of work?
Learning in the flow of work means that learning becomes part of daily work rather than an interruption to it. People learn or practice when it is most relevant, through short sessions, without having to set aside an entire day for a training course.
How do you practice soft skills digitally?
Soft skills can be practiced digitally through AI role-playing. You engage in realistic conversations with an AI Avatar that responds like a real conversation partner, followed by feedback on your behavior. This allows you to practice communication, giving feedback, leadership, and other interpersonal skills as often as needed, over time.
About the author — Sven is the founder of PractAIce and a behavioral change expert. For many years, he has focused on helping organizations achieve lasting behavioral change, embedding new behaviors into everyday practice, and making soft skills development tangible and measurable.
PractAIce puts the new approach to learning into practice. Employees engage in realistic conversations with an AI Avatar whenever they need, receiving immediate feedback after every session. Would you like to see how this works in your organization? Discover AI Avatar Training or schedule a demo.