If your organization invests one million euros a year in training and doesn’t apply any systematic post-learning reinforcement strategy, the scientific data show that between six hundred thousand and seven hundred thousand euros evaporate within the first 48 hours after each course. This figure is neither a metaphor nor a marketing exaggeration: it is the direct consequence of a phenomenon discovered almost a century and a half ago and confirmed by all subsequent research. Hermann Ebbinghaus’s forgetting curve is probably the most important finding in cognitive psychology applied to training, and at the same time the one most systematically ignored by the corporate sector. And here’s the news: for 140 years there was no technology that allowed his insights to be applied at scale. Now there is.
At CAE we have spent more than forty-five years designing training programs for large companies and institutions. We have found that the difference between training that produces lasting change and training that remains merely a measurable activity almost always comes down to one variable: whether or not the program includes specific strategies to combat the forgetting curve. This article is a rigorous explanation of what this curve is, why it matters especially in corporate contexts, which proven techniques fight it effectively, and why only artificial intelligence applied to the personalization of learning paths finally makes that application viable in organizations of any size.
Who Ebbinghaus was and why his discovery is still central 140 years later
Hermann Ebbinghaus was a German psychologist who in 1885 published the work “On Memory,” considered the starting point of modern experimental psychology applied to the study of learning. His contribution, documented and repeatedly validated by later research according to the American Psychological Association, was to establish for the first time, using a rigorous method, how information retention evolves over time when no reinforcement is applied.
Ebbinghaus conducted experiments on himself, memorizing lists of nonsense syllables and measuring, at increasing intervals, what proportion of those lists he could recall. The result was a curve that still bears his name today and that describes a drastic drop in retention in the first hours after learning, followed by a progressive slowdown in forgetting.
What is remarkable about his discovery is not just the curve itself, but the fact that later research, using far more sophisticated methods, functional neuroscience, longitudinal studies and experiments with large samples, has consistently confirmed his general conclusions. The forgetting curve is not a hypothesis: it is one of the most solid findings of 20th-century cognitive psychology.
What the forgetting curve actually looks like: the concrete data
The forgetting curve describes the typical evolution of information retention without subsequent reinforcement. The exact figures vary depending on the type of content, the initial degree of understanding and the emotional context of learning, but the general ranges are consistent.
The first 24 hours: the sharpest drop
Without reinforcement, a person loses between 50% and 70% of the information learned within the first 24 hours after learning. This is the phase of greatest loss and, therefore, the most important one to intervene in.
The first 48-72 hours: incomplete consolidation
Between the second and third day, the loss continues but at a slower pace. At this point, a significant proportion of the initial content has already been lost if there has been no active retrieval by the learner.
The first 30 days: memory’s natural floor
After a month without reinforcement, retention usually stabilizes at a low percentage, often between 10% and 30% of what was originally learned. This is the natural floor toward which any unconsolidated learning tends.
The effects of spaced repetition
When learning is reinforced through repetitions spaced out over time, the curve changes radically. Each subsequent exposure flattens the slope and raises the retention floor. After four or five well-distributed repetitions, long-term retention can rise above 80% of the original content.
Why the forgetting curve is especially critical in corporate training
The forgetting curve affects any human learning process, but its impact on corporate training has three particularities worth highlighting.
The first is economic. When a company invests in training, it expects a measurable return in terms of productivity, quality, compliance or customer satisfaction. If between 50% and 70% of learning evaporates within 48 hours, the expected return erodes in the same proportion. Corporate training without subsequent reinforcement is, literally, investment that is lost.
The second is operational. Unlike the academic world, where students can review their notes, study before an exam and build knowledge progressively, employees have a working memory absorbed by the demands of their job. Without a deliberate reinforcement system, training content competes against hundreds of daily stimuli that quickly erode the memory trace of what was learned.
The third has to do with transfer. In corporate training, what matters is not just retaining but applying. And application requires retention robust enough for the knowledge to be available at the moment of a professional decision. Learning retained at 20% rarely translates into effective application on the job.
The four proven techniques for combating the forgetting curve
Pedagogical research over recent decades has precisely identified which interventions flatten the forgetting curve. These are the four techniques with the strongest evidence.
First technique: spaced repetition
Spaced repetition involves exposing the learner to the same content at increasing intervals (1 day, 3 days, 7 days, 21 days). Each exposure reinforces the memory trace and shifts the retention floor upward. It is probably the technique with the best ratio between conceptual simplicity and demonstrated impact.
Second technique: retrieval practice
Retrieval practice, extensively documented by academic projects such as Retrieval Practice, consists of forcing the learner to actively recall learned information, rather than rereading the content. The act of remembering is, in itself, what consolidates long-term memory. This turns questions, short quizzes and applied exercises into training tools, not just evaluation tools.
Third technique: interleaving of content
The interleaving technique consists of alternating related but distinct content instead of blocking learning into single-topic sessions. Although it initially seems less effective to the learner (who perceives greater difficulty), it produces significantly more robust memory traces in the long term.
Fourth technique: guided application on the job
Deliberately applying learning on the job in the days following the course is the most natural reinforcement technique and, according to bodies such as the Association for Talent Development, the one most correlated with real transfer to work. Designing concrete tasks that require employees to use what they have learned in their professional context turns reinforcement into something organic rather than added on.
Why annual training plans cannot apply these techniques at corporate scale
Here we reach the critical point of this article. The four techniques above have been described in the pedagogical literature for decades. Any training manager with a background in pedagogy knows them. And yet, the vast majority of organizations do not apply them. The reason is not lack of knowledge: it is operational impossibility within the traditional paradigm.
Applying spaced repetition requires knowing, for each employee, what they learned, when they learned it, and the optimal moment to send the next re-exposure. Applying retrieval practice requires generating and distributing short quizzes and exercises tailored to what each person needs to review. Applying interleaving requires designing learning paths in which content is alternated according to pedagogical criteria. Applying on-the-job application requires knowing each employee’s role and specific tasks in order to propose credible activities.
All of this is feasible for one employee with a dedicated human tutor. It is completely unfeasible for a workforce of two thousand, five thousand or ten thousand people managed with spreadsheets and static annual training plans. And it is precisely that operational impossibility that has, for decades, condemned corporate training to operate below its real pedagogical potential, even though it knew perfectly well how to combat the forgetting curve.
How artificial intelligence finally makes it viable to apply Ebbinghaus’s findings at corporate scale
The emergence of artificial intelligence applied to the personalization of training paths has closed the gap between what pedagogy has known for a century and what corporate operations could afford. For the first time, it is possible to apply the four techniques for combating the forgetting curve individually and at the full scale of an organization.
An AI system turns the annual plan into a living path
When the training plan is generated automatically by AI, each employee has their own path adjusted to their role, level and skill gaps. This path is not a closed document but a living structure that can be continuously updated. And on top of that living structure, it becomes feasible to schedule spaced reinforcements, incorporate new retrieval practices and adjust interleaving according to the learner’s progress.
Automating spaced reinforcement stops being aspirational
The system knows what each employee learned and when. It can precisely calculate the optimal moment for the first re-exposure (approximately 24 hours), the second (at 3 days), the third (at 7 days) and subsequent ones according to the increasing intervals recommended by research. And it can do this simultaneously for ten thousand employees at no additional cost. What was operationally impossible yesterday is operationally trivial today.
Retrieval practice is integrated into the path naturally
A broad, well-structured training catalog, combined with an AI engine that decides which micro-quiz or short exercise to send to each employee at each moment, produces retrieval practice specifically designed to reinforce what has been learned. The employee doesn’t notice the technique; they simply experience training that arrives exactly when they need it.
Interleaving becomes just another parameter in path design
When the path is built automatically, alternating between related but distinct content stops being a craft decision made by an instructional designer and becomes a configurable principle of the system. AI can adjust interleaving by profile, by competency, or by the employee’s learning preference.
Imagine within the CAE ecosystem: finally applying Ebbinghaus at real scale
The principles above are not theoretical: they describe the conditions we have built into the design of Imagine, our AI-powered solution for automated training plan creation. Imagine analyzes each employee’s competencies, generates personalized paths in seconds, and automatically activates the spaced reinforcement, retrieval practice and interleaving mechanisms that pedagogical research has recommended for decades.
The personalization engine relies on a broad, up-to-date catalog of more than eight thousand five hundred courses that can be incorporated modularly into each person’s path. When the catalog is broad, real personalization is feasible. When the catalog is narrow, any promise of personalization ends up being merely cosmetic.
Frequently asked questions
Is it true that 70% of what is learned is forgotten within 24 hours?
It is an approximation based on Ebbinghaus’s experiments and confirmed, with some nuances, by later research. The exact figure varies depending on the type of content, the initial degree of understanding and perceived relevance. The typical range without reinforcement is between 50% and 70% loss in the first 24 hours.
How many spaced repetitions are needed to consolidate learning?
The evidence indicates that between four and six spaced repetitions, distributed at increasing intervals over the course of a month, are usually enough to bring retention above 80%. The key is not to repeat many times, but to do so at the optimal moments according to the curve.
Why don’t traditional training plans apply spaced repetition?
Because doing it manually for an entire workforce is operationally unfeasible. It requires knowing what each employee learned, when they learned it, and scheduling individualized re-exposures at the optimal moment. What is simple to do for one employee is impossible to do for ten thousand without an AI engine that automates it.
Can a conventional LMS platform implement spaced reinforcement automatically?
It can implement basic reminder mechanisms, but it rarely reaches the level of personalization that a serious application of the forgetting curve requires. The systems that do achieve it are those that combine an AI-based personalization engine (such as Imagine) with a broad, modular training catalog, not standalone conventional LMS platforms.
How do you measure whether an anti-forgetting strategy is working?
The key metric is retention at 30, 60 and 90 days after the course, measured with short active-recall tests. And, above all, real transfer to the job, measured with operational indicators of the employee’s work. Course completion alone says nothing about subsequent retention.
Conclusion: the gap between what we know and what we apply is finally closing
For 140 years, corporate pedagogy has known how to combat the forgetting curve. For 140 years, corporate operations have been unable to apply at scale what pedagogy already knew. This gap, as old as the discipline itself, is finally closing thanks to artificial intelligence applied to the personalization of training paths.
Organizations that are taking advantage of this convergence are achieving far higher training retention than their competitors, much greater transfer to the job and, ultimately, returns on training investment that five years ago were merely aspirational. The difference is not that they know more pedagogy than the others: it is that they finally have the technology to apply it.
At CAE, after forty-five years, we continue to maintain that rigorous pedagogy is the differentiating factor between training that transforms and training that merely entertains. Ebbinghaus’s forgetting curve is a scientific reminder that this difference is not a matter of opinion, it is science that has been established since 1885. And artificial intelligence is, for the first time, the tool that makes it possible to honor that science at the scale that corporate training today requires.
Would you like to find out how Imagine could transform the effective retention of your training program? Contact our team for an initial conversation about how to raise the real retention rate in your organization.