The Computer Scientist Who Turned Forgetting Into a Learning Science

Ever wonder why you can study for hours and still forget what you've learned? For decades, the frustrating cycle of memorization and forgetting seemed an unavoidable part of learning a new language or anything, really. But what if forgetting wasn't a flaw in our brains, but a fundamental part of *how* we learn, and could even be harnessed to help us learn *better*? Computer scientist Piotr Wozniak dedicated years to answering that question.

In the summer of 1985, Wozniak was a student in Poznań, Poland, studying molecular biology and computer science simultaneously. He had been keeping paper databases of English word pairs since 1982, but he was dissatisfied with how he was reviewing them at random intervals, with no sense of whether the timing was helping or hurting. So he ran an experiment on himself. Working with pages of roughly forty word pairs, he tested how long the gap before a review could grow before recall broke down. The finding was simple but consequential: after each successful review, the next interval could be made considerably longer without increasing the risk of forgetting. He had, without knowing it, rediscovered a principle that German psychologist Hermann Ebbinghaus had described nearly a century earlier the spacing effect and turned it into a procedure. Review after one day. Then seven. Then sixteen. Then thirty-five. The intervals roughly doubled each time. He called this paper-and-pencil system Algorithm SM-0, and he studied with it for two years before he ever touched a computer.

That patience the willingness to measure, to wait, to refine would define everything Wozniak built afterward. By December 1987, he had access to an Amstrad PC 1512, and he wrote the first computer version of his system: SuperMemo 1.0 for DOS. The move from paper to software changed something fundamental. Instead of scheduling whole pages of vocabulary, the program could now schedule every item individually, according to how well that particular item was actually remembered. The computer could do what the human brain could not easily do on its own: track the forgetting curve for dozens, then hundreds, then thousands of discrete pieces of knowledge and calculate the optimal moment to surface each one again.

The Algorithm That Became an Industry Standard

The algorithm that carried those early DOS versions of SuperMemo described in detail in Wozniak's 1990 master's thesis Optimization of Learning and later published openly on the SuperMemo website became known as Algorithm SM-2. It is disarmingly simple in structure, yet precise in effect. Every item gets its own "easiness factor," or E-Factor, a number that starts at 2.5. After each review, the learner grades their own recall on a scale from zero to five, and that grade nudges the easiness factor up or down. The first interval is one day, the second six days; after that, each new interval is the previous one multiplied by the item's easiness factor. An item that is failed goes back to the beginning of the cycle and returns quickly. Easy material drifts far apart. Difficult material keeps coming back. Every flashcard ends up with a personal review schedule derived entirely from the learner's own performance.

What made SM-2 unusual was not just its mechanics but its openness. Wozniak published the algorithm in full. He did not patent it. He did not restrict its use. Within a few years, adapted versions of SM-2 were running inside Anki, Mnemosyne, and countless other flashcard applications. The formula that began in a Polish student's stopwatch experiment had become the de facto standard of an entire industry. Wozniak himself has noted that he came up with the ideas of repetition spacing later renamed spaced repetition incremental reading, and neural creativity, and that his work in areas including intelligence, creativity, pleasure of learning, dyslexia, sleep, and problem solving earned him the description of a "mad scientist of the learning systems."

From Student Experiment to Global Company

In July 1991, Wozniak co-founded SuperMemo World, becoming the first company in the world to use spaced repetition commercially at scale. By 1995, he had completed his doctorate at the Wrocław University of Economics with a dissertation titled Economics of Learning: New Aspects in Designing Modern Computer Aided Self-Instruction Systems. The title captured something important: Wozniak was not merely building software. He was thinking about learning as an economic system one with costs, returns, and inefficiencies that could be measured and optimized. He served as president and head of research and development at SuperMemo World until 1997, when he founded SuperMemo Research as an independent research unit within the company. He continued authoring new implementations: SuperMemo 7.0 for Windows in 1992, SuperMemo 10 in 2000, and SuperMemo 17 in 2016.

The commercial success of SuperMemo World validated what Wozniak had suspected from the beginning: that the forgetting curve was not an inevitable feature of human memory but a problem with a technical solution. The SuperMemo method, as he described it, was based on a single insight that the optimal moment to review any piece of knowledge is exactly when it is about to fade, not when it has already been forgotten. The algorithm schedules reviews at that precise moment, and the better a learner remembers given information, the longer the intervals grow. Knowledge is gradually consolidated in long-term memory through repetition at increasing intervals, and the process becomes more efficient over time more than more burdensome.

The Learn Drive and the Failure of Schooling

Wozniak's thinking about learning extended well beyond flashcard scheduling. His research interests came to include incremental reading a method for reading texts gradually, with new information extracted and converted into reviewable material over time and the optimization of sleep. He has written extensively about what he sees as the failure of schooling, arguing that learning needs to be driven by the natural "learn drive" more than external compulsion. In his view, the adaptability of human brains is largely underappreciated, and this underappreciation extends to the immune system, musculoskeletal system, vision, appetite, and many other domains. He has described his views as sliding "on the edges of what is acceptable in science," a fact he traces to his work on adaptability.

This perspective shaped the design of SuperMemo itself. The program was not built as a drill-and-kill tool but as a system that works with the grain of human memory. Wozniak has written that if a learner has a bad day and lacks the strength and will to study new material, skipping new cards and focusing on the repeat section instead is still a step forward. The algorithm adapts to the learner's state. It does not punish. It schedules. This philosophy that learning systems should be responsive more than rigid runs through everything he has built.

Incremental Reading and the Living Knowledge System

In 1999, Wozniak introduced the concept of incremental reading, which he first implemented in SuperMemo 10 in 2000. The idea was to apply the logic of spaced repetition not to isolated facts but to entire texts. Instead of reading an article or book from beginning to end in a single sitting, a learner using incremental reading extracts key points gradually, converts them into reviewable items, and lets the algorithm schedule when each piece of knowledge surfaces for re-reading. The text itself becomes a living database, with material reviewed at intervals determined by how well the learner remembers it.

By 2015, Wozniak had developed the concept of neural creativity, first implemented in SuperMemo 17 in 2016. This extended his system from memory optimization into the territory of creative thinking using the spaced repetition framework to surface knowledge in patterns that might generate unexpected connections. The two components of memory for any piece of knowledge can now be inspected by users of SuperMemo, a feature introduced as of Algorithm SM-17. Wozniak has described his current research interests as including molecular and neural correlates of the two-component model of long-term memory, modeling stability and retrievability of memory in learning and forgetting, structural and molecular mechanisms of the spacing effect, and the impact of education on learning and creativity.

Why This Matters for KnowledgePosts Readers

For readers researching practitioners, frameworks, and ideas in the education and learning space, Wozniak's story offers a case study in what it looks like when a single researcher pursues a problem across decades without distraction. He prefers anonymity, he has said, because it allows him to focus on his learning without the noise of public recognition. He has continued publishing research from his home in Poland, maintaining an active presence on his personal site and, as of May 2025, a new Twitter account. His work has influenced not only commercial products but academic research into memory, forgetting, and the spacing effect.

The SuperMemo method remains relevant because its core insight that memory benefits from review at increasing intervals, scheduled precisely at the point of fading has been validated by decades of use and is now embedded in tools used by millions of people worldwide. Whether a learner is studying anatomy, Mandarin, or the history of the Roman Empire, the principle is the same: the algorithm does the scheduling, and the learner does the remembering. Wozniak built the infrastructure. The rest is up to the learn drive.

Timeline: Key Moments in SuperMemo's Development

Where to Read Further

Wozniak has written extensively about the history of spaced repetition, the science of forgetting, and his own research in his personal archive at the History of SuperMemo page on supermemo.guru, which traces the concept from Ebbinghaus through Bjork's Strength Paradox to Wozniak's own experiments. The Taalhammer account of SuperMemo's origins provides a detailed narrative of the 1985 memory experiment and the development of Algorithm SM-2. For the current state of the SuperMemo method and its commercial offerings, the SuperMemo method page describes how the algorithm works in practice for learners today.