How lanyting works: the learning science behind adaptive quizzes and spaced repetition

lanyting is an AI adaptive learning platform that quizzes you first, teaches only your knowledge gaps, and uses spaced repetition to make it stick. Every part of that loop comes from decades of research on how people actually remember things. This page explains the evidence and how the app applies it.

Retrieval practice: why quizzing beats re-reading

Pulling an answer out of memory strengthens that memory more than seeing the answer again. This is called the testing effect, or retrieval practice. In a classic experiment, students who practiced recalling a text remembered about 61% of it one week later, while students who spent the same time re-reading it remembered about 40% (Roediger & Karpicke, 2006). The advantage grows the longer you wait (Karpicke & Roediger, 2008), and a major review of study techniques rated practice testing as high utility, its top rating (Dunlosky et al., 2013).

In lanyting: every course starts with a quiz, not a lecture. The diagnostic doubles as retrieval practice, and each lesson ends with questions to answer from memory.

Feedback: learning from each mistake right away

Retrieval works best when you find out immediately whether you were right, and why. lanyting shows the correct answer and a short explanation after every question you miss. When you answer correctly, it simply confirms it and moves on — no re-teaching what you already know.

Spaced repetition: reviewing at growing intervals

Spreading reviews over days produces far more durable memory than cramming the same number of reviews into one session (Cepeda et al., 2006). The best gap between reviews grows with how long you need to remember the material.

In lanyting: each question you miss becomes a review card. It comes back after 1 day, then 3 days, then 7 days. Answer it correctly and the interval grows; miss it and it resets to one day. This is a simplified version of the SM-2 scheduling approach popularized by SuperMemo and Anki (Wozniak, 1990).

Interleaving: mixing related subtopics

Practicing several related problem types in one session beats drilling one type at a time, because you learn to tell which idea applies (Rohrer & Taylor, 2007). lanyting's diagnostic deliberately mixes subtopics instead of asking five variations of the same question.

Self-explanation: understanding why

Explaining why an answer is correct improves understanding and transfer to new problems (Chi et al., 1989). That is why lanyting's feedback and lessons focus on the reasoning behind each answer, not just the answer itself.

Why effective studying feels harder

Retrieval and spacing produce more mistakes during practice, which many learners read as failure. But that difficulty is exactly what builds long-term memory — researchers call these desirable difficulties (Bjork & Bjork, 2011). If a lanyting review feels harder than re-reading your notes, it is working.

What lanyting deliberately avoids

Re-reading and highlighting, the two most popular study strategies, were rated low utility by Dunlosky et al. (2013). A repetition feature that only re-shows the same lesson text would be the least effective option, so lanyting re-quizzes your missed questions instead.

The lanyting learning loop, step by step

  1. Diagnose: a 5-question adaptive quiz, with "I don't know" always available so guesses don't hide gaps.
  2. Feedback: the correct answer and a short explanation after every miss.
  3. Teach: short AI-written lessons for only the concepts you missed, each ending in retrieval-practice questions.
  4. Review: spaced-repetition cards for every mistake, at 1, 3 and 7 days and beyond.

Lessons and answer keys in lanyting are generated by AI and can contain mistakes — verify important facts against official sources, especially for licensing exams.

Sources

  • Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. doi:10.1111/j.1467-9280.2006.01693.x
  • Karpicke, J. D., & Roediger, H. L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966–968. doi:10.1126/science.1152408
  • Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4–58. doi:10.1177/1529100612453266
  • Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354–380. doi:10.1037/0033-2909.132.3.354
  • Rohrer, D., & Taylor, K. (2007). The shuffling of mathematics problems improves learning. Instructional Science, 35(6), 481–498. doi:10.1007/s11251-007-9015-8
  • Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. doi:10.1207/s15516709cog1302_1
  • Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In Psychology and the Real World (pp. 56–64). Worth.
  • Wozniak, P. A. (1990). Optimization of learning (SM-2 algorithm). SuperMemo. supermemo.com