The most complete attempt, Quantum Country, embedded 112 questions in an essay that takes about four hours to read and then emailed readers to schedule reviews. Its authors rate the evidence that readers would remember much less without review as only emerging, and say there is no evidence it generalizes to other domains. When a MOOC pushed review questions to learners, about 80% of those assigned never touched them, and grades showed no detectable difference; the example that worked was a classroom experiment where review was scheduled into class time.
Telling readers what something is good for makes them rate the material as more useful, but across seven experiments no difference was detected in immediate test scores; in classroom studies where students wrote about usefulness themselves, results flipped across students and courses. Interesting details unrelated to the main point lowered immediate test scores on average, by an uncertain amount; only one pair of experiments detected no harm when learning was voluntary.
In HarvardX and MITx courses, about half of registrants on average never entered the course; among those who did enter, only 3.13% passed in 2017–18. A prompt to write a study plan raised completion from 13.8% to 17.7% in three courses, but at 247 courses no difference in completion was detected. These are all MOOC numbers; this site's traffic statistics are aggregate, with no data on individual readers' behavior.
Adding a graphic raises reading comprehension by about 0.39 standard deviations on average. Placing each diagram next to the text that explains it, and having readers do something with a diagram rather than just look at it, each have research behind them and cost less than drawing one more diagram, though no experiment compares them directly with adding a diagram; only one experiment isolated prediction, and it found no detectable difference. Use a state table for step-by-step execution, a numbered list for a single-path flow, and a table for comparisons. On a phone, a mermaid diagram with more than 3 nodes side by side in one rank shrinks until the text is unreadable.
Put a video at the top if it covers the whole article, at the start of a section if it covers only that section; videos at the bottom often get skipped. Readers mostly rewatch specific segments, so every section should link to its timestamp and the intro should state the video's length. Prequestions and writing your own explanation after each segment have experimental support, but those experiments all used short material, a little over 20 minutes at most.
Three practices have meta-analytic support: give readers a fully worked example, ask them to explain for themselves, and state a common misconception before refuting it. Questions before reading mainly help the content they ask about, and most experiments used math and science materials, short texts, and college students.
Asked content gains the most, according to research on classroom quizzes and prequestions (I found no synthesis of inserted questions themselves with a legally available full text), and feedback works better the more information it carries, though that evidence comes from classrooms, computer-based materials, and short laboratory texts. Readability formulas explain only a quarter to a third of teachers' difficulty judgments, and 30 Bloom verb lists disagree on where the same verb belongs.
Research interfaces share one move: keep the video and the matching text visible together, either beside the text or right at the passage being read. In the 16-person Papeos study, the condition with clips attached to passages had the lowest mean mental demand (the omnibus test across three conditions was significant). No experiment has compared layouts for a long written guide paired with a lecture recording, and no research I found says what to do on phones.
A 2025 review of 44 studies concludes that shadowing can improve the comprehensibility and fluency of your pronunciation. Most of those studies only tested controlled tasks such as reading aloud, and almost none compared shadowing with other techniques. Use it for rhythm, intonation, and smooth delivery. Building your own sentences and responding to people takes retelling, role play, and conversation.
AI Engineering from Scratch is a free, MIT-licensed course on GitHub with about 63k stars: 20 phases and 523 lessons, running from linear algebra, classical ML, and deep learning to LLMs, agents, and AI safety. Each lesson builds the algorithm by hand, then repeats it with a framework. The early math and deep-learning-core phases are worth following step by step; skip the Phase 10 SFT/RLHF/DPO lessons, and treat the rest as a map of topics.
In 2026, CMU recombined its separate general-AI and SCS machine-learning introductions into the 07-280 → 07-380 sequence. This is a redistribution of content and prerequisites, not a pair of simple course renames.
Lecture 4 first shows that signed/unsigned conversion can preserve bits while changing meaning, that mixed comparisons may surprise, and how sign extension, zero extension, and truncation alter width. It then derives AND, OR, NOT, XOR, and bitmask idioms for testing, setting, clearing, and combining fields.
Lecture 3 starts with 32/64-bit address spaces, derives the ranges of unsigned and two's-complement signed integers, inversion-plus-one, and shared addition hardware, then separates unsigned modular arithmetic from C signed overflow and tests the model against four failure cases.
Lecture 5 extends masks to shifts, power-of-two and popcount tricks, then uses an absolute-value example to expose signed intermediate overflow at INT_MIN. Its second half establishes a GDB workflow around breakpoints, execution control, formatted printing, memory examination, and backtraces.
Lecture 2 puts C back into its Unix history and development environment: headers, main, printf, argc/argv, ssh, emacs, make, and executables. It then derives 8 bits = 1 byte, 256 byte patterns, and reliable conversion among decimal, binary, and hexadecimal.
Winter 2026 opens by explaining why CS107 goes below programming-language abstractions: from bytes and memory through assembly and heap allocators. It then lays out the 40/10/20/30 grading structure and closes with a first tour of the Unix command line.
CMU 15-281's Search and Games explicitly credits Berkeley's Pacman AI projects. The official course site separately lists a zero-point P0 tutorial and five programming assignments, P1–P5.
This lecture identifies exactly when an implication is false, then turns quantified negation, contraposition, and contradiction into checkable proof tools.