Skill Sprint · Growth Library
Ask a Few Questions Before You Start Learning
Use prequestions to make specific information easier to notice and remember
Try this
Choose a small set of questions that point to information you genuinely want to learn. Before opening the explanation, reading the chapter, or watching the lesson, make a brief attempt at each answer even if you are unsure. Then study normally, paying attention when the material resolves those questions. Afterward, check the answers against the source. Use this to target important information, not as a claim that every unasked detail will also be learned better.
Check-in: After studying, can I answer the questions accurately without looking, and did the questions point me toward information that actually mattered?
Evidence: Reviewed · source · King-Shepard et al. (2025) — The Effect of Prequestions on Learning: A Multilevel Meta-Analysis
Review note: Reviewed against the 2025 multilevel meta-analysis. Public guidance is restricted to improved learning for information explicitly targeted by prequestions; the near-zero non-target effect, lack of a universal question dose, and role of feedback remain explicit boundaries.
Starting with questions can change what you notice while learning. A 2025 multilevel meta-analysis found that prequestions improved later learning for the information those questions targeted. The important boundary is just as useful: the benefit did not spread reliably to other, non-prequestioned information. Brali therefore treats prequestions as an attention-and-learning targeting tool, not as a general upgrade for an entire lesson.
1. Choose what deserves attention
Before the learning session, identify a few ideas, distinctions, mechanisms, or facts that would make the session worthwhile. Turn those into answerable questions. Prefer Why does X happen?, What distinguishes A from B?, or What are the conditions for Y? over vague prompts such as What is this chapter about?. The question should point to information you actually care about remembering or using.
2. Attempt an answer before exposure
Spend a short moment trying to answer from your current knowledge. A wrong answer is not failure; the question is doing useful work by creating a target for correction. Do not turn this into a long pre-test or search for the answer elsewhere first. Make the attempt, mark your uncertainty if useful, and move into the material.
3. Notice when the material resolves the question
Study normally, but pay attention when the source gives evidence relevant to a prequestion. Compare the explanation with your initial answer. If the source contradicts you, update the answer rather than rationalizing the guess. If the source never answers the question, that is also information: the question may be outside the lesson or the material may be incomplete.
4. Check the answer after learning
After the relevant section, try the question again without looking. Then check against the source. This creates a simple loop: question, attempt, learn, retrieve, correct. The 2025 meta-analysis also found stronger targeted learning when feedback accompanied prequestions, which is another reason to close the loop instead of leaving the initial guess uncorrected.
5. Do not confuse targeting with coverage
Prequestions can make selected information more learnable, but they can also make your study plan too narrow if the questions represent only a small part of the material. If broad coverage matters, choose questions across the important concepts or combine this protocol with retrieval practice after the full lesson. The evidence does not support claiming that unasked content automatically benefits.
6. Keep the claim narrow
The reviewed meta-analysis pooled many learning settings and found a robust average advantage for prequestioned information, not a universal guarantee for every learner or every question. It does not establish a magic question count, a required delay, or a general comprehension boost. Use prequestions when there are specific things worth noticing; judge the protocol by whether you can later answer those questions accurately and usefully.
Questions to consider
Do I need to know the answers before I start?
No. Prequestions are asked before the learning event, so being unsure or wrong is expected. The practical point is to make a genuine attempt and then let the material resolve the question. This is not a confidence test.
Will this improve learning of the whole lesson?
Do not assume that. The 2025 meta-analysis found a clear benefit for information targeted by the prequestions, but essentially no general benefit for other non-prequestioned information. Use questions to aim attention at what matters.
How many questions should I use?
There is no universal evidence-backed number for every learning task. Start with a small set that covers the concepts or facts you most need to understand, then adjust if the questions are distracting or too narrow.
Should I check the correct answer immediately?
Feedback can help, and the meta-analysis found stronger target learning in conditions that combined prequestions with feedback than in prequestion-only conditions. In practice, the lesson itself can provide the answer; check against the source rather than protecting your first guess.
Sources
Evidence state: reviewed. Sources are provided so the underlying material can be inspected directly.
Maintenance record
Review history
Current status: Reviewed. An editorial or evidence review is recorded and no later event changes that conclusion.
2026-09-15 · reviewed · Brali Evidence Reviewer Source: review-registry-research-watch-2026-09-15-learning-protocols.json
Publish as a new learning protocol because the current Growth Library has no prequestion/pretesting surface, the meta-analytic signal is directly actionable, and the target-versus-nontarget boundary can be preserved without inventing a dose.
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Reviewed evidence decisions
These source reviews are linked to this hack for provenance. They do not change lifecycle status automatically; any material status change still requires an explicit lifecycle event.
2026-09-15 · propose-protocol ·
prequestions-targeted-learning-boundary-2025Reviewed source. Prequestions can improve later learning of the specific information they target. The meta-analysis reported g = 0.66 for prequestioned information and g = 0.01 for non-prequestioned information; feedback alongside prequestions was associated with stronger targeted learning.
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- Version 2026-09-15: first recorded publication in the migrated Brali corpus.
- Evidence review 2026-09-15: reviewed by Brali Evidence Reviewer; evidence state is
reviewed.
Canonical source record · Evidence state: reviewed.