Around 2019 as I was studying I unknowingly discovered a method for learning any subject quite easily. I’ve never documented the process however most of my classmates used these question and answers I created based on this method. I have also seen no one else document such a process and not a single research that looks into the process as well. I’ve decided to document this today in the hopes that there actually might be something about this that is unique. — I’m calling it “reverse questions”.
The process is not complicated. You just read a sentence of a text and create a question out of it. until there is a question for every single sentence in the whole paragraph. Another requirement is that each question you create must be created in reverse.
Typically, exam questions are posed in a specific way. For example, a history exam might include:
When did the construction of the Great Wall of China begin?
with the answer: 7th century BC.
This conventional format is exactly how an examiner would ask it. However, by reversing the question, you embed the answer into the question itself. A reversed version might be:
In 7th Century BC, what spectacular construction event occurred in China?
Now, the answer becomes “The Great Wall of China.”
Normally, we tend to remember the questions very well but often forget the answer.
was it 7th or 8th century you wonder.
To simplify you flip the format so that the answer becomes part of the question you read through. This way, when you see the question in your exam, your brain automatically recalls the question you created in reverse which will be the reversed question but since it was reversed the answer was in the question and for some reason its just easier to remmember the question rather than the answer.
This is not active or passive voice either. This is basically predicting how an examiner would ask the question and then reversing it to be the complete opposite of that
As I write this blog posts I looked up this topic in a deeper way, Here are some stuff I found through Perplexity and ChatGPT that came from research papers.
Active Recall and the Testing Effect
The testing effect, robustly demonstrated in cognitive psychology, posits that retrieving information from memory strengthens long-term retention more effectively than passive review2. In a seminal study by Karpicke and Roediger (2006), students who engaged in repeated retrieval practice outperformed peers who restudied material, even when total exposure time was equated2. Reverse question methodology operationalizes this principle by requiring learners to actively reconstruct knowledge through self-generated queries. For instance, reversing a standard question like “When did the Great Wall of China begin?” into “In the 7th century BC, what monumental structure was initiated in China?” demands not only recall but also contextual reconfiguration, deepening semantic encoding2.
Critically, the act of generating reversed questions mirrors the potentiating effect observed in retrieval practice, where initial retrieval attempts enhance subsequent encoding during restudy2. By embedding answers within questions, learners create “retrieval cues” that prime memory pathways, facilitating faster and more accurate recall during assessments.
The Generation Effect
The generation effect—wherein self-produced information is retained better than externally provided material—further underpins reverse questioning2. Studies show that generating questions requires learners to engage in elaborative processing, linking new information to prior knowledge and fostering conceptual coherence2. For example, reformulating a textbook sentence into a reversed question necessitates parsing syntactic structures and identifying key entities, thereby reinforcing both factual and inferential understanding.
This aligns with findings from Karpicke and Blunt (2011), where students who generated their own summaries or questions demonstrated superior retention compared to those who passively reviewed texts2. In the context of reverse questions, the dual demands of content generation and structural inversion amplify cognitive engagement, creating a “desirable difficulty” that enhances durable learning2.
Reverse Reasoning
Recent advances in AI research highlight the efficacy of reverse reasoning, where models trained to predict questions from answers exhibit enhanced problem-solving capabilities. Analogously, reverse questioning cultivates bidirectional knowledge networks in learners, enabling flexible application of facts across contexts. For instance, knowing that “The Great Wall’s construction began in the 7th century BC” becomes applicable not only to date-based queries but also to discussions on historical engineering or geopolitical strategies.
Memory consolidation through retrieval cues
Reverse questions uniquely integrate answers into their phrasing, transforming isolated facts into interconnected knowledge nodes. Research on memory effects across question types reveals that retrieval success correlates with cue distinctiveness3. For example, when respondents are asked to recall previous answers, questions framed with embedded context (e.g., temporal or spatial markers) yield higher accuracy3. By embedding answers within questions (e.g., “In 7th century BC, what event occurred?”), learners create context-rich cues that reduce interference and improve recall precision3.
Additionally, reversed questions mitigate the “cue overload” problem—a phenomenon where generic cues (e.g., “When did X happen?”) activate competing memories. Specific, answer-embedded cues streamline retrieval by narrowing associative pathways to target information3.
This aligns with the transfer-appropriate processing theory, which posits that learning is optimized when encoding and retrieval conditions match2. By simulating exam-style questioning through reversed formats, learners align their study practices with assessment demands, thereby improving performance.
While above are some reasons to why this works, the more important point is that it does work and I would like everyone who read through this article to try this on their own to test this out. Also the creation part is just the beggining, you should read through them instead of your notes. (If you properly converted every single sentence in the notes to a reverse question, the reverse questions are essentially your notes)
Citations (Perplexity Deep Research)
- https://journal.iepa.ir/article_170293_5068e3e19636fb3e9f1381c9410536e0.pdf
- https://learninglab.psych.purdue.edu/downloads/2012/2012_Karpicke_CDPS.pdf
- https://ojs.ub.uni-konstanz.de/srm/article/view/7903/7174
- https://arxiv.org/html/2411.19865v1
- https://www.wranx.com/blog/the-importance-of-active-recall/
- https://www.reddit.com/r/Anki/comments/rep63q/the_problem_with_active_recall_and_spaced/
- https://pure.port.ac.uk/ws/files/3318143/Using_the_reverse_order_technique.pdf
- https://theeffortfuleducator.com/2021/11/19/reverse-engineering-the-multiple-choice-question/
- https://upchieve.org/blog/active-recall-studying-methods
- https://www.ejmste.com/article/investigating-the-effects-of-flipped-learning-student-question-generation-and-instant-response-5443
- https://www.irjet.net/archives/V7/i6/IRJET-V7I6753.pdf
- https://dergipark.org.tr/tr/download/article-file/3343135
- https://aclanthology.org/2024.lrec-main.454.pdf
- https://www.semanticscholar.org/paper/e53eac042fa65c0dbb3a50a225ca56127bf84008
- https://www.semanticscholar.org/paper/df9db3eb3ba25e4f9def4104c75f3a73d04a4a22
- https://www.semanticscholar.org/paper/a58d2c56443e2dce6ab192d9cd419845fd3ea762
- https://www.semanticscholar.org/paper/1c763e9fbbd4008a91c7fa14ead370e81361e25e
- https://arxiv.org/abs/2410.22648
- https://www.semanticscholar.org/paper/a7fee74dbba9e978f7e7c022775e7813bae142a1
- https://arxiv.org/abs/2305.18473
- https://pubmed.ncbi.nlm.nih.gov/32332242/
- https://www.semanticscholar.org/paper/f4e7780c5585f4a4dfd842cafd357297610684ed
- https://www.semanticscholar.org/paper/94ffbcfabf5ea052725f3893f07aecd523670107
- https://www.researchgate.net/publication/387779337_Reverse_Teacher_Questioning_Strategy_to_Enlarge_ZPD_----_A_New_Approach_to_Improve_Differentiated_Classroom
- https://methods.sagepub.com/ency/edvol/sage-encyclopedia-of-educational-research-measurement-evaluation/chpt/reverse-scoring
- https://www.linkedin.com/pulse/paper-review-reverse-thinking-makes-llms-stronger-andrey-lukyanenko-feogf
- https://www.wasyresearch.com/reverse-step-building-research-and-knowledge-from-data-or-observation-study/
- https://www.semanticscholar.org/paper/112066603f2871d1ca3afa45cb90fde06c1a37c5
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339075/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865039/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11684041/
- https://www.semanticscholar.org/paper/a596e0bbb12a79ad769315f8b0cc621d0d86d93c
- https://www.semanticscholar.org/paper/92563e452dd58fa2fc025b757e25f5a0d97e42e3
- https://pubmed.ncbi.nlm.nih.gov/39776195/
- https://pubmed.ncbi.nlm.nih.gov/3167500/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958610/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821853/
- https://notes.andymatuschak.org/Testing_effect
- https://www.researchgate.net/publication/344860405_The_Effects_of_Prequestions_versus_Postquestions_on_Memory_Retention_in_Children
- https://pmc.ncbi.nlm.nih.gov/articles/PMC3729568/
- https://aliabdaal.com/studying/active-recall-study-technique/
- https://www.researchgate.net/publication/338725383_Comparing_the_effects_of_generating_questions_testing_and_restudying_on_students’_long-term_recall_in_university_learning
- https://pmc.ncbi.nlm.nih.gov/articles/PMC6231774/
- https://www.vaia.com/en-us/explanations/psychology/memory-studies-in-psychology/active-recall/
- https://onlinelibrary.wiley.com/doi/full/10.1002/acp.3639
- https://www.researchgate.net/publication/226044747_Memory_Recall_Errors_in_Retrospective_Surveys_A_Reverse_Record_Check_Study
- https://www.raulpacheco.org/2016/12/reverse-planning-backcasting-a-paper-or-a-research-project/
- https://www.prepladder.com/fmge-study-material/preparation-strategy/5-ways-to-enhance-active-recall-for-fmg-exam-preparation
- https://www.semanticscholar.org/paper/9da4635fc9181d4fee3dd168ded7824cfa1c78e7
- https://www.semanticscholar.org/paper/8d22e4b42f3b1bdeda4d2dc000399174f2a60cc5
- https://www.semanticscholar.org/paper/dc82700d46a6da669a1716e336fa6e42f85eaeb7
- https://www.semanticscholar.org/paper/b392753a35ac491bd7e2e32c088eb0664b6c714a
- https://www.semanticscholar.org/paper/e4ab5036c2d9410a28a5a535942019232e71e5b0
- https://www.semanticscholar.org/paper/1ea2b70a4988097e7f1c76a651ca4d95be3471d6
- https://www.semanticscholar.org/paper/5990ba86de8c71f8aaf746734d26d1efb3f77fa7
- https://www.semanticscholar.org/paper/1616bac7964ee57d09b948fc07a416e33d7450ab
- https://arxiv.org/abs/2405.10517
- https://www.semanticscholar.org/paper/1504a7f149bc960392ec520e6a5e58b8afc80736
- https://www.researchgate.net/publication/332812965_Reverse_SQL_Question_Generation_Algorithm_in_the_DBLearn_Adaptive_E-Learning_System
- https://arxiv.org/html/2203.14187v2
- https://www.researchgate.net/publication/365116282_A_Review_of_Automatic_Question_Generation_in_Teaching_Programming
- https://www.sarahshihuiwong.com/_files/ugd/8b1a2f_76e33cd7e068453d9e937ace2fff3be0.pdf?index=true
- https://dl.acm.org/doi/10.1016/j.knosys.2022.109625