{"id":326295,"date":"2026-10-03T04:30:00","date_gmt":"2026-10-03T01:30:00","guid":{"rendered":"https:\/\/ceotudent.com\/better-questions-beat-better-answers-question-quality-framework-ai-era"},"modified":"2026-10-03T04:30:00","modified_gmt":"2026-10-03T01:30:00","slug":"better-questions-beat-better-answers-question-quality-framework-ai-era","status":"publish","type":"post","link":"https:\/\/ceotudent.com\/en\/better-questions-beat-better-answers-question-quality-framework-ai-era","title":{"rendered":"Better Questions Beat Better Answers: A Framework for Question Quality in the AI Era"},"content":{"rendered":"<p><strong>TL;DR.<\/strong> Answers have become close to free. By July 2025, ChatGPT alone had more than 700 million weekly users sending over 2.5 billion messages a day, and the share of those messages that were questions (&ldquo;Asking&rdquo;) rose from an even split with task requests in July 2024 to 51.6 percent by late June 2025. Users also rate the answers to questions higher than the output of tasks. At the same time, Anthropic&rsquo;s data show the opposite habit growing: conversations in which the user hands over a whole task with almost no back-and-forth rose from 27 percent to 39 percent in eight months. So the differentiator is no longer who can get an answer but who can ask the question that is worth answering. Research on questioning, from 1990s classrooms to 2018 lab games and a 2025 survey of 319 knowledge workers, converges on three findings. People recognise a good question much better than they generate one. Asking more questions is not the same as asking better ones. And ambiguity is the default, not the exception. A CEOtudent analysis of nine primary studies turns these into a six-dimension Question Quality Framework, scored 0 to 12, with a worked example. This is an editorial framework, not a validated instrument, and we say where its evidence is thin.<\/p>\n<p>This is a companion to our hub on <a href=\"\/en\/the-judgment-economy-human-judgment-ai-era\">the judgment economy<\/a>. That piece argued that judgment is becoming the scarce skill. This one looks at the first act of judgment: deciding what to ask. The CEO move is to treat your questions as the highest-leverage inputs you control. The student move is to practise question generation deliberately, because the evidence says it does not improve on its own.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/ceotudent.com\/en\/better-questions-beat-better-answers-question-quality-framework-ai-era\/#Why-is-question-quality-suddenly-the-bottleneck\" >Why is question quality suddenly the bottleneck?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/ceotudent.com\/en\/better-questions-beat-better-answers-question-quality-framework-ai-era\/#What-has-research-actually-measured-about-questions\" >What has research actually measured about questions?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/ceotudent.com\/en\/better-questions-beat-better-answers-question-quality-framework-ai-era\/#What-pattern-do-the-studies-share\" >What pattern do the studies share?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/ceotudent.com\/en\/better-questions-beat-better-answers-question-quality-framework-ai-era\/#The-Question-Quality-Framework\" >The Question Quality Framework<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/ceotudent.com\/en\/better-questions-beat-better-answers-question-quality-framework-ai-era\/#How-do-you-practise-it-as-a-student\" >How do you practise it as a student?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/ceotudent.com\/en\/better-questions-beat-better-answers-question-quality-framework-ai-era\/#How-do-you-run-it-as-a-CEO-of-yourself\" >How do you run it as a CEO of yourself?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/ceotudent.com\/en\/better-questions-beat-better-answers-question-quality-framework-ai-era\/#What-this-framework-cannot-tell-you\" >What this framework cannot tell you<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/ceotudent.com\/en\/better-questions-beat-better-answers-question-quality-framework-ai-era\/#Frequently-asked-questions\" >Frequently asked questions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/ceotudent.com\/en\/better-questions-beat-better-answers-question-quality-framework-ai-era\/#Sources\" >Sources<\/a><\/li><\/ul><\/nav><\/div>\n<h2 id=\"why-is-question-quality-suddenly-the-bottleneck\"><span class=\"ez-toc-section\" id=\"Why-is-question-quality-suddenly-the-bottleneck\"><\/span>Why is question quality suddenly the bottleneck?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Two data sets published in September 2025 describe how people actually talk to AI systems, and read together they explain the shift.<\/p>\n<p>The first is a working paper by OpenAI economists and David Deming of Harvard, published through the National Bureau of Economic Research. It classified a sample of about 1.1 million ChatGPT conversations from May 2024 to June 2025 into three intents. &ldquo;Asking&rdquo; means seeking information or clarification to inform a decision. &ldquo;Doing&rdquo; means producing an output or performing a task. &ldquo;Expressing&rdquo; means sharing views or feelings. Across the period, 49 percent of messages were Asking, 40 percent Doing and 11 percent Expressing. In July 2024 Asking and Doing were evenly split; by late June 2025 the split was 51.6 percent Asking, 34.6 percent Doing and 13.8 percent Expressing. The authors also report that Asking messages are &ldquo;consistently rated as having higher quality&rdquo; both by a satisfaction classifier and by direct user feedback. Work-related use, meanwhile, fell from 47 percent of messages in June 2024 to 27 percent in June 2025, not because work use shrank but because everything else grew faster.<\/p>\n<p>The second is the Anthropic Economic Index. Its first report, covering late 2024 and early 2025, found 57 percent of Claude.ai conversations showed augmentation patterns (learning, iterating, validating) and 43 percent showed automation patterns. By the September 2025 report, &ldquo;directive&rdquo; conversations, in which the user gives a complete task and takes the result with minimal back-and-forth, had jumped from 27 percent to 39 percent, and automation and augmentation were nearly even on Claude.ai (49 percent automation under the current classifier). The report notes this rise &ldquo;came primarily at the expense of task iteration and learning interactions.&rdquo;<\/p>\n<p>Put the two together. More of what people send to AI is a question, and questions get the better answers. But a growing share of users ask once, take the output and leave. The second question, the one that tests or refines the first answer, is exactly what is disappearing. That is a question-quality problem, and it predates AI by decades.<\/p>\n<h2 id=\"what-has-research-actually-measured-about-questions\"><span class=\"ez-toc-section\" id=\"What-has-research-actually-measured-about-questions\"><\/span>What has research actually measured about questions?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Most advice about &ldquo;asking better questions&rdquo; is folklore. The studies below measured something specific. All figures are taken from the primary texts; none comes from a secondary summary.<\/p>\n<table>\n<thead>\n<tr>\n<th>Study (primary source)<\/th>\n<th>Setting and sample<\/th>\n<th>What was measured<\/th>\n<th>Verified finding<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Graesser and Person, 1994, American Educational Research Journal<\/td>\n<td>Classroom literature synthesis plus 27 college tutoring students and a 7th-grade algebra sample<\/td>\n<td>Question frequency and depth; correlation with exam scores<\/td>\n<td>A single student asks about 0.11 questions per hour in class versus 26.5 per hour in tutoring, roughly 241 times more. Teachers ask about 69 per hour, 96 percent of all classroom questions, and only 4 percent of theirs are high-level. In the first half of the course, total question count correlated negatively with exam scores (r = -0.56); the proportion of deep-reasoning questions correlated positively (0.36, rising to 0.47 later)<\/td>\n<\/tr>\n<tr>\n<td>Rosenshine, Meister and Chapman, 1996, Review of Educational Research<\/td>\n<td>26 intervention studies teaching students to generate their own questions<\/td>\n<td>Effect on reading comprehension tests<\/td>\n<td>Median effect size 0.36 on standardized tests and 0.86 on experimenter-developed tests. The strongest prompt type, generic question stems such as &ldquo;How does &hellip; affect &hellip;?&rdquo;, reached a median of 1.12 across four studies<\/td>\n<\/tr>\n<tr>\n<td>Huang, Yeomans, Brooks, Minson and Gino, 2017, Journal of Personality and Social Psychology<\/td>\n<td>199 conversation dyads, 368 coded transcripts, 110 speed daters and 1,961 dated observations<\/td>\n<td>Question types and how much the partner liked the asker<\/td>\n<td>Partners of high question-askers reported liking of 5.79 versus 5.31 (d = 0.35). Only follow-up questions predicted liking. In speed dating, an 8 percent difference in the follow-up question rate was associated with one additional second date over an evening of 20 dates<\/td>\n<\/tr>\n<tr>\n<td>Rothe, Lake and Gureckis, 2018, Computational Brain and Behavior<\/td>\n<td>40 participants generating free-form questions on 18 Battleship-style boards (605 categorised questions); 45 and 41 participants evaluating questions<\/td>\n<td>Expected information gain (EIG) of generated versus evaluated questions<\/td>\n<td>How often a question was generated correlated with its information value at only r = 0.16. When the same kind of participants ranked questions written by others, their rankings correlated with information value at r = 0.89. About 3 percent of generated questions carried zero information<\/td>\n<\/tr>\n<tr>\n<td>Ruggeri, Lombrozo, Griffiths and Xu, 2016, Developmental Psychology<\/td>\n<td>24 seven-year-olds, 23 ten-year-olds and 23 adults in a hierarchical 20-questions game<\/td>\n<td>Number of questions to solution; level of first question; questions asked after the answer was already determined<\/td>\n<td>Optimal strategy needs 2.85 questions. Adults needed 3.36, ten-year-olds 4.38, seven-year-olds 4.92. Out of three rounds, adults opened at the most informative level 1.87 times versus 0.41 for seven-year-olds. Still, 52 percent of adults asked at least one question that could no longer change the answer<\/td>\n<\/tr>\n<tr>\n<td>Min, Michael, Hajishirzi and Zettlemoyer, 2020, EMNLP (AmbigQA)<\/td>\n<td>14,042 real questions from the Natural Questions open-domain benchmark, drawn from Google searches<\/td>\n<td>Share of questions with more than one valid interpretation<\/td>\n<td>&ldquo;Over half&rdquo; of the development and test questions were ambiguous (47 percent in the training split), with time dependence and entity references among the main sources<\/td>\n<\/tr>\n<tr>\n<td>Lee, Sarkar, Tankelevitch, Drosos, Rintel, Banks and Wilson, 2025, CHI (Microsoft Research)<\/td>\n<td>319 knowledge workers, 936 first-hand examples of using generative AI at work<\/td>\n<td>Self-reported critical thinking and what predicts it<\/td>\n<td>Critical thinking was reported in 59.29 percent of examples. Confidence in the AI was negatively associated with critical thinking (beta = -0.69); confidence in one&rsquo;s own ability (0.26) and in one&rsquo;s ability to evaluate AI output (0.31) were positively associated. Effort shifted &ldquo;from information gathering to information verification&rdquo;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Three caveats sit underneath the table. The classroom numbers are 1990s estimates aggregated by Graesser and Person from earlier studies. The lab games measure questions against a mathematically defined optimum that real decisions rarely have. And the Microsoft survey is self-report and, as the authors state, &ldquo;does not establish causation.&rdquo; We use these studies for the shape of the problem, not for precise forecasts about your next prompt.<\/p>\n<h2 id=\"what-pattern-do-the-studies-share\"><span class=\"ez-toc-section\" id=\"What-pattern-do-the-studies-share\"><\/span>What pattern do the studies share?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Read side by side, the seven studies describe the same four failures, and each failure has an AI-era twin.<\/p>\n<p><strong>1. The generation gap.<\/strong> Rothe and colleagues found that people are poor at producing the best question (r = 0.16 between frequency and value) and good at recognising it (r = 0.89). This is the single most important finding for anyone working with AI. It means the bottleneck is not taste but production. You will know a great question when you see one; you will rarely write one unprompted. Rosenshine&rsquo;s review shows the fix: students explicitly taught to generate their own questions improved substantially on comprehension tests designed around the material (median effect size 0.86, the 81st percentile), and generic question stems were the strongest prompt type (median 1.12 across four studies). Question generation is a trainable skill, and it is trained with scaffolds, not with exhortation.<\/p>\n<p><strong>2. Quantity is not quality.<\/strong> In Graesser and Person&rsquo;s data, students who asked more questions early in the course scored lower on exams, while students whose questions were more often &ldquo;why, how, what if&rdquo; scored higher. Ruggeri&rsquo;s adults, who were far more efficient than children, still asked redundant questions in more than half of cases. A chat window with no cost per message makes this failure cheap and invisible: you can ask twenty shallow questions and feel productive.<\/p>\n<p><strong>3. Ambiguity is the default.<\/strong> AmbigQA found that over half of ordinary search questions have more than one legitimate answer depending on time, entity or scope. A human expert answers an ambiguous question by asking one back. A language model usually answers the most statistically likely reading without telling you that it chose. That silent choice is one mechanism behind the <a href=\"\/en\/first-conclusion-bias-why-you-accept-ai-first-answer\">first-conclusion bias<\/a> we described earlier: the first answer feels complete because the question&rsquo;s ambiguity was resolved for you, invisibly.<\/p>\n<p><strong>4. The follow-up is where the value is.<\/strong> Huang&rsquo;s team found that only follow-up questions, not total questions, predicted whether a conversation partner liked you, and the effect carried into second-date decisions. Anthropic&rsquo;s data show follow-up behaviour (task iteration, learning) is precisely what shrank as directive use grew. Lee&rsquo;s survey adds the mechanism: when people trust the AI more, they think less critically, and the critical thinking that remains has moved to verification. A follow-up question is verification in its cheapest form.<\/p>\n<h2 id=\"the-question-quality-framework\"><span class=\"ez-toc-section\" id=\"The-Question-Quality-Framework\"><\/span>The Question Quality Framework<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The table below is a CEOtudent editorial framework. It is our synthesis of the studies above, not an instrument that has been validated against outcomes. Each of six dimensions is scored 0, 1 or 2. A question scores 0 to 12.<\/p>\n<table>\n<thead>\n<tr>\n<th>Dimension<\/th>\n<th>What a 0 looks like<\/th>\n<th>What a 2 looks like<\/th>\n<th>Research anchor<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Decision link<\/strong><\/td>\n<td>No decision depends on the answer; curiosity or filler<\/td>\n<td>You can name the decision the answer will change and the threshold that would flip it<\/td>\n<td>NBER definition of &ldquo;Asking&rdquo; as informing a decision; Graesser&rsquo;s knowledge-deficit questions<\/td>\n<\/tr>\n<tr>\n<td><strong>Disambiguation<\/strong><\/td>\n<td>Open to several readings of time, entity, scope or audience<\/td>\n<td>Time frame, entity, scope and audience are pinned down in the question itself<\/td>\n<td>AmbigQA (over half of real questions ambiguous)<\/td>\n<\/tr>\n<tr>\n<td><strong>Partition power<\/strong><\/td>\n<td>Tests one guess at a time (&ldquo;Is it X?&rdquo;)<\/td>\n<td>Splits the space of possibilities roughly in half at the most general useful level<\/td>\n<td>Ruggeri&rsquo;s superordinate-first questions; Rothe&rsquo;s expected information gain<\/td>\n<\/tr>\n<tr>\n<td><strong>Depth type<\/strong><\/td>\n<td>Yes\/no, who, what, how many<\/td>\n<td>Why, how, what if, why not: asks for mechanism, cause or consequence<\/td>\n<td>Graesser&rsquo;s deep-reasoning categories, which correlated with achievement<\/td>\n<\/tr>\n<tr>\n<td><strong>Answer shape<\/strong><\/td>\n<td>You would accept any fluent answer<\/td>\n<td>You have written down what a wrong answer would look like and what evidence would settle it<\/td>\n<td>Lee et al.: effort moving to verification; Rosenshine&rsquo;s generic stems<\/td>\n<\/tr>\n<tr>\n<td><strong>Follow-up plan<\/strong><\/td>\n<td>The first answer ends the session<\/td>\n<td>You already know the second question for each likely answer<\/td>\n<td>Huang et al. (follow-up predicts liking and outcomes); Anthropic&rsquo;s declining iteration<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Scoring bands (editorial):<\/strong> 0 to 4 is a search query, fine for facts you will verify elsewhere. 5 to 8 is a working question, good enough for drafts and exploration. 9 to 12 is a decision-grade question, the only kind you should hand to an AI system or a senior colleague when a real choice depends on it.<\/p>\n<h3 id=\"a-worked-example\">A worked example<\/h3>\n<p>Take a common career question: &ldquo;Should I learn Python?&rdquo;<\/p>\n<ul>\n<li>Decision link: 0. No decision or threshold is stated.<\/li>\n<li>Disambiguation: 0. For what role, by when, instead of what?<\/li>\n<li>Partition power: 0. It tests one option against nothing.<\/li>\n<li>Depth type: 0. It invites a yes or no.<\/li>\n<li>Answer shape: 0. Any confident answer would be accepted.<\/li>\n<li>Follow-up plan: 0. &ldquo;Yes&rdquo; ends the conversation.<\/li>\n<\/ul>\n<p>Score: 0 of 12. Any answer to it is noise, and most AI tools will supply a confident, generic yes.<\/p>\n<p>Now the same need, rewritten: &ldquo;I am a marketing analyst with two years of experience and I decide in the next 30 days whether to spend my 60 training hours this quarter on Python for data analysis or on advanced SQL. Which of the two more often appears as a required skill in analyst postings at companies of my size, and which produces usable output faster for weekly reporting? If the answer is Python, what is the smallest project that would prove it in four weeks? If SQL, what would I be giving up?&rdquo;<\/p>\n<ul>\n<li>Decision link: 2. A dated decision with a budget.<\/li>\n<li>Disambiguation: 2. Role, experience, horizon, alternatives.<\/li>\n<li>Partition power: 2. Two live options, compared on two named criteria.<\/li>\n<li>Depth type: 1. It asks &ldquo;which&rdquo; and &ldquo;what&rdquo;, with mechanism implied rather than demanded.<\/li>\n<li>Answer shape: 1. Criteria are named, but no threshold (&ldquo;required in at least X of postings&rdquo;) is set.<\/li>\n<li>Follow-up plan: 2. Both branches already have their next question.<\/li>\n<\/ul>\n<p>Score: 10 of 12. Notice that nothing about the second version is clever. It is the first version plus the scaffolding the research says people do not add on their own.<\/p>\n<h2 id=\"how-do-you-practise-it-as-a-student\"><span class=\"ez-toc-section\" id=\"How-do-you-practise-it-as-a-student\"><\/span>How do you practise it as a student?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Rosenshine&rsquo;s review is clear that question generation improves with explicit scaffolds, and that generic stems worked best. Keep a short list and use it before you open any AI tool:<\/p>\n<ul>\n<li>How does &hellip; affect &hellip;?<\/li>\n<li>What are the strengths and weaknesses of &hellip;?<\/li>\n<li>How does &hellip; tie in with what we already know?<\/li>\n<li>What is a new example of &hellip;?<\/li>\n<li>What conclusions can you draw about &hellip;?<\/li>\n<li>Why is it important that &hellip;?<\/li>\n<\/ul>\n<p>A practical drill: for one week, write your question in a plain text file before typing it into a chat window, score it on the six dimensions, and do not send anything under 5. Then require one follow-up question per session, chosen from your plan rather than invented in reaction to the answer. The <a href=\"\/en\/socratic-prompting-method-make-ai-teach-you\">Socratic prompting method<\/a> is a structured way to run that follow-up, and the <a href=\"\/en\/decision-journal-template-protocol-improving-judgment\">decision journal<\/a> is where the Decision link and Answer shape columns live over time.<\/p>\n<h2 id=\"how-do-you-run-it-as-a-ceo-of-yourself\"><span class=\"ez-toc-section\" id=\"How-do-you-run-it-as-a-CEO-of-yourself\"><\/span>How do you run it as a CEO of yourself?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Treat questions as a budget, not a reflex. Three rules follow from the evidence.<\/p>\n<p><strong>Ask before you delegate.<\/strong> Our piece on <a href=\"\/en\/should-you-let-ai-make-the-decision-delegation-boundaries\">which decisions to hand to AI<\/a> set boundaries by decision type. The framework adds a gate: a task that cannot be phrased as a 9-plus question is not ready to delegate, to an AI agent or to a person, because you have not yet said what a good answer is.<\/p>\n<p><strong>Calibrate trust through the follow-up, not the first answer.<\/strong> Lee&rsquo;s survey found confidence in the AI was the strongest negative predictor of critical thinking. The cheapest counterweight is a planned second question. <a href=\"\/en\/trust-calibration-when-to-trust-ai-recommendations\">Trust calibration<\/a> covers when to trust; the follow-up plan is how you earn that trust case by case.<\/p>\n<p><strong>Audit your question mix monthly.<\/strong> Graesser&rsquo;s achievement correlations tracked the proportion of deep questions, not the count. Once a month, pull ten of your own prompts and classify them as shallow (who, what, yes\/no) or deep (why, how, what if). If the deep share is below a third, your tooling is fine and your questions are the problem. The <a href=\"\/en\/assumption-audit-questioning-what-you-know-about-your-industry\">assumption audit<\/a> is a ready-made source of deep questions about your own field, and the <a href=\"\/en\/discernment-gap-telling-ai-output-from-human-thinking\">discernment gap<\/a> explains why shallow questions produce output you cannot tell apart from thinking.<\/p>\n<h2 id=\"what-this-framework-cannot-tell-you\"><span class=\"ez-toc-section\" id=\"What-this-framework-cannot-tell-you\"><\/span>What this framework cannot tell you<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>It is not validated. No study has tested whether scoring questions on these six dimensions improves decisions. The dimensions are anchored in research; the scoring is editorial.<\/li>\n<li>The usage data are platform-specific and classifier-based. The NBER figures describe ChatGPT consumer plans and exclude users who opted out of data sharing; the Anthropic figures describe Claude.ai and API traffic classified by Anthropic&rsquo;s own models. Neither is a census of AI use.<\/li>\n<li>The lab studies define &ldquo;good&rdquo; as information gain against a known answer space. Most strategic questions have no such space, which is why the Decision link and Answer shape dimensions carry more weight in practice than in the lab.<\/li>\n<li>The classroom rates are decades old and aggregated from several studies; treat the 241-fold difference as an order of magnitude, not a precise figure.<\/li>\n<li>Lee and colleagues measured self-reported critical thinking and state plainly that their analysis does not establish causation.<\/li>\n<\/ul>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently-asked-questions\"><\/span>Frequently asked questions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>Is this the same as prompt engineering?<\/strong><br \/>\nNo. Prompt engineering is about formatting instructions so a model performs a task well; our pieces on the <a href=\"\/en\/prompt-engineering-is-not-enough-ai-literacy-stack\">AI literacy stack<\/a> and <a href=\"\/en\/what-is-context-engineering-skill-replacing-prompt-engineering\">context engineering<\/a> cover that. This framework is about whether the thing you are asking is worth asking, which matters just as much when the person across the table is human.<\/p>\n<p><strong>If people recognise good questions so well, why not just ask the AI to improve my question?<\/strong><br \/>\nThat is a reasonable use of the generation gap, and we recommend it as a step. But Rothe&rsquo;s finding was that people rank questions well when the alternatives are supplied. You still have to evaluate the AI&rsquo;s rewrite, which means you need the six dimensions in your head anyway.<\/p>\n<p><strong>Does asking more follow-up questions slow me down?<\/strong><br \/>\nIn Huang&rsquo;s data the average speed date already contained 4.51 follow-up questions in four minutes, so the cost is small. The time saved by not acting on a misread question is the larger number, though no study we found has measured it directly for AI use.<\/p>\n<p><strong>What is the single highest-value change?<\/strong><br \/>\nWrite the follow-up before you see the first answer. It forces the Decision link and Answer shape columns, and it is the behaviour the 2025 usage data show is declining fastest.<\/p>\n<p><strong>Can I use the framework for meetings, not just AI?<\/strong><br \/>\nYes. Graesser and Person&rsquo;s classroom numbers (teachers asking 96 percent of questions, only 4 percent of them high-level) describe most status meetings. The same six columns apply to the question you bring to your manager or your team.<\/p>\n<h2 id=\"sources\"><span class=\"ez-toc-section\" id=\"Sources\"><\/span>Sources<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>Chatterji, Cunningham, Deming, Hitzig, Ong, Shan and Wadman, &ldquo;How People Use ChatGPT&rdquo;, National Bureau of Economic Research Working Paper 34255, September 2025.<\/li>\n<li>Anthropic Economic Index, &ldquo;Uneven geographic and enterprise AI adoption&rdquo;, September 2025 report, and Handa, Tamkin et al., &ldquo;Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations&rdquo;, March 2025 (arXiv 2503.04761).<\/li>\n<li>Graesser and Person, &ldquo;Question Asking During Tutoring&rdquo;, American Educational Research Journal, 31(1), 1994, pages 104 to 137.<\/li>\n<li>Rosenshine, Meister and Chapman, &ldquo;Teaching Students to Generate Questions: A Review of the Intervention Studies&rdquo;, Review of Educational Research, 66(2), 1996, pages 181 to 221.<\/li>\n<li>Huang, Yeomans, Brooks, Minson and Gino, &ldquo;It Doesn&rsquo;t Hurt to Ask: Question-Asking Increases Liking&rdquo;, Journal of Personality and Social Psychology, 113(3), 2017, pages 430 to 452.<\/li>\n<li>Rothe, Lake and Gureckis, &ldquo;Do People Ask Good Questions?&rdquo;, Computational Brain and Behavior, 1, 2018, pages 69 to 89.<\/li>\n<li>Ruggeri, Lombrozo, Griffiths and Xu, &ldquo;Sources of Developmental Change in the Efficiency of Information Search&rdquo;, Developmental Psychology, 52(12), 2016, pages 2159 to 2173.<\/li>\n<li>Min, Michael, Hajishirzi and Zettlemoyer, &ldquo;AmbigQA: Answering Ambiguous Open-domain Questions&rdquo;, Proceedings of EMNLP 2020.<\/li>\n<li>Lee, Sarkar, Tankelevitch, Drosos, Rintel, Banks and Wilson, &ldquo;The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers&rdquo;, Proceedings of CHI 2025, Microsoft Research.<\/li>\n<\/ul>\n<hr>\n<p><em>This content was compiled with the support of AI following in-depth research, then written and prepared for publication by the CEOtudent editorial team.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>By July 2025 more than 700 million people were sending ChatGPT over 2.5 billion messages a day, and the fastest-growing kind of message was a question. Yet five decades of research on questioning show the same three gaps: people recognise a good question far better than they generate one, more questions do not mean better thinking, and over half of real search questions are ambiguous. This piece joins the 2025 usage data with the classroom, lab and conversation studies and turns the pattern into a six-dimension Question Quality Framework you can score in a minute.<\/p>\n","protected":false},"author":1,"featured_media":326296,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4599,18],"tags":[],"class_list":["post-326295","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-gelisim","category-strateji"],"_links":{"self":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts\/326295","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/comments?post=326295"}],"version-history":[{"count":0,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts\/326295\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/media\/326296"}],"wp:attachment":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/media?parent=326295"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/categories?post=326295"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/tags?post=326295"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}