[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"blog-article-152-ai-dlia-formuvannia-rezultativ-navchannia":3},{"uuid":4,"title":5,"subtitle":6,"content":7,"preview":324,"date_published":329,"date_pinned":329,"img":330,"alias":331,"is_published":194,"is_pinned":332,"type":192,"category":333,"category_name":334,"category_alias":335,"background_color":336,"preview_icon":192,"category_value":337,"external_link":192},"1ceaf56f-2292-43fb-b1f0-8a9d504fafa1","AI for Formulating Learning Outcomes","How to formulate learning outcomes with AI: observable actions, performance conditions, scope, criteria, and checking on new material.",{"type":8,"content":9},"doc",[10,16,17,21,22,33,34,42,46,47,51,52,58,62,63,67,68,74,78,79,83,84,90,94,95,99,100,106,110,111,115,116,122,126,127,131,132,138,142,143,147,148,154,158,159,163,164,170,174,175,179,180,186,195,196,200,201,207,211,212,216,217,221,222,228,232,233,237,238,244,251,255,256,262,266,267,273,277,278,284,288,289,295,299,300,304,305,311],{"type":11,"content":12},"paragraph",[13],{"text":14,"type":15},"AI can speed up preparing an instructional-design decision when it receives clear source materials and an expected output. For 'reading a short table of reading-room inquiries,' the benefit is not the number of generated pages. The aim is a written comparison of two weeks naming the unit, period, and conclusion boundaries, alongside understanding why the chosen revision fits the learning task. Instructional designers set context, check assumptions, and approve final requirements. Persuasive labels do not establish learning logic. Check the action, practice, and conditions.","text",{"type":11},{"type":11,"content":18},[19],{"text":20,"type":15},"Consider the author's hypothetical scenario: reading a short table of reading-room inquiries. The learning table has twelve inquiries in week one and eighteen in week two. There is no data on causes, visitor counts, or other weeks. The learning action is comparing the stated counts and distinguishing an observed change from an assumption about its cause. This is a design example rather than a report of a completed course or measured AI benefit. Adapt it to a specific audience, available time, and materials. Separate data from machine suggestions in the package. Approved decisions should be understandable to the next author.",{"type":11},{"type":11,"content":23},[24,31],{"text":25,"type":15,"marks":26},"CMU on Learning Outcomes",[27],{"type":28,"attrs":29},"link",{"href":30},"https:\u002F\u002Fwww.cmu.edu\u002Fteaching\u002Fdesignteach\u002Fdesign\u002Flearningobjectives.html",{"text":32,"type":15},". CMU's recommendations help formulate outcomes through observable student actions. Below, we apply that principle ourselves to an original table. The source supports this pedagogical principle. The original scenario is not research on AI effectiveness. Keep unknowns labeled. Authors must clarify them before substantive approval. The generator helps compare options. The final document must remain verifiable.",{"type":11},{"type":35,"attrs":36,"content":39},"heading",{"level":37,"id":38},2,"heading_2_1",[40],{"text":41,"type":15},"From Intention to Action",{"type":11,"content":43},[44],{"text":45,"type":15},"'Understands statistics' does not explain what to check in work. Our narrow lesson needs only naming periods, correctly comparing counts, and avoiding an unknown cause attributed to the change. This specificity does not reduce all statistical literacy to one sentence. It defines this course fragment's outcome. AI can suggest many verbs, but instructional designers must choose the necessary action rather than the most impressive-sounding word.",{"type":11},{"type":11,"content":48},[49],{"text":50,"type":15},"When comparing weeks, 'name the difference' has a specific object: inquiry counts. 'Analyzes attendance' is broader and would need other data. Include the exact quantity the table contains in the approved outcome so the task attributes no extra meaning to the source. Practical check for 'From intention to action': name information supporting comparison. Record the decision in the current version together with the example used to check it. The boundary for this step is drawing a conclusion about all library visits. Rejected options remain in history rather than becoming current requirements for subsequent material.",{"type":11},{"type":35,"attrs":53,"content":55},{"level":37,"id":54},"heading_2_2",[56],{"text":57,"type":15},"Performance Conditions Matter",{"type":11,"content":59},[60],{"text":61,"type":15},"Students receive a prepared table rather than collect primary information. The outcome should therefore not promise independent demand research. They may use the table and a short glossary, but have no prepared conclusion. Changed conditions change what mastery claims are possible. If AI adds working without a source or conducting a survey, that is another learning design needing its own time and criteria.",{"type":11},{"type":11,"content":64},[65],{"text":66,"type":15},"Access to the table is not a checking weakness. Reading data is part of the action needed. Removing the source may accidentally test number memory. Retain table access while keeping prepared conclusions only in a reviewed example. Practical check for 'Performance conditions matter': state which supports are available during checking. Record the decision in the current version together with the example used to check it. The boundary for this step is treating copying a prepared conclusion as independent comparison. Rejected options remain in history rather than becoming current requirements for subsequent material.",{"type":11},{"type":35,"attrs":69,"content":71},{"level":37,"id":70},"heading_2_3",[72],{"text":73,"type":15},"Outcome Scope",{"type":11,"content":75},[76],{"text":77,"type":15},"One outcome should not hide five different competencies. Comparing counts, drawing a graph, assessing a sample, explaining a cause, and preparing a report are different actions. Our exercise selects the first and adds an honest interpretation boundary. Graphing can be a subsequent outcome if needed. Instructional designers check wording scope against actual student work rather than curriculum line counts.",{"type":11},{"type":11,"content":80},[81],{"text":82,"type":15},"Separate arithmetic from interpretation: students may correctly subtract twelve from eighteen but add an invented cause. One overall score hides the difference. A working map needs two visible indicators so feedback points to a specific action. Practical check for 'Outcome scope': divide complex wording into checkable components. Record the decision in the current version together with the example used to check it. The boundary for this step is adding causal analysis without causal data. Rejected options remain in history rather than becoming current requirements for subsequent material.",{"type":11},{"type":35,"attrs":85,"content":87},{"level":37,"id":86},"heading_2_4",[88],{"text":89,"type":15},"An Example of a Sufficient Answer",{"type":11,"content":91},[92],{"text":93,"type":15},"An acceptable learning conclusion states there were six more inquiries in the second specified week and explicitly notes the table does not explain why. 'The reading room became more popular because of advertising' adds information not supplied. Both answers may be grammatically good, but only one respects source boundaries. Ask AI to locate the transition from calculation to assumption, then check the final model manually.",{"type":11},{"type":11,"content":96},[97],{"text":98,"type":15},"The insufficient answer 'there were more' names neither the amount of change nor the period. It is not necessarily erroneous but does not confirm all declared components. Do not treat imperfect form as proof of misunderstanding: clarify which information participants still need to add. Practical check for 'An example of a sufficient answer': mark data, the arithmetic action, and the conclusion boundary. Record the decision in the current version together with the example used to check it. The boundary for this step is assessing an outcome solely by persuasive style. Rejected options remain in history rather than becoming current requirements for subsequent material.",{"type":11},{"type":35,"attrs":101,"content":103},{"level":37,"id":102},"heading_2_5",[104],{"text":105,"type":15},"Criteria Without Hidden Requirements",{"type":11,"content":107},[108],{"text":109,"type":15},"This exercise checks correct counts, period names, difference size, and absence of invented causes. Do not lower grades for lacking a chart if the outcome does not require one. Do not demand professional terminology absent from materials. AI may add a generic rubric with design, originality, and volume. Reduce it to indicators confirming the stated action. Students should see criteria before working.",{"type":11},{"type":11,"content":112},[113],{"text":114,"type":15},"The rubric may accept different syntax expressing the same correct meaning. Do not demand a verbatim six-inquiry model. Check that the difference refers to the specified weeks and the causal boundary survives answer shortening. Practical check for 'Criteria without hidden requirements': align each criterion with a learning-outcome component. Record the decision in the current version together with the example used to check it. The boundary for this step is including attractive formatting as proof of numerical understanding. Rejected options remain in history rather than becoming current requirements for subsequent material.",{"type":11},{"type":35,"attrs":117,"content":119},{"level":37,"id":118},"heading_2_6",[120],{"text":121,"type":15},"Course and Lesson Levels",{"type":11,"content":123},[124],{"text":125,"type":15},"A course may end with explaining several types of quantitative information. Our lesson handles one short comparison. Instructional designers show the connection between levels rather than copy a large outcome into every exercise. A course map marks where students first name units, practice comparison, and independently work with new data. This distinguishes repeated wording from actual action development.",{"type":11},{"type":11,"content":128},[129],{"text":130,"type":15},"At module level, this outcome may contribute to broader work with tables. State its contribution in one line without promising every subsequent action in one exercise. A level map should show development rather than the same large outcome in each block. Practical check for 'Course and lesson levels': show the lesson's contribution to a broader course outcome. Record the decision in the current version together with the example used to check it. The boundary for this step is attributing complete data-analysis mastery to a short exercise. Rejected options remain in history rather than becoming current requirements for subsequent material.",{"type":11},{"type":35,"attrs":133,"content":135},{"level":37,"id":134},"heading_2_7",[136],{"text":137,"type":15},"Checking on New Material",{"type":11,"content":139},[140],{"text":141,"type":15},"An independent attempt needs another original table with analogous logic. Merely changing the initial example's color is insufficient. Use two new periods and check whether students transfer comparison rather than reproduce six. Explicitly label new data educational too. If a task unexpectedly needs percentages, that is another prerequisite to teach or remove from this outcome check.",{"type":11},{"type":11,"content":144},[145],{"text":146,"type":15},"The new example may decrease rather than increase. This checks reading method rather than 'week two has more.' Record change direction and size in the key; do not force students to infer a cause absent from the material. Practical check for 'Checking on new material': retain the action while changing source counts. Record the decision in the current version together with the example used to check it. The boundary for this step is testing a new mathematical operation without preparation. Rejected options remain in history rather than becoming current requirements for subsequent material.",{"type":11},{"type":35,"attrs":149,"content":151},{"level":37,"id":150},"heading_2_8",[152],{"text":153,"type":15},"Revision After a Trial Reading",{"type":11,"content":155},[156],{"text":157,"type":15},"Give colleagues the wording and ask for a possible student answer. If they imagine advertising research while you mean a short comparison, the outcome is insufficiently precise. AI can compare interpretations but must not hide differences through smooth rewriting. Retain initial and approved versions, the change reason, and related task. The next material author then knows why a causal-conclusion boundary is a learning requirement here.",{"type":11},{"type":11,"content":160},[161],{"text":162,"type":15},"After clarifying the outcome, update the example and criteria. If wording now requires a conclusion boundary, teaching must make it visible. Do not leave the new component only in a curriculum document: task authors need it together with its role explained. Practical check for 'Revision after a trial reading': check consistent outcome understanding across authors. Record the decision in the current version together with the example used to check it. The boundary for this step is fixing a verb while retaining a contradictory task. Rejected options remain in history rather than becoming current requirements for subsequent material.",{"type":11},{"type":35,"attrs":165,"content":167},{"level":37,"id":166},"heading_2_9",[168],{"text":169,"type":15},"An AI Prompt with a Verifiable Output",{"type":11,"content":171},[172],{"text":173,"type":15},"Example prompt: 'Act as an instructional designer's assistant. Context: reading a short table of reading-room inquiries. Expected output: a written comparison of two weeks naming the unit, period, and conclusion boundaries. Learning action: compare stated counts and distinguish observed change from an assumption about its cause. Use only the card and materials below. For each proposal, identify the source fragment, specific change, reason, and checking method. Separate facts from assumptions and questions for the author. Do not invent delivery results, institutional rules, expert answers, or service functions. Offer two feasible options if the decision is genuinely ambiguous. Mark related materials needing updates. Leave final approval to the instructional designer.' Then insert the source card and current document version.",{"type":11},{"type":11,"content":176},[177],{"text":178,"type":15},"Read the first response for 'reading a short table of reading-room inquiries' as a list of proposals. Do not transfer it wholesale into the curriculum or task. First check whether every change concerns the selected outcome and relies on available input. Ask the model to shorten unnecessary explanations while retaining reasons and unknowns. Specify fields directly if another output structure is needed. AI can change the presentation of approved content. Check the document from the author's, instructor's, and reviewer's perspectives.",{"type":11},{"type":35,"attrs":181,"content":183},{"level":37,"id":182},"heading_2_10",[184],{"text":185,"type":15},"A Working-Version Approval Table",{"type":187,"attrs":188},"image_block",{"id":189,"alt":190,"url":191,"type":187,"float":192,"width":193,"preview":194},"960da980-eb89-4217-b422-deb9cf00bdb9","Stage | What the instructional designer checks | What should not be accepted; From intention to action | Name information supporting comparison. | Draw a conclusion about all library visits.; Outcome scope | Divide complex wording into checkable components. | Add causal analysis without causal data.; Criteria without hidden requirements | Align each criterion with a learning-outcome component. | Include attractive formatting as proof of numerical understanding.; Checking on new material | Retain the action while changing source counts. | Test a new mathematical operation without preparation.","https:\u002F\u002Fcloud.cs3.westudy.ua\u002Ffile?path=westudy_online%2Fpublic%2Fwestudy%2F97f4c7c5-9e69-4551-873a-c93162d74d34.png",null,"100%",true,{"type":11},{"type":11,"content":197},[198],{"text":199,"type":15},"The table for 'reading a short table of reading-room inquiries' does not replace substantive review. It connects comments to specific decisions and prevents losing them during formatting. If supplied material cannot verify an item, mark the clarification needed. Do not automatically mark readiness merely because AI filled every row. Keep the approved table beside the material. It passes current requirements to the next author.",{"type":11},{"type":35,"attrs":202,"content":204},{"level":37,"id":203},"heading_2_11",[205],{"text":206,"type":15},"A Working Trial Before Handoff",{"type":11,"content":208},[209],{"text":210,"type":15},"Imagine the first machine version for 'reading a short table of reading-room inquiries' looks complete but does not permit checking the action: compare stated counts and distinguish observed change from an assumption about its cause. Take one problematic fragment. Compare input information, the proposed decision, and required output. This is the author's checking demonstration rather than a completed experiment. Look for a specific gap in outcome alignment. One overall score will not explain the necessary revision.",{"type":11},{"type":11,"content":213},[214],{"text":215,"type":15},"For the first trial, perform the action using initial materials and record where a question arises. If an answer depends on an unknown, do not fill it by guessing. In the 'written comparison of two weeks naming the unit, period, and conclusion boundaries' package, leave a missing-information marker and a question for the responsible author. Run a second trial after specific clarification. Check whether the obstacle was removed and whether new dependencies appeared. This separates input-data problems from wording or structure problems.",{"type":11},{"type":11,"content":218},[219],{"text":220,"type":15},"Then ask a colleague to read the approved version without verbal explanation. For 'reading a short table of reading-room inquiries,' they should name the expected output, permitted supports, and reason for one decision. A difference between their interpretation and your intention is a reason to clarify the document. Do not force colleagues to guess the correct reading method. AI can compare interpretations. Clarify the conditions so colleagues can work without verbal hints.",{"type":11},{"type":35,"attrs":223,"content":225},{"level":37,"id":224},"heading_2_12",[226],{"text":227,"type":15},"Two Short Conclusions to Check",{"type":11,"content":229},[230],{"text":231,"type":15},"Compare 'there were six more inquiries in week two' with 'advertising increased demand.' The first matches the supplied counts, though a conclusion boundary should be added. The second introduces an unsourced causal explanation. Participants should show which table fragment supports each answer component. If none exists, mark the claim as an assumption rather than an established result. This check needs no harder statistics but does require disciplined data reading.",{"type":11},{"type":11,"content":234},[235],{"text":236,"type":15},"Also check measurement units when approving the outcome.",{"type":11},{"type":35,"attrs":239,"content":241},{"level":37,"id":240},"heading_2_13",[242],{"text":243,"type":15},"Frequently Asked Questions",{"type":35,"attrs":245,"content":248},{"level":246,"id":247},3,"heading_3_14",[249],{"text":250,"type":15},"Can the First AI Version Be Approved?",{"type":11,"content":252},[253],{"text":254,"type":15},"For 'reading a short table of reading-room inquiries,' the first version is a draft. Approve it after checking if it actually supports comparing stated counts and distinguishing observed change from an assumption about its cause. Finished formatting is not separate proof of correctness. Compare data, output, and criteria. Mark unconfirmed points and read without author hints. More subsequent generations do not themselves improve the result if requirements stay unclear.",{"type":11},{"type":35,"attrs":257,"content":259},{"level":246,"id":258},"heading_3_15",[260],{"text":261,"type":15},"What If AI Proposes Broader Content?",{"type":11,"content":263},[264],{"text":265,"type":15},"Return to the 'reading a short table of reading-room inquiries' boundaries and check whether the expected output needs the new content. If it creates another learning action, that requires a separate decision about curriculum, time, and prerequisites. Do not add it merely because the model's explanation is persuasive. Keep the additional idea for a later version. Complete the main package under current requirements. Extensions need instructional-designer approval reflected in every related material.",{"type":11},{"type":35,"attrs":268,"content":270},{"level":246,"id":269},"heading_3_16",[271],{"text":272,"type":15},"How Should Generator Work Be Saved?",{"type":11,"content":274},[275],{"text":276,"type":15},"For 'reading a short table of reading-room inquiries,' retain the approved document, source card, reasons for substantive changes, and open questions. A full chat may be a working trace but does not replace the current version. Another author should see which conditions are agreed and which proposals remain rejected. Update related tasks and keys when material changes. Version numbers and short change descriptions help history more than only the last generation date.",{"type":11},{"type":35,"attrs":279,"content":281},{"level":246,"id":280},"heading_3_17",[282],{"text":283,"type":15},"Does AI Checking Mean Publication Readiness?",{"type":11,"content":285},[286],{"text":287,"type":15},"For the 'written comparison of two weeks naming the unit, period, and conclusion boundaries,' machine checking is a supplementary pass. Confirm the substance and action feasibility. Compare examples and criteria separately. If unknowns or expert clarification remain, keep the status open. Do not claim course effectiveness proven before delivery. Keep design separate from actual learning outcomes. Both are needed for the next revision.",{"type":11},{"type":35,"attrs":290,"content":292},{"level":37,"id":291},"heading_2_18",[293],{"text":294,"type":15},"Handing Over the Approved Version",{"type":11,"content":296},[297],{"text":298,"type":15},"The completed working package for 'reading a short table of reading-room inquiries' contains a written comparison of two weeks naming the unit, period, and conclusion boundaries, source data, criteria, and open questions. Generator proposals enter material only after an instructional designer's decision. In WeStudy, hand over an agreed package with explicit status. Authors and instructors should see one current version. After conditions change, check dependent parts. The team retains substantive responsibility.",{"type":11},{"type":11,"content":301},[302],{"text":303,"type":15},"The WeStudy LMS platform can support organization of approved materials. Your process determines how it is used. This article assumes no specific automatic integrations or checking functions. For 'reading a short table of reading-room inquiries,' readiness depends on a clear output, sufficient data, and human approval. Keep drafts separate from approved versions. Plan checking during actual completion. ",{"type":11},{"type":35,"attrs":306,"content":308},{"level":37,"id":307},"heading_2_19",[309],{"text":310,"type":15},"Sources and Further Reading",{"type":312,"content":313},"bullet_list",[314],{"type":315,"content":316},"list_item",[317,323],{"type":11,"content":318},[319],{"text":25,"type":15,"marks":320},[321],{"type":28,"attrs":322},{"href":30},{"type":11},{"type":8,"content":325},[326],{"type":11,"content":327},[328],{"text":6,"type":15},1788527832,"https:\u002F\u002Fcloud.cs3.westudy.ua\u002Ffile?path=westudy_online%2Fpublic%2Fwestudy%2F4e5c7827-2e03-44db-bfd3-83fba98ab789.webp","152-ai-dlia-formuvannia-rezultativ-navchannia",false,"206d2883-c296-4cff-8de9-5738b1b92c75","AI для методистів","instructional-design","light","news.articles"]