[{"data":1,"prerenderedAt":650},["ShallowReactive",2],{"uc-teacher-lesson-planning-and-resource-assistant":3,"uc-regulations":429},{"useCase":4,"evidence":200,"blitsAiDeployments":314,"benchmarks":315,"indicative":327,"related":330,"indexability":427,"includeUnpublished":206},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":43,"valueDrivers":44,"kpis":47,"indicativeValue":51,"macroEstimates":85,"feasibility":86,"implementation":97,"risk":143,"blitsAi":180,"faq":182,"related":192,"datePublished":195,"dateModified":195,"lastVerified":195,"changelog":196,"slug":199},"AI assistant for teachers: lesson planning and classroom resources","Teacher lesson planning assistant","AI lesson planning assistant for teachers","AI lesson assistants draft plans, quizzes and worksheets. An EEF trial found Oak National Academy's Aila saved primary teachers about 49 minutes a week.","published","An AI lesson planning assistant is software that drafts lesson plans, slides, quizzes and worksheets from a teacher's learning goal, which the teacher edits and checks before using in class. It works from the school's curriculum and leaves teaching, marking and every pupil facing decision to the teacher.",[12,13,14,15],"AI lesson planner","AI lesson assistant","AI teaching assistant for teachers","teacher AI copilot",[17],"education",[19,20],"operations","knowledge-management",[22,23,24],"content-generation","rag-knowledge-assistant","conversational-agent",[26],"web-chat","employee-facing","copilot","early-adopters","schools","Planning lessons and preparing resources is a large, steady part of a teacher's week. In the\nrandomized trial of Oak National Academy's Aila, teachers who did not use it spent about three\nhours 19 minutes a week on lesson and resource preparation, and the evaluation found that almost\nall participating teachers regarded lesson planning as a source of stress and felt they spent too\nmuch time on it. The New South Wales government ties its work on the teacher shortage to a commitment to\ncut teachers' administrative work by five hours a week.\n\nA general chatbot can draft a worksheet in seconds, but the draft has to fit the curriculum, the\nyear group and the reading level of the class, and the teacher has to check it before pupils see it.\nThe job of a lesson assistant is to produce a first draft that is close enough to the school's own\nscheme of work to be worth editing, and to cover the adjacent chores that eat time, such as\ndifferentiating a resource for pupils of different ability or checking a newsletter.\n\nThe evidence so far is modest and uneven. An independent randomized trial of Aila found a saving of\naround 49 minutes a week and resources that were no worse, with light use that declined over ten\nweeks. Education departments in New South Wales and Singapore have rolled out their own assistants\nto teachers, and we found no measured average saving published by either.",[33,38],{"statement":34,"sourceTitle":35,"sourceUrl":36,"year":37},"The EEF commissioned evaluation of Oak National Academy's Aila, run by NFER, found that almost all participating teachers regarded lesson planning as a source of stress and felt they spent too much time on it, as reported by EdTech Innovation Hub.","Oak's Aila saves primary teachers 49 minutes a week on lesson planning, EEF trial finds","https:\u002F\u002Fwww.edtechinnovationhub.com\u002Fnews\u002Foaks-aila-saves-primary-teachers-49-minutes-a-week-on-lesson-planning-eef-trial-finds",2026,{"statement":39,"sourceTitle":40,"sourceUrl":41,"year":42},"The New South Wales government says it is committed to reducing the admin load on teachers by five hours per week, and describes NSWEduChat as one tool aimed at routine administrative tasks.","Expansion of AI education tool trial delivers more time for teachers in our classrooms","https:\u002F\u002Fwww.nsw.gov.au\u002Fmedia-releases\u002Fexpansion-of-ai-education-tool-trial-delivers-more-time-for-teachers-our-classrooms",2024,"1. **The teacher states the goal.** The teacher starts from a learning outcome, a year group and a\n   subject, and can add what the class already knows or the context of the school.\n2. **The assistant drafts from curriculum content.** It builds sections, activities and components\n   for a lesson, drawing on the curriculum aligned material the school or education body provides.\n   Oak's Aila produces a lesson plan, a slide deck, two quizzes and a worksheet for each lesson;\n   Singapore's Authoring Copilot proposes sections, activities and components in any subject and\n   level.\n3. **The teacher edits step by step.** Content can be added or removed, the reading age lowered or\n   the context changed, and the files stay fully editable.\n4. **The assistant adapts what the teacher already has.** A resource can be re levelled for pupils\n   of different ability or adjusted to their interests, and a letter or newsletter can be checked.\n   The Aila trial found that teachers often wanted to adapt existing material rather than start from\n   scratch. Oak says it is now bringing Aila's capabilities into its existing lessons and resources,\n   and NSWEduChat's teacher version is aimed at adjusting resources to different ability levels.\n5. **The teacher uses the result in class.** The draft is a starting point: the teacher checks it for\n   accuracy, sequencing and suitability for the class, and the teacher decides what to teach and how to teach it.",[45,46],"employee-productivity","speed",[48,49,50],"employee-adoption","hours-saved","productivity-gain",{"referenceOrg":52,"inputs":53,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A primary school in England with 20 classroom teachers",[54,59,66,73],{"key":55,"label":56,"low":57,"high":57,"unit":55,"note":58},"teachers","Classroom teachers who have the assistant",20,"The reference school.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65,"sourceUrl":36},"minutesSaved","Minutes of planning time saved per teacher per week",15,49,"minutes per teacher per week","The high end is the average in the EEF commissioned Aila trial with primary teachers (around 49 minutes a week over ten weeks, reported by EdTech Innovation Hub). The low end allows for the light and declining use seen in that trial. Editorial assumption, replace with your own time diary data.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"weeks","Teaching weeks per year",36,39,"weeks per year","Editorial assumption, replace with your school calendar.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"hourlyCost","Cost of one teacher hour",25,40,"GBP per hour","Editorial assumption for a fully loaded teacher hour. Replace with your own figure.","teachers * minutesSaved \u002F 60 * weeks * hourlyCost","GBP","per year","Value of teacher planning time released","The result prices time, not cash: nobody's pay falls and the hours only matter if teachers use them on pupils or on rest. It leaves out licence or build cost, training, the time teachers spend checking and correcting drafts, and any effect on lesson quality, which the trial measured only as \"no worse\". The trial covered primary teachers over one autumn term, so secondary schools and longer periods may differ.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":93},"low","A planning assistant needs the school's or the education body's curriculum documents, staff sign in through the school's identity provider and a data protection impact assessment for staff accounts. No pupil data, payment or core system is involved when it is used for planning.",[90,91,92],"The curriculum, schemes of work and exemplar lessons the assistant should draw on, with the right to use them","School policies that affect resources, such as reading age, accessibility and safeguarding wording","A baseline of how long teachers spend on planning, from a short diary or survey",[94,95,96],"School or district identity provider for single sign on","Document storage or learning platform where teachers keep and share resources","Staff messaging channel such as Microsoft Teams, if teachers should reach it there",{"steps":98,"guardrails":117,"humanInTheLoop":123,"kpisToInstrument":124,"failureModes":130},[99,102,105,108,111,114],{"title":100,"detail":101},"Start with the teachers most likely to gain","The Aila trial found the largest time savings among early career teachers, those less confident in their subject or teaching knowledge and those comfortable with technology. Offer the tool, with training, to those groups first and ask other teachers to opt in.",{"title":103,"detail":104},"Ground the assistant in your curriculum","Index the scheme of work, exemplar lessons and approved resources, so drafts use the school's sequence, vocabulary and examples, and make the assistant say when the curriculum does not cover a request.",{"title":106,"detail":107},"Support adapting existing material as well as planning from scratch","In the Aila trial teachers often adapted material they already had, and the evaluation links the low share of lessons planned with the tool (about 7% in a typical week) to a design built around lessons from scratch. Let teachers paste in or upload a resource and ask for a differentiated version, a shorter version or a quiz.",{"title":109,"detail":110},"Agree what teachers must check","The EEF cautions that saving time does not automatically mean better teaching and that teachers still need to check AI drafts for accuracy, sequencing and suitability. Give them a short checklist and make clear that the teacher owns every resource.",{"title":112,"detail":113},"Keep pupil information out","Planning needs no names or personal data. Block pupil identifiers from prompts and tell teachers not to paste in reports or assessment data, unless the school has assessed that use.",{"title":115,"detail":116},"Measure against a baseline for a full term","Oak's own earlier surveys suggested bigger savings than the independent trial found, so use a time diary against a baseline or comparison group. Track weekly use as well, because use declined over the ten weeks of the trial.",[118,119,120,121,122],"Staff accounts only, through the school's single sign on","No pupil names or personal data in prompts, with masking where staff paste in text","Drafts grounded in approved curriculum content, with a clear message when it does not cover a request","Age and reading level settings, and content filters suitable for school","A visible AI label on every draft until a teacher has reviewed it","The teacher is the author of every resource and checks it before it reaches pupils. Subject leads review a sample of AI drafts each term for accuracy and curriculum fit, and the school decides which uses are out of scope, such as writing reports or marking pupils' work.",[125,126,127,128,129],"Minutes spent on planning and resource preparation per teacher per week, against the baseline","Weekly active teachers and the share of lessons planned with the assistant, tracked for the whole term","Share of drafts edited before use and the kinds of edits teachers make","Quality of resources on a blind sample reviewed by subject leads","Teacher views on workload and on whether the tool changed how they plan",[131,134,137,140],{"title":132,"detail":133},"Light use that fades","The trial saw 81% of the Aila group open the tool but only about 7% of lessons planned with it in a typical week, with use falling over ten weeks. Put the tool inside the resources teachers already use and support adaptation.",{"title":135,"detail":136},"Plausible errors reach pupils","A fluent draft can contain a wrong fact, an out of sequence step or an unsuitable example. Ground drafts in the curriculum, require a teacher check and sample resources for accuracy.",{"title":138,"detail":139},"Time saved with nothing gained","The EEF notes that saving planning time does not by itself improve teaching. Agree up front where released time should go, such as feedback or small group work, and review that too.",{"title":141,"detail":142},"Language and subject gaps","One Singapore teacher reported that the Authoring Copilot could not yet read Chinese, so she translated her inputs into English. Test the assistant in every subject and language the school teaches before rollout.",{"euAiAct":144,"regulations":147,"guidance":151,"controls":173,"incidents":179},{"tier":145,"basis":146},"minimal","A tool that only drafts planning materials for teachers is not listed in Annex III. Annex III point 3 covers education systems that determine access or admission, evaluate learning outcomes, assess the level of education a person will receive or monitor prohibited behaviour in tests. The assistant would become high risk under point 3(b) if it were used to evaluate pupils' work or steer a pupil's learning on the basis of its outcomes. The AI literacy duty in Article 4 applies to schools as deployers.",[148,149,150],"eu-ai-act","gdpr","uk-gdpr",[152,158,164,169],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Guidance for generative AI in education and research","UNESCO","global","https:\u002F\u002Fwww.unesco.org\u002Fen\u002Farticles\u002Fguidance-generative-ai-education-and-research","UNESCO's global guidance on generative AI in education; it proposes mandating the protection of data privacy and setting an age limit for independent conversations with GenAI platforms.",{"title":159,"issuer":160,"region":161,"url":162,"note":163},"Generative artificial intelligence (AI) in education","UK Department for Education","europe","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fpublications\u002Fgenerative-artificial-intelligence-in-education\u002Fgenerative-artificial-intelligence-ai-in-education","The department's position on generative AI in schools and colleges; it states that it has funded Oak National Academy to develop AI tools that speed up lesson planning and reduce workloads.",{"title":165,"issuer":166,"region":161,"url":167,"note":168},"Article 4, AI literacy","European Union","https:\u002F\u002Fartificialintelligenceact.eu\u002Farticle\u002F4\u002F","Providers and deployers of AI systems must take measures to support the AI literacy of their staff and of other persons dealing with the systems on their behalf.",{"title":170,"issuer":166,"region":161,"url":171,"note":172},"Annex III, high risk AI systems (point 3, education and vocational training)","https:\u002F\u002Fartificialintelligenceact.eu\u002Fannex\u002F3\u002F","Lists AI used to determine access or admission, to evaluate learning outcomes, and to assess the level of education a person will receive as high risk, which marks the line a planning assistant should not cross.",[174,175,176,177,178],"A data protection impact assessment for staff accounts, covering prompts and logs","An approved tools list and a school policy on what teachers may and may not put into the assistant","Staff training on AI use and on checking drafts, as part of the school's AI literacy measures","Inventory entry with an accountable owner and a review date","A termly sample check of drafts for accuracy and curriculum fit",[],{"howToBuild":181},"On Blits.ai a teacher assistant is an **AI agent** with a teacher persona and prompt versioning,\ngrounded in a **knowledge base** that holds the school's scheme of work, exemplar lessons and\napproved resources, retrieved with hybrid search. The agent uses the built in **file generation**\ntool to hand back the lesson plan, quiz or worksheet as a file the teacher can edit, and a\nresource a teacher uploads can be adapted in the same conversation. **Output guardrails** with an\nadmin authored policy check drafts for age suitability, and **PII masking** keeps pupil names out\nof any model prompt.\n\nTeachers reach it in **Microsoft Teams**, in the **web chat** widget inside the school portal, or\nthrough the **REST or WebSocket API** channel in the learning platform. **Test suites** with LLM\ngrading replay sample requests from each subject after every prompt or model change, and\n**analytics** and conversation logs show weekly use, so a school can see whether use holds up past\nthe first weeks. The platform is model agnostic and supports EU data residency.",[183,186,189],{"question":184,"answer":185},"How much time does an AI lesson assistant save teachers?","An independent randomized trial found that primary teachers in England who were assigned to Oak's Aila spent around 49 minutes a week less on lesson and resource preparation than a comparison group, with resources judged no worse. The tool was used for only about 7% of lessons in a typical week and use declined over ten weeks. The NSW government says some teachers saved over an hour a week with NSWEduChat, but that is a report from some teachers and not an average.",{"question":187,"answer":188},"Which teachers benefit most?","In the Aila trial, gains were largest for early career teachers, for teachers less confident in their subject or teaching knowledge and for teachers comfortable with technology. Fewer than one in five teachers said the tool had meaningfully changed how they taught or planned, and many preferred other AI tools.",{"question":190,"answer":191},"Is an AI lesson planning assistant high risk under the EU AI Act?","Usually not. Drafting planning materials for teachers is not an Annex III use. It would become high risk under Annex III point 3(b) if it were used to evaluate pupils' work or to steer a pupil's learning on the basis of outcomes, so keep marking and placement decisions out of the assistant. Schools still need to meet the AI literacy duty in Article 4.",[193,194],"ai-tutor-for-students","automated-scoring-of-written-responses","2026-10-13",[197],{"date":195,"note":198},"First published","teacher-lesson-planning-and-resource-assistant",[201,233,286],{"title":202,"useCases":203,"organization":204,"vendors":210,"summary":213,"stage":214,"year":42,"channels":215,"languages":216,"metrics":218,"outcomeDisclosed":219,"sources":220,"verification":228,"grade":230,"id":231,"organizationSlug":232},"NSW Department of Education: NSWEduChat generative AI assistant for teachers",[199],{"name":205,"anonymized":206,"country":207,"region":208,"industry":17,"wikidata":209},"NSW Department of Education",false,"AU","asia-pacific","Q5260271",[211],{"name":205,"role":212},"in-house","The NSW Department of Education built NSWEduChat, a generative AI app with separate versions for teachers and students, hosted in the department's own cloud environment. The teacher version aims to cut time spent on routine administrative work: producing classroom resources at different ability levels, adjusting resources to students' interests, and checking correspondence and newsletters. It started in 16 schools at the start of 2024, grew to 50 schools in Term 2 and was announced for all public school teachers from the start of Term 4, with optional use. A September 2025 department release says the teacher version had been rolled out to all schools. The department reports positive trial feedback, including that some teachers saved over an hour a week producing lesson resources, and later said surveys show the teacher version saves time in producing resources for different ability levels. It has not published a measured average.","scaled",[26],[217],"en",[],true,[221,224],{"url":41,"title":40,"publisher":222,"date":223},"NSW Government","2024-09-16",{"url":225,"title":226,"publisher":205,"date":227},"https:\u002F\u002Feducation.nsw.gov.au\u002Fnews\u002Flatest-news\u002Froll-out-of-purpose-built-ai-education-tool-to-all-public-school","Roll out of purpose built AI education tool to NSW public school students","2025-09-23",{"level":229,"checkedAt":195},"source-verified","B","nsw-department-of-education-nsweduchat-teacher-assistant",null,{"title":234,"useCases":235,"organization":236,"vendors":240,"summary":242,"stage":243,"year":244,"channels":245,"languages":246,"metrics":247,"outcomeDisclosed":219,"sources":263,"verification":283,"grade":284,"id":285,"organizationSlug":232},"Oak National Academy: Aila AI lesson assistant, tested in a randomized trial with primary teachers",[199],{"name":237,"anonymized":206,"country":238,"region":161,"industry":17,"wikidata":239},"Oak National Academy","GB","Q97358592",[241],{"name":237,"role":212},"Oak National Academy, which publishes free curriculum resources for teachers in England and which the Department for Education says it funded to develop AI tools for teachers, launched its AI lesson assistant Aila in September 2024. A teacher states what to teach and works step by step with Aila, which draws on Oak's curriculum aligned content to produce an editable lesson plan, slide deck, quizzes and worksheet. The Education Endowment Foundation commissioned the National Foundation for Educational Research to run a ten week randomized trial in autumn 2025 with teachers of Years 3 to 6 in England. Teachers assigned to Aila spent around 49 minutes less per week on lesson and resource preparation than the comparison group, and independent assessors judged their resources no worse. Use was light: 81% of the Aila group opened it at least once, but it supported only around 7% of lessons in a typical week and use declined over the ten weeks. Gains were largest for early career teachers, and trade press reports that Oak's chief executive said Oak's own earlier surveys had suggested bigger savings than the trial found.","production",2025,[26],[217],[248,256],{"kpi":49,"value":249,"unit":250,"qualifier":251,"period":252,"baseline":253,"claimant":254,"quote":255,"sourceUrl":36},0.8,"hours","approximately","per teacher per week","Comparison group of teachers not assigned to Aila, who spent about 3 hours 19 minutes a week on lesson and resource preparation","independent","Oak National Academy’s AI lesson assistant Aila saved primary teachers an average of around 49 minutes a week on lesson and resource preparation without reducing the quality of the materials they produced, according to a new independent randomized controlled trial.",{"kpi":50,"value":257,"unit":258,"qualifier":259,"baseline":260,"claimant":254,"quote":261,"sourceUrl":262},24,"percent","exact","Comparison group of teachers not assigned to Aila, measured as time spent preparing lessons","Primary teachers who used Aila, the AI lesson assistant from Oak National Academy, spent 24% less time preparing lessons, with no drop in the quality of what they produced, according to a randomised trial whose results were published on Tuesday 6 October.","https:\u002F\u002Fwww.resultsense.com\u002Fnews\u002F2026-10-06-eef-trial-oak-aila-lesson-planning-time\u002F",[264,267,271,275,278,279],{"url":36,"title":35,"publisher":265,"date":266},"EdTech Innovation Hub","2026-10-07",{"url":262,"title":268,"publisher":269,"date":270},"Oak's Aila AI tool cuts teacher planning time by 24%","Resultsense","2026-10-06",{"url":272,"title":273,"publisher":274},"https:\u002F\u002Fwww.nfer.ac.uk\u002Ffor-schools\u002Fparticipate-in-research\u002Fparticipate-in-research-projects\u002Flesson-planning-using-ai-lesson-assistant-aila-a-teacher-choices-trial\u002F","Lesson planning using AI lesson assistant, Aila - A Teacher Choices Trial","National Foundation for Educational Research (NFER)",{"url":276,"title":277,"publisher":237},"https:\u002F\u002Flabs.thenational.academy\u002F","Oak AI Experiments, Aila",{"url":162,"title":159,"publisher":160},{"url":280,"title":281,"publisher":282},"https:\u002F\u002Fassets.publishing.service.gov.uk\u002Fmedia\u002F692f11daa245b0985f034427\u002FOak_Annual-report-and-accounts_2024-25.pdf","Oak National Academy annual report and accounts 2024 to 2025","Oak National Academy, published on GOV.UK",{"level":229,"checkedAt":195},"C","oak-national-academy-aila-lesson-assistant",{"title":287,"useCases":288,"organization":289,"vendors":293,"summary":298,"stage":243,"year":42,"channels":299,"languages":300,"metrics":301,"outcomeDisclosed":219,"sources":302,"verification":312,"grade":284,"id":313,"organizationSlug":232},"Singapore Ministry of Education: Authoring Copilot for lesson planning on the Student Learning Space",[199],{"name":290,"anonymized":206,"country":291,"region":208,"industry":17,"wikidata":292},"Ministry of Education, Singapore","SG","Q4294530",[294,295],{"name":290,"role":212},{"name":296,"role":297},"GovTech Singapore","platform","Singapore's Ministry of Education rolled out the Authoring Copilot in June 2024 on the Student Learning Space, the national online learning portal for all MOE schools. A teacher gives the tool an input and it proposes sections, activities and components for a lesson in any subject and level, generating draft lesson ideas and learning resources that the teacher adapts. A second teacher tool on the same portal, the Data Assistant, analyses students' written responses to surface common themes and misconceptions. GovTech Singapore describes both as built to augment teachers, with human in the loop or human over the loop moderation. MOE has published no figure for teacher time saved. One primary school teacher quoted by The Straits Times says she saves a lot of time in lesson planning with it, and notes that the tool could not yet read Chinese, so she translates her inputs into English.",[26],[217],[],[303,308],{"url":304,"title":305,"publisher":306,"date":307},"https:\u002F\u002Fwww.tech.gov.sg\u002Ftechnews\u002Finside-singapore-digital-classroom-how-ai-is-supporting-teachers-and-students\u002F","Inside Singapore's digital classroom: How AI is supporting teachers and students","Government Technology Agency of Singapore (GovTech Singapore)","2025-12-09",{"url":309,"title":310,"publisher":311},"https:\u002F\u002Fntu.edu.sg\u002Fnie\u002Fnews-events\u002Fnews\u002Fdetail\u002Fmoe-s-newest-ai-tools-and-how-schools-are-using-them","MOE's newest AI tools and how schools are using them","National Institute of Education, NTU Singapore (reprint of a Straits Times article)",{"level":229,"checkedAt":195},"singapore-moe-authoring-copilot-lesson-planning",0,[316,322],{"kpi":49,"label":317,"unit":250,"aggregate":206,"higherIsBetter":219,"n":318,"nUpTo":314,"median":249,"min":249,"max":249,"byClaimant":319,"vendorOnly":206,"points":320},"Hours saved",1,{"organization":314,"vendor":314,"regulator":314,"independent":318},[321],{"evidenceId":285,"organization":237,"value":249,"qualifier":251,"claimant":254,"grade":284,"pooled":219},{"kpi":50,"label":323,"unit":258,"aggregate":219,"higherIsBetter":219,"n":318,"nUpTo":314,"median":257,"min":257,"max":257,"byClaimant":324,"vendorOnly":206,"points":325},"Productivity gain",{"organization":314,"vendor":314,"regulator":314,"independent":318},[326],{"evidenceId":285,"organization":237,"value":257,"qualifier":259,"claimant":254,"grade":284,"pooled":219},{"low":328,"high":329},4500,25480,[331,352,370,396],{"slug":193,"title":332,"shortTitle":333,"definition":334,"status":9,"industries":335,"functions":336,"patterns":338,"audience":339,"autonomy":340,"adoptionStage":341,"evidenceCount":342,"publicEvidenceCount":342,"organizations":343,"bestGrade":230,"headline":232,"lastVerified":351,"indexable":219},"AI tutor that coaches students through problems","AI tutor for students","An AI tutor for students is a tool that works with a student on course material in conversation, asking questions and giving hints instead of handing over answers. It is grounded in the course content, set up by the school or teacher, with limits on use and a clear route to a human teacher.",[17],[337],"customer-service",[24,23],"customer-facing","assist","emerging",7,[344,345,346,347,348,349,350],"Brockton Public Schools","Eedi","Hamilton County Schools","Harvard University","Muleshoe Independent School District","Spokane Public Schools","World Bank","2026-09-27",{"slug":194,"title":353,"shortTitle":354,"definition":355,"status":9,"industries":356,"functions":358,"patterns":359,"audience":362,"autonomy":363,"adoptionStage":364,"evidenceCount":365,"publicEvidenceCount":365,"organizations":366,"bestGrade":230,"headline":232,"lastVerified":351,"indexable":219},"AI scoring of essays and written answers in assessments","Essay and written answer scoring","AI scoring of written responses is AI that scores students' essays and short written answers against a rubric for a testing programme. It is trained on responses scored by human raters, with human raters rescoring a sample of responses and every response the engine is unsure about; in hybrid programmes such as Texas, a human score is the score of record whenever a human scores a response.",[17,357],"government",[19],[360,361],"classification-and-routing","prediction-and-scoring","back-office","supervised-agent","mainstream",3,[367,368,369],"ETS","Massachusetts Department of Elementary and Secondary Education","Texas Education Agency",{"slug":371,"title":372,"shortTitle":373,"definition":374,"status":9,"industries":375,"functions":378,"patterns":379,"audience":27,"autonomy":340,"adoptionStage":29,"segment":243,"evidenceCount":382,"publicEvidenceCount":382,"organizations":383,"bestGrade":230,"headline":389,"lastVerified":351,"indexable":219},"plant-operator-and-maintenance-copilot","AI copilot for plant operators and maintenance technicians","Plant operator and maintenance copilot","A plant operator and maintenance copilot is a generative AI assistant for the people who run and repair machines in plants, workshops and service centres. It answers fault and procedure questions from equipment manuals, fault reports, shift logs and live machine data, in the technician's language, with links to the sources, so faults are diagnosed faster and expert knowledge is not lost when experienced staff retire.",[376,377],"manufacturing","automotive",[19,20],[23,24,380,381],"summarization","translation",5,[384,385,386,387,388],"BMW Group","Daikin Industries","Georgia-Pacific","Husqvarna","Textron Aviation",{"kpi":390,"label":391,"unit":258,"n":318,"nUpTo":314,"kind":392,"value":393,"qualifier":394,"claimant":395,"organization":385,"vendorReported":206},"accuracy","Accuracy","reported",90,"at-least","organization",{"slug":397,"title":398,"shortTitle":399,"definition":400,"status":9,"industries":401,"functions":407,"patterns":408,"audience":27,"autonomy":340,"adoptionStage":364,"evidenceCount":409,"publicEvidenceCount":409,"organizations":410,"bestGrade":230,"headline":424,"lastVerified":351,"indexable":219},"enterprise-knowledge-search","AI enterprise knowledge search for employees","Enterprise knowledge search","Enterprise knowledge search is an AI assistant that lets any employee ask a question in plain language and get a synthesized answer from the organization's own documents. It draws the answer from policies, procedures, product manuals and research, with citations to the source documents, and only from documents the employee is allowed to see.",[402,403,404,405,357,406],"cross-industry","banking","wealth-and-asset-management","insurance","professional-services",[20,19,337],[23,24,380],13,[411,412,413,414,415,416,417,418,419,420,421,422,423],"All Nippon Airways","Bank of America","Booking.com","Cathay Financial Holdings","DZ BANK AG","Insurance Australia Group","Morgan Stanley","PLDT","Raiffeisen Bank International AG","SIGNAL IDUNA","Wells Fargo","Zain Jordan","Zillow",{"kpi":48,"label":425,"unit":258,"n":426,"nUpTo":314,"kind":392,"value":393,"qualifier":394,"claimant":395,"organization":422,"vendorReported":206},"Employee adoption",2,{"indexable":219,"reasons":428},[],[430,435,440,447,455,461,467,474,481,488,495,501,507,514,521,527,533,539,545,550,555,562,568,575,580,585,591,596,603,608,615,621,627,633,639,644],{"id":148,"label":431,"issuer":166,"region":161,"url":432,"description":433,"useCases":434,"indexable":219},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":149,"label":436,"issuer":166,"region":161,"url":437,"description":438,"useCases":439,"indexable":219},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":441,"label":442,"issuer":443,"region":155,"url":444,"description":445,"useCases":446,"indexable":219},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":448,"label":449,"issuer":450,"region":451,"url":452,"description":453,"useCases":454,"indexable":219},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":150,"label":456,"issuer":457,"region":161,"url":458,"description":459,"useCases":460,"indexable":219},"UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":462,"label":463,"issuer":166,"region":161,"url":464,"description":465,"useCases":466,"indexable":219},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":468,"label":469,"issuer":470,"region":161,"url":471,"description":472,"useCases":473,"indexable":219},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":475,"label":476,"issuer":477,"region":208,"url":478,"description":479,"useCases":480,"indexable":219},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":482,"label":483,"issuer":484,"region":208,"url":485,"description":486,"useCases":487,"indexable":219},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":489,"label":490,"issuer":491,"region":451,"url":492,"description":493,"useCases":494,"indexable":219},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":496,"label":497,"issuer":166,"region":161,"url":498,"description":499,"useCases":500,"indexable":219},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":502,"label":503,"issuer":504,"region":155,"url":505,"description":506,"useCases":500,"indexable":219},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":508,"label":509,"issuer":510,"region":161,"url":511,"description":512,"useCases":513,"indexable":219},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":515,"label":516,"issuer":517,"region":451,"url":518,"description":519,"useCases":520,"indexable":219},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":522,"label":523,"issuer":524,"region":155,"url":525,"description":526,"useCases":62,"indexable":219},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":528,"label":529,"issuer":166,"region":161,"url":530,"description":531,"useCases":532,"indexable":219},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":534,"label":535,"issuer":536,"region":451,"url":537,"description":538,"useCases":532,"indexable":219},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":540,"label":541,"issuer":542,"region":451,"url":543,"description":544,"useCases":409,"indexable":219},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",{"id":546,"label":547,"issuer":166,"region":161,"url":548,"description":549,"useCases":409,"indexable":219},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":551,"label":552,"issuer":166,"region":161,"url":553,"description":554,"useCases":409,"indexable":219},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":556,"label":557,"issuer":558,"region":451,"url":559,"description":560,"useCases":561,"indexable":219},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":563,"label":564,"issuer":565,"region":155,"url":566,"description":567,"useCases":561,"indexable":219},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":569,"label":570,"issuer":571,"region":161,"url":572,"description":573,"useCases":574,"indexable":219},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":576,"label":577,"issuer":166,"region":161,"url":578,"description":579,"useCases":574,"indexable":219},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":581,"label":582,"issuer":166,"region":161,"url":583,"description":584,"useCases":574,"indexable":219},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":586,"label":587,"issuer":477,"region":208,"url":588,"description":589,"useCases":590,"indexable":219},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":592,"label":593,"issuer":166,"region":161,"url":594,"description":595,"useCases":590,"indexable":219},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":597,"label":598,"issuer":599,"region":451,"url":600,"description":601,"useCases":602,"indexable":219},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",8,{"id":604,"label":605,"issuer":166,"region":161,"url":606,"description":607,"useCases":342,"indexable":219},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":609,"label":610,"issuer":611,"region":612,"url":613,"description":614,"useCases":382,"indexable":219},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":616,"label":617,"issuer":166,"region":161,"url":618,"description":619,"useCases":620,"indexable":219},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",4,{"id":622,"label":623,"issuer":624,"region":161,"url":625,"description":626,"useCases":620,"indexable":219},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":628,"label":629,"issuer":630,"region":161,"url":631,"description":632,"useCases":620,"indexable":219},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":634,"label":635,"issuer":636,"region":208,"url":637,"description":638,"useCases":365,"indexable":219},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":640,"label":641,"issuer":166,"region":161,"url":642,"description":643,"useCases":365,"indexable":219},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":645,"label":646,"issuer":647,"region":451,"url":648,"description":649,"useCases":365,"indexable":219},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904355360]