LANG: A Lesson Plan Generation Framework via Multi-Form Interaction with Large Language Models

LANG: A Lesson Plan Generation Framework via Multi-Form Interaction with Large Language Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article LANG: A Lesson Plan Generation Framework via Multi-Form Interaction with Large Language Models Yong Ouyang, Jinhao Quan, Huanwen Wang, Yawen Zeng, Lingyu Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6808103/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lesson plans are essential tools for structuring and organizing the teaching process. However, the traditional approach to creating lesson plans demands considerable time and effort from teachers, as they must review extensive literature and collate relevant information. Thus, developing a technology capable of automatically generating lesson plans holds great significance. Such technology not only alleviates teachers' workloads but also enhances the efficiency of lesson preparation. This paper addresses this need by leveraging the capabilities of large language models (LLMs) and improving the quality of generated lesson plans through various input forms, enhanced generation methods, and teacher-interaction mechanisms. In particular, we present the ''Plan10k'' dataset-a high-quality, bilingual collection of lesson plans. Based on this dataset, we propose the LANG framework, which comprises three key modules: a query rewriting module, a lesson plan generation module, and a chapter correction module. The query rewriting module processes diverse teacher inputs, including specific knowledge points and textbooks, ensuring contextual accuracy. The lesson plan generation module then constructs comprehensive lesson plans, while the chapter correction module integrates retrieval tools to rectify errors and improve output quality. Importantly, the framework supports multiple forms of interaction with intermediate results, offering teachers flexibility and control over the generation process. Extensive experiments validate the effectiveness of our framework, demonstrating significant improvements in performance and high teacher satisfaction. All codes and datasets are publicly available at https://github.com/ssakana/LANG . Lesson Plan Generation AI-assisted Education Chapter Correction Large Language Models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6808103","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472140887,"identity":"10f38a12-7422-492d-9ad7-ce3081545efc","order_by":0,"name":"Yong Ouyang","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Ouyang","suffix":""},{"id":472140888,"identity":"e62ba8f9-b97a-40c7-ab1e-a39a4d4f0629","order_by":1,"name":"Jinhao Quan","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jinhao","middleName":"","lastName":"Quan","suffix":""},{"id":472140889,"identity":"faf292e1-7d83-4634-a298-388a3224f3f4","order_by":2,"name":"Huanwen Wang","email":"","orcid":"","institution":"Hunan University","correspondingAuthor":false,"prefix":"","firstName":"Huanwen","middleName":"","lastName":"Wang","suffix":""},{"id":472140890,"identity":"16661483-7d66-4170-98fd-55022a870891","order_by":3,"name":"Yawen Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYFAD9h42COMA0Vp4zgC1JJCkRSKHSC0Gx88YPi74Y5MnH/n22GPeHwxyfDcSGD8X4NNyJsfYeAZPWrHh7bx0Y54EBmPJGwnM0jPwaTmQu02aR+Jw4sbZOWbSQC2JG24ksDHz4NNy/u323zwG/xM3zjwD1lJPWMuN3G3MPAkHEudL8IC1JBgQ0iJ54/1naZ4DyYkbeHLMDeekSRjOPPOwWRqfFr7zaYmfef7YJc5vP2P24I2NjTzf8eSDn/FpUTgAcyGEIQHEjA14NDAwyDegM0bBKBgFo2AUoAMAYmlMajjr4SQAAAAASUVORK5CYII=","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yawen","middleName":"","lastName":"Zeng","suffix":""},{"id":472140891,"identity":"dbe6db9c-afcf-45db-9379-4ab63b860c3e","order_by":4,"name":"Lingyu Chen","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lingyu","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-03 07:23:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6808103/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6808103/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85897567,"identity":"f9f1105a-ea15-4178-87ad-c71ab07bc5b1","added_by":"auto","created_at":"2025-07-03 00:16:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1482063,"visible":true,"origin":"","legend":"","description":"","filename":"LANG3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6808103/v1_covered_469167bc-076d-4286-8dda-5300c282333d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"LANG: A Lesson Plan Generation Framework via Multi-Form Interaction with Large Language Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lesson Plan Generation, AI-assisted Education, Chapter Correction, Large Language Models","lastPublishedDoi":"10.21203/rs.3.rs-6808103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6808103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLesson plans are essential tools for structuring and organizing the teaching process. However, the traditional approach to creating lesson plans demands considerable time and effort from teachers, as they must review extensive literature and collate relevant information. Thus, developing a technology capable of automatically generating lesson plans holds great significance. Such technology not only alleviates teachers' workloads but also enhances the efficiency of lesson preparation. This paper addresses this need by leveraging the capabilities of large language models (LLMs) and improving the quality of generated lesson plans through various input forms, enhanced generation methods, and teacher-interaction mechanisms. In particular, we present the ''Plan10k'' dataset-a high-quality, bilingual collection of lesson plans. Based on this dataset, we propose the LANG framework, which comprises three key modules: a query rewriting module, a lesson plan generation module, and a chapter correction module. The query rewriting module processes diverse teacher inputs, including specific knowledge points and textbooks, ensuring contextual accuracy. The lesson plan generation module then constructs comprehensive lesson plans, while the chapter correction module integrates retrieval tools to rectify errors and improve output quality. Importantly, the framework supports multiple forms of interaction with intermediate results, offering teachers flexibility and control over the generation process. Extensive experiments validate the effectiveness of our framework, demonstrating significant improvements in performance and high teacher satisfaction. 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