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AI BASED SMART TUTOR

Prof. Vinutha Raghu1 , Shravanth R Naik2, Sandeep Chavhan3 , Nandish N4 , Sanjay N5

1Assistant Professor, ISE, Acharya Institute of Technology, Bengaluru, Karnataka, India

2B.E Student, ISE, Acharya Institute of Technology, Bengaluru, Karnataka, India

3B.E Student, ISE, Acharya Institute of Technology, Bengaluru, Karnataka, India

4B.E Student, ISE, Acharya Institute of Technology, Bengaluru, Karnataka, India

5B.E Student, ISE, Acharya Institute of Technology, Bengaluru, Karnataka, India ***

Abstract

This paper explores how an AI-based online tutor addresses the limitations of traditional tutoring by providingpersonalized,scalable,andaccessiblelearning experiences. Utilizing natural language processing and machine learning, it offers dynamic subject material, interactivequizzes,andadaptivelearningpaths.Features like real-time progress tracking and automated doubt resolutionenhanceinclusivity,particularlyinunderserved regions.Thesystemalsosupportsexampreparationand skill-based training through predictive analysis. By processing vasteducationaldata,AI-driven tutoringhas the potential to reshape education, making it more equitable and connected. This research examines its design, implementation, and impact, referencing prior workinAI-driveneducation.

Keywords: Smart Tutor, Python, Flask, Gemini API, PostgreSQL

1. Introduction

The concept of AI-based education systems has gained substantial momentum with advancements in artificial intelligence, enabling the creation of highly personalizedandeffectivelearningplatforms.AnAI-based online tutor leverages these technologies to provide learners with curated content, interactive lessons, and customized feedback, ensuring a more engaging and efficient learning experience compared to traditional methods. This technological intervention not only complements existing education systems but also addresses significant gaps in accessibility, adaptability, andscalability.

Unlikeconventionaleducationsystems,whichoften struggle to address the individual needs of learners, AI tutors analyze user preferences, learning pace, and proficiency to tailor their teaching strategies. This adaptability enhances user satisfaction while improving knowledgeretention and skill acquisition.Furthermore, the system ensures inclusivity by offering multilingual support,cateringtodiverselearningstyles,anddelivering contentthatalignswithindividual.

Recent developments in this field include the integrationofreal-timedoubtresolution,intelligentnote summarization,andinteractivelearningflowcharts.These features, based by AI, ensure that learners receive immediate support and a clearer understanding of complex concepts. Moreover, AI-driven platforms have expanded to support diverse educational applications, suchascompetitiveexampreparation,vocationaltraining, andcorporateupskilling,showcasingtheirflexibilityand utilityinvariouscontexts.Theintegrationofgamification techniques, such as quizzes, achievement badges, and leader boards, further boosts user engagement and motivates learners to achieve their academic or professionalgoals.

AsignificantmilestoneinAIeducationsystemsisthe deploymentofscalablearchitecturesthatcatertodiverse userbaseswhilemaintainingapersonalizedapproach.For instance,advanced natural language processingmodels, such as GPT-based frameworks, enhance the system's ability to understand and respond to user queries effectively. Additionally, machine learning algorithms analyzeuserinteractionstoprovidedata-driveninsights intolearningprogressandrecommendoptimallearning paths. Cloud-based infrastructures and data encryption techniques ensure that the platform remains accessible, secure, and efficient across devices and regions, particularlyinareaswithlimitedtechnologicalresources.

The potential impact of AI-based online tutors extendsbeyondindividuallearningexperiencestosocietal andeconomiclevels.Theseplatformscanbridgethegap betweenurbanandrurallearners,reducedropoutrates, and contribute to workforce readiness. For example, in underservedregionswhereaccesstoqualifiededucators is scarce, AI tutors can serve as a reliable alternative, deliveringhigh-qualityeducationandskilldevelopmentat scale.

ThispaperdelvesintothearchitectureofanAI-based onlinetutor,highlightingitscorecomponents,including userprofilingalgorithms,AI-basedcontentcuration,and real-timelearninganalytics.Byleveragingthesetools,the tutor provides an interactive and adaptive learning environment, fostering inclusivity and bridging

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 Apr 2025 www.irjet.net p-ISSN: 2395-0072

educationalgapsinregionswheretraditionalsystemsfall short. The platform also incorporates features such as predictive analytics to identify areas of improvement, automated progress tracking to motivate learners, and voice-based assistance for hands-free learning experiences. Building on existing work in AI education, thisstudyemphasizespracticalimplementationandrealworld impact, positioning AI-based tutors as transformative agents in the education sector. These platformsnotonlyredefinetraditionalteachingmethods but also pave the way for equitable and innovative learningsystems,empoweringstudentsandprofessionals worldwide. Additionally, this paper explores the ethical considerations and challenges associated with implementing AI in education, such as data privacy, algorithmic bias, and the digital divide, providing a roadmapforfuturedevelopmentsinthisdomain.

2. Problem Definition

Traditional online learning platforms offer accessibility and convenience but face significant challengesthathindereffectiveandpersonalizedlearning experiences.Thesechallengesinclude:

 Lack of Personalization: Existing platforms often providegenericlearningcontentthatdoesnotadapt to individual learners' levels, preferences, or progress. This one-size-fits-all approach limits effectiveness.

 OverwhelmingInformation:Studentsarefrequently bombarded with excessive information, making it difficult to focus on key concepts and relevant material.

 Limited Interaction: Traditional platforms lack mechanisms for interactive and engaging learning experiences, which are crucial for retaining knowledgeanddevelopingcriticalthinkingskills.

 InadequateAssessmentandFeedback:Mostsystems failtooffermeaningful,real-timeassessmentsand constructivefeedbacktailoredtoalearner'sneeds.

 Accessibility Barriers: Students in remote or underprivileged areas often face challenges in accessing quality learning resources due to technologicalorinfrastructurallimitations.

The proposed solution is an AI-based online tutor application designed to address these challenges by leveragingadvancedtechnologies,includingAI,machine learning, and cloud computing. The system provides a personalized,efficient,andengaginglearningexperience throughthefollowingkeyfeatures:

 Dynamic Personalization: The AI system tailors learning content and paths based on individual progress,knowledgelevel,andlearningpace.

 Content Summarization: Using AI algorithms, the tutor summarizes vast notes into concise and meaningful formats, focusing on critical concepts andtopics.

 Interactive Learning Aids: Features such as flowcharts, quizzes, and guided tutorials enhance interactionandcomprehension.

 Real-Time Assessments: The system generates practice questions and offers instant feedback to helplearnersidentifyandaddressweaknesses.

 Accessibility and Inclusivity: By utilizing cloudbasedinfrastructure,theplatformensureslearning resourcesareavailableanytime,anywhere,bridging gapsforremotelearners.

The architecture of the AI-based online tutor is designed to provide a seamless, efficient, and usercentriclearning andexperience.ThesystemintegratesAI algorithms, cloud-based storage, and interactive user interfacestodeliveradaptiveandpersonalizededucation.

3. Architecture

The architecture of the AI-based online tutor is designedtoprovideapersonalized,scalable,andefficient learning experience. It is built using a microservices architecture, with each component serving a specific purpose to ensure seamless integration and responsiveness. The core of the system is a Django web framework, which handles the user interface, data processing,andservesasthemainapplicationserver.The systemintegrateswithvariousAPIstofetchsubjectnotes, summarize content, generate important questions, and provideinteractivelearningaidssuchasflowcharts.

and

Figure1:AI-BasedOnlineTutorInfrastructure

To ensure that the learning experience is both dynamic and personalized, the system leverages Google Cloud'smachinelearningmodelsfortextsummarization

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 Apr 2025 www.irjet.net p-ISSN: 2395-0072

and question generation. The backend employs a PostgreSQLdatabasetostoreuserdata,learningprogress, and educational content. This allows the AI tutor to recommend study material based on the student's level andpreferredlearningtime.Theuser’sprofile,including theircurrentsubjectknowledgeandlearningpreferences, isstoredinthedatabase,whichhelpsinprovidingtailored recommendations.

ThesystemutilizesaRESTfulAPItointeractwiththe front-end,ensuringsmoothcommunicationbetweenthe database,machinelearningmodels,andtheuserinterface. Forscalabilityandperformance,theAImodelsanddata processing are handled by Google Functions, which dynamicallyallocateresourcesbasedonusageandensure highavailability.

Uponstudentsinputtheirsubjectofinterest,current level,andthetimetheywishtodedicatetolearning.The AIthenfetchesthecorrespondingsubjectnotesfromthe database and processes them to generate summaries, importantquestions,andotherlearningaids.Flowcharts and diagrams are dynamically created to illustrate complexconcepts,providingavisuallearningexperience. Notifications and reminders are also integrated, promptingstudentsaboutupcominglessonsortasks.The architecture emphasizes seamless user interaction, scalability,andadaptabilitybyintegratingmultiplecloudbased tools. By utilizing machine learning, natural language processing, and cloud services, the AI-based online tutor offers a comprehensive, adaptive learning solutiondesignedformoderneducation.

TheAI-basedonlinetutorsystemfollowsawell-defined architecturetoensuresmoothandefficientfunctionality.In this system, the flow of data and interaction between components is carefully orchestrated, ensuring scalability andflexibility.Thearchitectureisdesignedtohandlevarious

tasks like fetching notes, summarizing content, generating questions, and providing interactive learning aids using machinelearningandcloudservices.

FlowoftheaboveFig:

 Student Inputs Learning Preferences: The student logs into the system and inputs their subject, current learning level, and the amount of time they wish to dedicatetolearning.

 AI Fetches Learning Material: The AI fetches the relevant subjectnotes andmaterial fromthe database basedonthestudent’sinputs.Thesearethenprocessed forsummarizationandquestiongeneration.

 Content Processing & Summarization: Using NLP models,theAIprocessesthe notes,generatingconcise summaries,importantquestions,andlearningaidslike flowchartsanddiagrams.

 Interactive Learning Interface: The frontend, built with modern web technologies like HTML, CSS, and JavaScript,displaysthesummarizedcontent,interactive flowcharts,andkeyquestions.Studentscaninteractwith thecontentthroughauser-friendlyinterface.

 Notifications&Reminders:TheAI-basedtutorsends notifications to the student about upcoming lessons, reminders, and rescheduled sessions based on their preferences.

 Student Progress & Feedback: The system tracks student progress and feedback. Based on the performance and feedback, it adjusts the material providedandrecommendsfurtherlearningpaths.

InternalStructure

 Frontend: A combination of HTML, CSS, and JavaScript provides an interactive and user-friendly interface.Thefrontendcommunicateswiththebackend viaRESTfulAPIstofetchlearningmaterialsandinteract withtheAI.

 AIModels(GoogleCloud):TheAImodels,suchastext summarizationandquestiongeneration,arehostedon GoogleCloudFunctions.Thesemodelsprocessthedata fetched from the database and generate learning materialdynamically.

 Backend (Django): The backend handles user authentication,interactswiththePostgreSQLdatabase, and coordinates the AI model interactions. It also managestheuserprofilesandlearningdata.

 PostgreSQL Database: The database stores user profiles,learningprogress,subjectmaterial,andother necessarydatatoensureapersonalizedexperience.

 GoogleCloudFunctions:Thesehandlethemachine learning operations such as summarizing notes,

Figure2:SystemArchitecture

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 Apr 2025 www.irjet.net p-ISSN: 2395-0072

generating questions, and providing learning recommendations.

StepsInvolvedinImplementation:

 FrontendDevelopment:UseframeworkslikeReact.js orVue.jstobuildanintuitiveuserinterface.Studentswill use this interface to input learning preferences and interactwiththelearningcontent.

 AIModelIntegration:LeverageGoogleCloud'sNLP APIs to perform text summarization, question generation,andrecommendationservices.Ensurethese servicesareaccessiblethroughbackendAPIs.

 BackendAPI(Django):SetuptheDjangoframework asthebackendtomanageuserrequests,interactwith the database, and serve the AI-generated content. Use RESTful APIs to fetch content from the database and processitwithAImodels.

 PostgreSQL Integration: Store subject notes, user data,andlearningprogressinPostgreSQL.Thisensures thattheAIcanprovidepersonalizedcontentandtrack studentperformance.

 NotificationSystem:Implementanotificationsystem tosendlearningremindersandsessionupdatesbasedon thestudent'sschedule.

 Testing&Debugging:Thoroughlytestthesystemfor bugs, performance issues, and AI model accuracy. Use tools like Django’s test framework and Google Cloud's debuggingtools.

 Deployment:DeploytheAImodelsand backendto Google Cloud, while hosting the frontend on a cloud platformlikeNetlifyoradecentralizedplatformlikeIPFS forscalabilityandaccessibility.

4.Different

Modules

TheOnlineSmartAITutorintegratesmultipleAI-driven modules that work together to create a personalized, efficient, and interactive learning experience. The Quiz Moduleallowsuserstoselectatopicorsubjectforthequiz. Upon selection, the frontend sends a request to the Flask backend, which queries the Gemini API to fetch quiz questionsrelatedtothechosentopic.Thebackendstoresthe questions temporarily in a session and sends them to the frontend.Oncetheusercompletesthequiz,theiranswersare sentbacktothebackend,wherethesystemcomparesthem tothecorrectanswersfromtheGeminiAPI.Thesystemthen calculatesthescoreandprovidesinstantfeedback,helping users identify areas for improvement. This feature allows quizzestobedynamicandadapttotheuser’sprogressand knowledgelevel.

TheChatbotModuleisareal-timevirtualassistantthat facilitatescontinuoususerinteraction.Whenausersubmitsa queryormessage,thefrontendsendsthequerytotheFlask backend.ThebackendthenforwardsthequerytotheGemini API, which processes the input and generates a response basedoncontext.Thisresponseissentbacktothefrontend and displayed to the user in real time. By using Natural Language Processing (NLP), the chatbot ensures that the answers provided are context-sensitive, making the interaction feel conversational and natural. This module fostersadynamiclearningenvironmentbyofferinginstant clarificationonconceptsandqueries.

TheQ&AModuleallowsuserstoaskspecificquestions aboutasubjectortopic.Aftertheusersubmitsaquestion,the frontendsendsittotheFlaskbackend,whichforwardsthe requesttotheGeminiAPI.TheAPIanalyzesthequestionand returns the most relevant, accurate answer. This system ensuresthattheanswersprovidedarenotonlyconcisebut also deeply informative, enabling students to better understand complex topics and resolve doubts quickly. Additionally,thebackendcanretrieveanswersfromapreexistingFAQdatabase,offeringanotherlayerofassistanceto theuser.

ThePDFSummarizerModuleisparticularlyusefulfor students dealing with large volumes of text in educational PDFs.TheuseruploadsaPDFdocument,whichissenttothe Flaskbackendfortextextraction.Thebackendprocessesthe document and sends the extracted text to the Gemini API, which generates a summarized version by identifying key pointsandreducingthecontenttomoremanageablechunks. Thesummarizedcontentisthensentbacktothefrontend, allowinguserstoaccessaneasy-to-understandoverviewof theirmaterials.UserscanalsoannotatethesummarizedPDF, makingitamoreinteractivestudytool.

Eachofthesemodules,fromthedynamicquizsystemto the interactive chatbot and the document summarizer, is designed to work seamlessly together. These features empower users with instant access to tailored content, immediateclarificationofdoubts,andtheabilitytonavigate and engage with learning materials more efficiently. By integratingAImodelsliketheGeminiAPI,theplatformoffers a comprehensive, adaptive, and scalable solution for personalized education, making learning more accessible, engaging,andeffective.

5. Result and Analysis

Theimplementationofthe"AIBasedOnlineTutor" successfully addresses the challenges of traditional tutoring systems by leveraging advanced AI algorithms and integrating them with an intuitive, user-friendly interface.Thesystemefficientlyfetchessubjectnotesfrom a database and delivers them in a concise and easily digestibleformat.TheAI’sabilitytosummarizecontent, provideimportantquestions,andguidestudentsthrough

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 Apr 2025 www.irjet.net p-ISSN: 2395-0072

learningwithvisualaidslikeflowchartsensuresthatthe learningexperienceispersonalizedandeffective.

The system’s backend, built with Django and PostgreSQL,handlesuserdatasecurely,whilethefrontend providesanengagingexperiencewithsimplenavigation and interactive elements. The integration of AI to recommendstudymaterialsbasedontheuser'slearning levelandgoalsfurtherenhancesthetutor'spersonalized approach,increasingthelikelihoodofstudentengagement andretention.

Testing of the AI tutor system demonstrated its ability to deliver accurate and relevant study content based on user input. The AI model's effectiveness in summarizing notes and suggesting learning paths was verifiedthroughreal-timeinteractionwithstudents.While thesystemoperatesseamlesslyundernormalconditions, thereareareasforimprovement,particularlyintermsof theAI'sabilitytoprovidehighlycustomizedfeedback

Further analysis suggests that while the AI-based approachimprovesefficiencyanduserengagement,afew challenges remain. Scaling the system to handle a large volumeofuserswhilemaintainingtheperformanceofthe AI model and database queries may require additional optimization.Moreover,thereisroomtoenhancetheAI’s abilitytoengageusersinamoreinteractivemanner,with features such as voice interaction or real-time Q&A capabilities.Additionally,ensuringaseamlessexperience acrossdifferentdevicesandplatformsremainsakeyfocus forfutureimprovements.

Thesystem,overall,demonstratesthepotentialof AI-drivenlearningsolutions,offeringacost-effectiveand scalable method of delivering high-quality education. Future work will focus on improving the adaptability of the AI system, expanding its range of subjects, and exploringnewmethodsforenhancinguserengagement. Integrating real-time analytics to monitor student progressandincorporatingadaptivelearningtechniques will further refine the tutor's ability to meet diverse learningneeds.

The AI-based tutor also has the potential to integrate multi-modal learning, including interactive videosandexercises,tocatertodiverselearningstyles.By incorporating real-time feedback mechanisms, it can provide more personalized guidance, further improving user engagement. Additionally, incorporating peer collaboration features can create a sense of community, enrichingthelearningexperience.

The system could also explore multilingual capabilities, broadening its accessibility across different regions. Continuous monitoring and adaptation to user needswillenhancethesystem'sabilitytodeliverrelevant andaccuratecontent.Inthefuture,expandingthetutor’s

capabilitiestocoverawiderrangeofsubjectswillmakeit amoreversatileeducationaltool.

Figure3: PerformanceanalysisoftheAImodel.The varianceinresponsetimesisattributedtoserverload andAIprocessingduringpeakusage.

The resource consumptionof the system, including computationalcostforprocessinguserinputandgenerating responses,wasevaluatedundervariousconditions:

AIModelExecution:RunningtheAImodeltoanalyzeand summarize subject notes typically consumes significant processingpower.Forexample,generatingasummaryofa subject’s notes requires multiple computations, with each summary processing consuming around 500–1,000 millisecondsperquery,dependingonthecomplexityofthe subjectmaterial.

Data Retrieval: Fetching data from the PostgreSQL database to generate relevant study materials costs approximately50–100millisecondsperquery.However,this canincreaseifthequeryinvolvesfilteringorretrievinglarge datasets.

UserInteractions:Eachuserinteraction(e.g.,submittinga learning request or querying the tutor) generally takes around1-2secondstoprocess,includingdatabaseretrieval, AIprocessing,andcontentdelivery.

These system operations were evaluated for cost efficiencyintermsofresourceutilization,whichiscrucialfor scaling the platform.Further refinements are necessaryto optimize server-side processesand reduce latency in realtimeinteractions.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 Apr 2025 www.irjet.net p-ISSN: 2395-0072

The results of user engagement studies highlight the strengthsoftheAItutorintermsofusersatisfactionand learning efficiency. Users reported higher satisfaction levelswiththepersonalizedguidanceprovidedbytheAI, which directly impacts the overall learning experience. Additionally, the AI tutor's ability to suggest relevant materials and create a structured learning path contributestoamoreengagingexperiencecompared to traditional methods. Despite a slightly higher upfront investment in setting up the AI system, the long-term benefits of scalable and cost-effective tutoring services make the AI-based solution a competitive alternative in theeducationmarket.

6.Conclusion

TheOnlineSmartAITutorintegratesadvancedAI features to create a highly interactive and personalized learning experience. A key component is the AI-based Chatbot,whichactsasa24/7virtualassistant,answering user queries, explaining concepts, and guiding learners throughtheir studies using naturallanguage processing (NLP).

Another essential feature is PDF Handling, allowing users to upload, read, and interact with educational PDFs. The AI extracts key information, summarizes content, highlights important points, and enables annotations, making large documents more manageable. The Q&A System provides subject-specific answersandanFAQsectionforquickaccesstocommonly askedquestions.Thisfeatureenhancesunderstandingby addressing knowledge gaps and offering detailed explanations.

Additionally, the Quizzes feature generates dynamic assessments tailored to the learner’s progress. Adaptivequizzeswithinstantfeedbackhelpusersidentify weaknesses,reinforcelearning,andimproveretention.

Thesefeaturescollectivelycreateanengaging,AIdriven educational environment that enhances learning efficiencyandaccessibility.

7. Future Work

FutureenhancementsfortheAI-basedonlinetutor will focus on key areas to improve its effectiveness and accessibility.Onepriorityisadaptivelearning,wherethe tutorevolvesbasedonuserinteractionsusingreal-time feedback and continuous learning techniques for better personalization. Another crucial development is multimodal content integration, incorporating videos, interactivequizzes,andvoice-basedfeedbacktocreatean immersivelearningexperience.Scalabilitywillalsobea major focus, leveraging cloud-based services like AWS, GCP, or Azure to handle a growing user base efficiently. Additionally, privacy and data security will be essential, ensuring compliance with regulations like GDPR and implementing robust encryption to protect user data. These advancements will enhance the tutor’s impact, makingeducationmorepersonalized,scalable,andsecure.

8. References

[1] H. Shahri, M. Emad, N. Ibrahim, R. N. B. Rais, and Y. AlFayoumi,"ElevatingEducationthroughAITutor:Utilizing GPT-4 for Personalized Learning," 15th Annual UndergraduateResearchConferenceonAppliedComputing (URC),2024,IEEE.

[2] M. Alam, M. S. Alam, S. O. Siddique, N. Hasan, M. M. H. Tajwar, M. K. Rahman, and M. Rahman, "Quantifying AttentionLevelsinIndividualizedOnlineTutoring:ACase of One-on-One Sessions," International Conference on Computational Intelligence, Networks and Security (ICCINS),2023,IEEE.

[3]I.Pesek,N.Nosović,andM.Krašna,"TheRoleofAIinthe Education and for the Education," 11th Mediterranean ConferenceonEmbeddedComputing(MECO),2022,IEEE.

[4]A.Islam,R.Ali,G.Singh,B.Islam,A.Islam,andS.Hossain, "An Evaluation of AI-Enhanced Collaborative Learning Platforms," International Conference on Communication, ComputerSciencesandEngineering(IC3SE),2024,IEEE.

[5]A.Abduljabbar,N.Gupta,L.Healy,Y.Kumar,J.J.Li,andP. Morreale, "A Self-Served AI Tutor for Growth Mindset Teaching," 5th International Conference on Information andComputerTechnologies(ICICT),2022,IEEE.

[6]E.Frankford, C.Sauerwein,P. Bassner,S. Krusche,andR. Breu, "AI-Tutoring in Software Engineering Education: ExperienceswithLargeLanguageModelsinProgramming Assessments,"IEEE/ACM46thInternationalConferenceon SoftwareEngineering:SoftwareEngineeringEducationand Training(ICSE-SEET),2024,IEEE.

Figure4: ThisFigComparisonoftutorengagement andusersatisfactionlevels.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 Apr 2025 www.irjet.net p-ISSN: 2395-0072

[7]W.-H.KimandJ.-H.Kim,"IndividualizedAITutorBased onDevelopmentalLearningNetworks,"IEEEAccess,vol. 8,2020,IEEE.

[8] A. Baillifard, M. Gabella, P. B. Lavenex, et al., "Effective Learning with a Personal AI Tutor: A Case Study," EducationandSpringer,2024.

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