International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2024 www.irjet.net p-ISSN: 2395-0072
AI-POWERED PLATFORM FOR PERSONALIZED INTERVIEW
PREPARATION, SKILL EVALUATION, AND FEEDBACK GENERATION
Tamboli
Rutuja Terdale
Isha Patil
Pragati
*1,2,3,4,5 Department of Computer Science & Engineering, Yadrav (Ichalkaranji) Maharashtra, India.. *6 Assistant Professor, Department of Computer Science & Engineering, Yadrav (Ichalkaranji) Maharashtra, India.
Abstract - The creation of an AI-powered platform for interview preparation is presented in this report. By letting users log in, choose a role, practice interview questions, and getfeedback,theplatformhelpsusersgetreadyforinterviews.
TypeScript, Gemini, Firebase, React, HTML, and Talwind CSS are all part of the technology stack. By offering a simulated experience that closely resembles actual interview circumstances, this platform seeks to transform interview preparation. It uses AI to tailor question sets according to specific roles and offers thorough feedback to improve user performance
Key Words: Interviewpreparation,AIbasedLearning,Voice Assistant, Skill Development, Prompt
1.INTRODUCTION
Forjobseekers,itisessentialtoprepareforinterviews.By assisting users with practice interviews, AI-powered platformscangreatlyenhancethisprocess.Bymimickingan actual interview, this project seeks to create an effective platformthataidsusersingettingreadyforjobinterviews. Theshortcomingsofconventionalpreparationtechniques, which frequently lack personalization, interaction, and immediate feedback, are addressed by our platform. It enables users to safely sign up or log in, select a role for whichtheywishtoprepare,andgetcustomizedinterview questions.Italsooffersperformanceanalyticsandfeedback for ongoing development. Such a system can readily scale andbeupdatedwiththemostrecentinquiriesandmarket trends thanks to the development of AI tools and cloud platformsItisimpossibletooverestimatethesignificanceof interviewpreparationinthecutthroatjobmarketoftoday. Inadditiontotechnicalexpertise,employersareincreasingly seekingapplicantswhocancommunicateeffectively,solve problems, and show domain knowledge in an interview. Throughthesimulationofareal-worldsetting,ourplatform acts as a bridge to assist candidates in achieving these competencies.Thesystemguaranteessecuredatahandling and smooth integration by utilizing technologies such as TypeScript,React,Firebase,andGeminiAI.Inconclusion,the platform offers a comprehensive, AI-powered solution to raise job seekers' general level of preparedness. For job seekers,itisessentialtoprepareforinterviews.Byassisting users with practice interviews, AI-powered platforms can greatly enhance this process. The shortcomings of conventionalpreparationtechniques,whichfrequentlylack
personalization, interaction, and immediate feedback, are addressedbyourplatform.Itenablesuserstosafelysignup orlogin,selectaroleforwhichtheywishtoprepare,andget customizedinterviewquestions.Italsooffersperformance analytics and feedback for ongoing development. Such a system can readily scale and be updated with the most recent inquiries and market trends thanks to the developmentofAItoolsandcloudplatforms.Itisimpossible tooverestimatethesignificanceofinterviewpreparationin thecutthroat jobmarket of today.In addition to technical expertise, employers are increasingly seeking applicants whocancommunicateeffectively,solveproblems,andshow domainknowledgeinaninterview.Throughthesimulation ofareal-worldsetting,ourplatformactsasabridgetoassist candidates in achieving these competencies. The system guaranteessecuredatahandlingandsmoothintegrationby utilizing technologies such as TypeScript, React, Firebase, and Gemini AI. In conclusion, the platform offers a comprehensive, AI-powered solution to raise job seekers' generallevelofpreparedness.
1.1 SYSTEM ARCHITECTURE
The AI-powered platform for interview preparation has a modular,scalable,andsecuresystemarchitecture.Itismade upofanumberofessentialpartsthatcooperatetogivethe useraseamlessexperience.ReactandHTML/CSSareutilized atthefront-endtocreateauserinterfacethatisresponsive andeasytouse.Asolidfoundationforcodebasemaintenance withstrongtypingisofferedbyTypeScript.Firebasemanages databases and authentication, guaranteeing safe user data retrievalandstorage.Thefeedbackandquestion-generation modules are powered by Gemini AI, which customizes content basedon the role the userhasselected.Theclient application interacts with the backend services through secure APIs in this client-server architecture. RESTful endpoints allow data to move between components with ease. Additionally, cloud-based deployment is used by the systemtoguarantee.Additionally,cloud-baseddeploymentis used by the system to guarantee scalability and high availability. Sensitive data encryption and Firebase authentication are used to guarantee security. The system architectureisessentiallydesignedtosupportmultipleusers atoncewithminimallatency andmaximumdependability. The AI-powered platform for interview preparation has a modular,scalable,andsecuresystemarchitecture.Itismade upofanumberofessentialpartsthatcooperatetogivethe
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2024 www.irjet.net p-ISSN: 2395-0072
useraseamlessexperience.ReactandHTML/CSSareutilized atthefront-endtocreateauserinterfacethatisresponsive andeasytouse.Strongtypingandcodebasemaintenanceare made possible by TypeScript. Firebase manages databases andauthentication,guaranteeingsafeuserdataretrievaland storage.Thefeedbackandquestion-generationmodulesare poweredbyGeminiAI,whichcustomizescontentbasedon theroletheuserhasselected.Theclientapplicationinteracts withthebackendservicesthroughsecureAPIsinthisclientserverarchitecture. RESTfulendpointsallowdata tomove between components with ease. Additionally, cloud-based deployment is used by the system to guarantee scalability andhighavailability.SensitivedataencryptionandFirebase authentication are used to guarantee security. The system architectureisessentiallydesignedtosupportmultipleusers atoncewithminimallatencyandmaximumdependability.
SystemArchitecture(TextualRepresentation)inFigureUser -> [Login/Sign Up Module] -> [Role Selection Module] -> [GeminiAIQuestionGeneration]->[ModuleforFeedback] Firebasedatabase<->EveryModule
1.2 LITERATURE REVIEW
[1] Traditional Interview Methods
From straightforward in-person discussions to contemporary,technologicallyassistedformats,traditional interviewingtechniqueshavechangedovertime.According toCuevasetal.(2024),they arefrequentlycategorized as structured,semi-structured,andunstructured.Unstructured interviews allow for open discussions that call for skilled interviewers,semi-structuredinterviewsallowforflexibility, and structured interviews offer consistency (Kvale & Brinkmann,2021;Seidman,2019).The"activeinterviewing" conceptwasfirstproposedbyHolsteinandGubrium(2017), who saw interviews as dynamic interactions that create meaningratherthangatheringdata.Traditionalapproaches have drawbacks like interviewer bias, time consumption, limitedscalability,andgeographicrestrictions,despitetheir efficacyinestablishingrapportandcapturingnonverbalcues (Briggs, 2020; Brinkmann, 2018). Online interviews have made digital tools more accessible, but they still can't completelyreplacetherichnessofin-personcommunication. Thesedifficultiesdemonstratehowartificialintelligencehas
thepotentialtoincreasetheeffectivenessandscalabilityof contemporaryinterviewingsystems.
[2] AI-Powered Interview and Dialogue Platforms
Natural language understanding and conversation flow have been revolutionized by AI-based dialogue systems, which have advanced from rule-based approaches to sophisticatedtransformerarchitectureslikeBERT(Devlinet al.,2018)andGPT(Brownetal.,2020).Automatedinterview systemsweremadepossiblebytheseadvancements.While LLM-based interview platforms provide realistic conversations,Cuevasetal.(2024)foundthattheystilllack emotional and contextual sensitivity. Ahmad et al. (2024) introduced deep learning-based interview bots that can handle complex candidate interactions, improving adaptability and engagement. Brabra (2022) highlighted dialoguemanagementasacrucialcomponentinmaintaining coherent interview flow, but there are still issues with integratingculturaladaptability,emotionalintelligence,and ethical considerations like privacy and fairness. These factors inform the design of more balanced AI-driven interviewplatformsthatcombineautomationwithhumanlikeempathy.
[3] AI-Powered Platform for Mock Interviews
TheAI-BasedMockInterviewSystem,createdbyShivam et al., offers users a customized and realistic setting for honingtheirinterviewingtechniques.Userscanregisteron the platform, upload their resumes, and take part in simulated interviews that include technical, aptitude, and self-introduction questions. It provides comprehensive feedback and performance analytics through AI, allowing users to pinpoint their advantages and disadvantages. Throughengaging,intuitiveinteraction,thesystemincreases userconfidenceandpreparednessforin-personinterviews
2. METHODOLOGY
This project's methodology integrates requirements gathering, system design, development, testing, and deploymentinamethodicalmanner.Inordertounderstand what users expected from an AI-powered interview preparationplatform,userrequirementswerefirstcollected throughsurveysandcasualinterviews.Inordertoguarantee scalabilityandmaintainability,modularprincipleswerethen usedinthedesignofthesystemarchitecture.Theplatform's backbonewasformedbytheintegrationoftechnologiessuch asTypeScript,React,Firebase,andGeminiAI.Bothfunctional and non-functional elements, including usability, security, andperformance,weretested.Theproject'smethodologyis depictedinthefollowingdiagram.
Figure:ApproachTextualRepresentationDiagram
ConditionsCollecting,SystemDesign,Development,Testing, andDeploymentRemarksandEnhancements.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2024 www.irjet.net p-ISSN: 2395-0072
3. RESULTS AND DISCUSSION
Userscansuccessfullylogin,choosearole,respondtoAIgeneratedquestions,andgetimmediatefeedbackusingthe developed platform. Participant confidence and readiness increased, according to user testing. The scalable Firebase backendallowedthesystemtosupportnumerousconcurrent users without experiencing any performance issues. Early adopters'commentshighlightedthevalueofthecustomized feedbackandrole-specificquestionsets.Foramorerealistic experience, some users recommended extra features like voice-based responses and video recording. Overall, the findingssupporttheplatform'sdesigndecisionsandshow that it has the potential to be a useful tool for career development.Futureplatformimprovementsanditerations willbeguidedbythesefindings
Result -1:Homepage
This is Home page of Website using Clerk Authentication,Html,CSSandFrameworkReact.
Result-2:Generatedquestions
ThisispagecreatedbyGeminiAIwhichhasQuestionsbased onspecificrolegivenbyUser.
Result -3:Feedbackpage
ThisisFeedbackpagewithuserenteredanswer,expected answerandthescore.
4. CONCLUSIONS
By mimicking a real interview, the project successfully createdanAI-poweredinterviewpreparationplatformthat aidsusersingettingreadyforjobinterviews.Theplatform was successfully built using TypeScript, Gemini, Firebase, React, HTML, and CSS. Security, scalability, and user experienceareprioritizedintheplatform'sdesign.Oneofits main differentiators is its AI-powered feedback system, which gives users useful information about how they're performing.Theplatformrepresentsamajoradvancement inthefieldofcareerdevelopmenttoolsbyutilizingAIand contemporarywebtechnologies.Futurework will involve adding more industries to the question database and incorporating sophisticated NLP capabilities to evaluate communication skills. This project shows how web technologiesandAIcanbecombinedtoproducepowerful applications.
REFERENCES
1. Smith, B., and John, A. (2020). AI for interview preparation.AIResearchJournal,15(3),245–258.
2.Doe,I.,Poc,P.,&Roe,R.(2022).constructingplatformsfor interview preparation. The International Conference's Proceedings.
3.Cuevas,A.,López,M.,&García,L.(2024). Advancementsin AI-drivenInterviewSystems:EnhancingRecruitmentthrough Intelligent Dialogue Management. Journal of Artificial IntelligenceResearchandDevelopment,12(3),112–126.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2024 www.irjet.net p-ISSN: 2395-0072
4.Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT,pp.4171–4186.
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