Artificial Intelligence Powered Mock Interview Generator: Revolutionizing Interview Preparation with by IRJET Journal

Artificial Intelligence Powered Mock Interview Generator: Revolutionizing Interview Preparation with Real-Time Feedback

Pankaj kumar1 , Navneet Kumar2 , Priyangsu Dutta3 , Tahshim Ansary4 , G Samiksha Reddy5

1Professor of Acharya Institute of Technology, Karnataka, India 2,3,4,5Students, Dept. of Information Science and Engineering, Acharya Institute of Technology, Karnataka, India

Abstract - The “Artificial Intelligence Powered Mock Interview Generator” is unique solution that transform interview preparation process. AI technologies and web developmentframeworks.Thisplatformdeliverscustommade mock interview real time feedback on both verbal and nonverbal communication abilities and keep track of performance it addresses the limitations of traditional methods by providing flexible jobs specific interview experience that combines audio, video andtext elements. The system is built using Next.Js for frontend development, the system looks into the Google Gemini API for problem solving and utilizes PostGreSQL for managing data. The key features include secure user authentication, AI driven question generation and speech functions that create real practice sessions. Testing has shown that the system works very well andprovidesvaluablesuggestions. Plansforimprovementlike capacity analysis, support multiple languages and integrate withjobportals. Overall,Artificialintelligencepoweredmock interview generator represent a significant development in interviewpreparationtools,providingjobseekerswithflexible guidance in today's job market.

Key Words: Artificial Intelligence, Natural Language Processing NLP, Customizable Interview.

1.INTRODUCTION

Traditional Methods of preparing for interviews often doesn’tprovideaccurateandpersonalizedpractise[1].This approachesusuallydependonalistofquestions,comments orlesshumaninput,makingithardforspecificneedsforan interview preparation [2] This platform uses advance artificial intelligence to generate new interview scenarios aims to overcome the limitations [3]. This tool provides smoothinteractionforspecificrolesandtaskprovidingreal time feedback. on both verbal and non-verbal cues. By integratingaudio,videoandtextanalysis,itovercomesthe traditional interview preparation methods. This AI driven solutiongivesuserstorefinetheirinterviewskillsbyadding importantvaluethroughfeedback,relevantquestionsand performanceassessments[4]

1.1 System Architecture

The"AIPoweredMockInterviewGenerator"hasaunique design to make it more capable, flexible and efficient. The platform uses various technologies to ensure efficient and effectiveoperation[5]

 FrontendLayer:Theuserinterfaceisdeveloped using Next.js, offering a fast and responsive experience[6].Itensuresthatuserscannavigate the platform smoothly and customize mock interviewseffortlessly.

 Backend Integration: Google Gemini API make the generation of context-specific interview questions using advanced natural language processing [7]. Drizzle ORM is utilized for efficientdatabasequeries,ensuringstreamlined interactionwiththedatabaselayer[8].

 Authentication and User Management: Clerk providessecureandrobustuserauthentication, supporting login, signup, and profile managementfunctionalities[9]

 Database Layer: The system uses PostgreSQL (hostedonNeon)tostoreandmanageuserdata, feedback,andinterviewconfigurations,ensuring reliabilityandscalability[10].

 SpeechandTextProcessing:React-basedspeechto-text and text-to-speech hooks handle realtimetranscriptionandfeedbackdelivery[11]

 Real-Time Feedback Analysis: AI algorithms analyze verbal communication, including tone, speed,andaccuracy,whilefutureenhancements willincludegestureandbodylanguageanalysis [12].

1.2 Key Technologies Used

 ThemotivationFrontendDevelopment:Next.js: A React-based framework offering server-side rendering and optimized performance for theuserinterface[6]

 DatabaseManagement:PostgreSQL:Arelational database system used to manage user data, performance metrics, and interview content efficiently[10].

 Drizzle ORM: Simplifies database interaction withcleanandtype-safequeries[8].

 Authentication: Clerk: Ensures secure login, signup, and user session management with minimalconfiguration[9].

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

Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072

 AI-driven question generation: Google Gemini API:UsesadvancedAItogeneratejobinterview questionsbasedonuserinput[7].

 SpeechProcessing:ReactHooksforSpeech-toTextandText-to-Speech:Speedupthetimingof speechanalysisandinteraction[11]

 Futuretechnologies:Gestureandbodylanguage analysis: Using deep learning to evaluate nonverbal communication in spoken conversation, multilingual support: Communication and custom language models NLP and collaboration in the portal: Connect users to the study program. Interview–Interview[12]

2. Features And Functionality

The “AI Powered Mock Interview Generator”combines artificial intelligence technology with a design designed to providejobseekerswithasimulatedinterviewexperience. Itscorefeaturesaredesignedtoimprovetheuserexperience andprovideusefultipsforinterviewpreparation.

2.1

Mock Interview Customization

Theplatformallowsuserstotailortheirmockinterviews accordingtotheirneeds[1].

 Position-SpecificQuestions:Userscanselectjob rolesandindustries,andthesystem,poweredby Google Gemini API, generates position-specific questionsthatmimicreal-worldscenarios[7]

 Flexibility:Userscanadjustinterviewdifficulty levels and durations based on their expertiseandgoals[3]

2.2 Real-Time Feedback

 Speech Analysis :Verbal Feedback: Using speech-totexttechnology,thesystemevaluatesclarity,tone,and pace of speech. Identifies filler words and suggestsimprovements[11]

 Text Analysis: AI evaluates answers for relevance, depth, and conciseness, providing suggestions forimprovement[12]

 Nonverbal feedback: The plan includes the use of gestures and body analysis to provide information aboutthebody,face,andeyecontact[12].

2.3 Performance Tracking

The platform enables further growth by tracking performance

 Conversation Management: Users receive detailed information about each interactive

critiqueinteraction,includingscoresforcorrect answers, good answers, and overallperformance[1]

 PerformanceAnalysis:TheDashboardreports real-time performance, helping users identify areasforimprovement[3]

 Goals: The Dashboard reports real-time performance, helping users identify areas forimprovement.

2.4 Multiple interactions

Increaseaccessibilityandusability

 TexttoSpeech:Providesabetterexperienceby translating questions and answers intospokenwords[11]

 Speech to Text: Allows users to respond to messagesandhavetheirresponsesrecordedfor anauthenticexperience.Identificationmethod–time[11]

2.5 Security and Privacy

 Secure Authentication: The system uses an operator to ensure that user information is protected and available only to authenticatedusers[9]

 Data Encryption: User data, including chat responsesandperformancemetrics,issecurely stored in PostgreSQL using encryptiontechnology[10].

3.Problem Statement

Existingplatformsfailtoprovidetailoredmockinterview sessionsthatfocusonspecificroles,topics,ordifficultylevels. They also lack real-time feedback mechanisms capable of analyzingverbalandnon-verbalcommunicationskills,such as tone, clarity, gestures, and expressions. This absence of holisticfeedbackmakesitdifficultforcandidatestoidentify andaddresstheirweaknesses.

3.1 Existing System

Traditionalinterviewpreparationmethodsrelyheavilyon manualapproaches,whichlackscalability,adaptability,and efficiency. Peer mock interviews provide personalized feedback but are constrained by the availability and expertise of the interviewer, leading to inconsistent outcomes. Pre-recorded sessions offer static content that failstoadapttoindividualneedsorindustrytrends,while generic feedback methods do not assess critical communication aspects like confidence, tone, and body language.Manualprogresstrackingiserror-proneandlacks detailed analytics, making it difficult for candidates to

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

Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072

identify areas for improvement. Additionally, traditional methods have minimal technological integration, relying primarilyontextorvideo-basedpracticewithoutleveraging AI,NLP,orspeechrecognition.Resourceconstraintsfurther limitaccessibility,especiallyforcandidatesinremoteareas. These limitations create a gap in interview preparation, highlightingtheneedforaninnovative,technology-driven solution that offers real-time feedback, adaptability, and comprehensiveprogresstracking.

3.2

Proposed System

The AI Mock Interview Generator – Virtual Prep is an advanced, AI-driven platform designed to revolutionize interview preparation by addressing the inefficiencies of traditionalmethodsthroughcutting-edgetechnologiessuch as artificial intelligence, natural language processing, and machinelearning.Unlikestaticcontentandmanualtracking systems, Virtual Prep dynamically generates realistic interviewquestionstailoredtotheuser’sjobrole,industry, anddifficultylevel,ensuringadaptabilityandrelevancefor an authentic interview experience. The platform provides real-timefeedbackthatevaluatesverbalattributessuchas tone,clarity,fluency,andvocabulary,alongwithnon-verbal communicationlikefacialexpressions,bodylanguage,and confidence,offeringholisticinsightstorefineacandidate’s overallpresence.Withinteractiveelementssuchasspeechto-texttranscriptionforresponseanalysis,text-to-speechfor realistic question delivery, and role-specific scenarios, VirtualPrepcreatesanimmersiveandengagingpreparation environment. Advanced AI-powered behavioral insights assess traits such as adaptability, engagement, and confidence,whiledetailedanalytics,visualdashboards,and multi-dimensional feedback reports enable users to systematically track their progress, identify strengths and weaknesses,anddeveloptargetedimprovementstrategies. Thescalablearchitectureensuresseamlessperformancefor a growing user base, and an intuitive interface makes it accessibletouserswithvaryingtechnicalproficiencies,with futureplansincludingmulti-languagesupportfora global audience.

4.System Requirement Specification

4.1

Functional Requirements

 User Authentication: Users should be able to securelyregister,login,andmanagetheiraccounts withpasswordrecoveryandsessionhandling.

 Role and Topic Selection: Users can select their desiredinterviewrole,topic,anddifficultylevelfor customizedmockinterviewsessions.

 AI-Generated Interview Questions: The system should generate personalized and relevant interview questions based on the user’s selected role,topic,anddifficultylevel.



Speech-to-Text:Theplatformshouldconvertusers’ spoken responses into text for analysis and feedback.

Text-to-Speech: The system should read out interview questions in audio form, providing a naturalandinteractivemockinterviewexperience.

 Real-TimeFeedback:Theplatformshouldanalyze userresponsesandprovideimmediatefeedbackon both verbal (speech clarity, tone) and non-verbal (bodylanguage)communication.

 ProgressTracking:Usersshouldbeabletoviewand track their past performance, including feedback andareasofimprovement,overtime.

 Multi-Device and Multi-Language Support: The platformshouldbeaccessibleonbothdesktopand mobiledevicesandsupportmultiplelanguagesfor globalusers.

 DataSecurity:Theplatformshouldsecurelystore userdataandensureprivacy,complyingwithdata protectionregulationslikeGDPR.

4.2 Non-Functional Requirements

 Performance:Theplatformshouldhandlemultiple concurrentusersandprovidefastresponsetimes, ensuring smooth performance even during peak usage.

 Scalability: The system should be able to scale to accommodate growing user demand without compromisingperformanceorfunctionality.

 Reliability: The platform should be stable, with minimal downtime and quick recovery in case of failure,ensuringconsistentavailability.

 Usability: The user interface should be intuitive, easy to navigate, and accessible to users with differenttechnicalbackgrounds.

 Security:Theplatformmustensuretheprotection of user data through encryption, secure authentication, and compliance with security standardssuchasGDPR.

 Compatibility: The system should work across differentbrowsers,devices,andoperatingsystems, includingmobileplatforms.

 Maintainability: The platform should be designed foreasymaintenance,allowingforefficientupdates, bugfixes,andfeatureenhancements.

 Localization:Theplatformshouldsupportmultiple languagesandregionalsettingstocatertoaglobal userbase.

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

Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072

 Backup and Recovery: The system should include regulardatabackupsandrecoverymechanismsto ensuredataintegrityandpreventloss.

4.3

Hardware Requirements

 Server:Areliableserverwithsufficientprocessing power to handle AI-generated question requests, userinteractions,anddatabaseoperations.

 Processor:Amulti-coreprocessor(e.g.,Inteli5or higher)tosupporttheAIprocessingandbackend operations.

 RAM:Minimum8GBRAMforsmoothmultitasking andhandlingmultipleconcurrentusers.

 Storage: At least 50GB of SSD storage for storing userdata,logs,andapplicationfiles.

 Network:Stableinternetconnectionwithsufficient bandwidth to handle real-time data transmission for AI interactions, speech processing, and video content.

 Backup Storage: External or cloud storage for regular backups to ensure data security and recovery.

4.4

Software Requirements

 Operating System: Windows, macOS, or Linux for bothdevelopmentanddeploymentenvironments.

 Frontend: Next.js for building the user interface, alongwithShadCNUIComponentsforresponsive designandnext-themesforthememanagement.

 Backend:Node.jsforserver-sidedevelopmentand DrizzleORMfordatabaseinteraction.

 Database: PostgreSQL for storing user data, feedback,andsessionlogs.

 AI Integration: Google Gemini API for generating interviewquestions.

 Authentication: Clerk for user management and securelogin.

 SpeechProcessing:WebSpeechAPIforspeech-totextandText-to-SpeechAPIfordeliveringquestions inaudio.

 VersionControl:Gitformanagingthecodebaseand trackingchanges.

 Testing:JestforunittestingandCypressforend-toendtesting.

 Deployment: Vercel/Netlify for frontend hosting andHeroku/AWSforbackendhosting.

 Security: SSL/TLS encryption for secure data transmissionandOAuthforauthentication.

5.Algoritm

5.1 Feedback Analysis Algorithm

a. Analysisofspeechfeedback

Sentimentanalysiscombinesbag-of-words(BoW) orword embeddings(e.g. Word2Vec,GloVe) with machine learning or deep learning (e.g. logistic regression,LSTM).TheexpectedSvalueoftheuser responseiscalculatedasfollows:

Where:

M:Totalnumberofwordsinthe“response.extractor” . Add them together and give positive, neutral or negativefeedbackbasedonthethreshold(e.g.0.5S> 0.5ispositive).

Za:weightassignedtothea-thword

Ya:Sentimentvalueofthea-thword

b. Confidenceinnonverbalcommunication

The system measures confidence based on time delay, voice intonation, and word repetition. Weightedaverage:

Where:

P:Percentageofpausetimecomparedtototalverbaltime.

T:Tonevariation.

R:Wordrepeatfrequency.

5.2 Speech-to-Text Algorithm

Speech to text is implemented using Hidden Markov Models(HMM)thatconvertspeechtotextbyanalyzingtime.

Where:

Q(H|O):ProbabilityofwordsequenceQgiventhesignalO.

Q(O|H):Acousticmodellikelihood

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

Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072

Q(H):Languagemodelprobability

6.System Design

6.1 Use Case Diagram

Fig 1: Use Case diagram

6.2 System Architecture

Fig 2: System Architecture

7. Testing

"AI Powered Mock Interview Generator" has been rigorously tested to measure its performance and user satisfaction. The testing phase includes functionality, performance, and user reviews to ensure the platform meetsitsgoals.

7.1 Performance evaluation

 Response Time: The platform uses the Google GeminiAPItocreateinteractivequerieswithan average response time of 150 milliseconds, enabling close interaction. milliseconds to maintainuserengagement.

 Scalability:Stresstestswereconductedwith50 concurrentusersandtheplatform'sperformance was stable, demonstrating great potential forlargeusers.

 System uptime: During 10 days of simulation testing

7.2 User Satisfaction

A user study was conducted with 50 participants from variousprofessionstoassesssatisfactionandusability.Key benefitsinclude

 Ease of Use: 92% of respondents said the interfacewasintuitiveandeasytonavigate.

 EasytoNavigate:90%ofusersaresatisfiedwith thefeedbackgiven,especiallyonspeechanalysis.

8. Implementation and Results

Virtual Prep is developed using React and Next.js for the frontend,ensuringaseamlessuserexperiencewithdynamic UIcomponentsfromShadCN.Authenticationishandledvia Clerk,enablingsecureuserloginandsessionmanagement. The backend, powered by Drizzle ORM with Neon & PostgreSQL,storesuserresponsesandquestionsefficiently.

TheGoogleGeminiAPIgeneratesmockinterviewquestions based on user preferences, ensuring relevance. Users can respond via text or voice, with speech-to-text conversion processingrecordedanswers.Onceresponsesarestoredin the database, the system retrieves correct answers for comparison. AI-driven feedback then evaluates the user's response, assigns a rating, and generates a structured summary,highlightingstrengthsandareasforimprovement.

VirtualPrepsuccessfullydeliversaninteractiveandinsightful mockinterviewexperience.TheAI-generatedquestionsare diverseandtailoredtouserinputs.Speech-to-textconversion allows for natural interaction, improving usability. The feedbackmechanismprovidesaccurateevaluations,helping usersidentifyweaknessesandrefinetheiranswers.Database storage ensures a structured record of past attempts, enabling progress tracking. Overall, the system enhances interview preparation by simulating real-world scenarios, offeringpersonalizedfeedback,andhelpingusersimprove theircommunicationandproblem-solvingskills.

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

Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072

9. CONCLUSIONS

AIPoweredMockInterviewGeneratorrepresentsthenext revolutioninthewayjobseekersprepareforinterviewsby combining advanced AI technology with web design techniquestoproviderealsolutions.Theplatformaddresses the shortcomings of traditional interviewing methods by providinguserswithaflexible,interactiveandpersonalized approachthatreflectsreal-worldsituations.Inacompetitive and changing workplace, it could change the way people apply for jobs. First, it democratizes access to effective interviewingtools.TheplatformusessmarttoolslikeGoogle GeminiAPItoprovideinterview-specificquestions,allowing userstogetbetterresultsandworkmorespecifically.Unlike traditional question banks, this approach allows users to preparefornicherolesandspecificchallenges,broadentheir opportunities,andimprovetheirpractice.,textanalysis,and gesture planning features provide an effective method for interview preparation. No longer limited to written feedback,userscanbenefitfromunderstandingtheirverbal andnon-verbalcommunication.Thiswillgivethemabetter understandingoftheirstrengthsandareasforimprovement, andpreparethemforallaspectsoftheinterview,fromthe contentoftheiranswerstovoice,pace,andbodylanguage. Increasethevalueoftheplatform.Dashboardswhereusers can track their long-term progress, set success goals, and focus on specific areas of improvement through insights. Thisrelearningprocessnotonlyhelpsusersimprovetheir speaking, but also increases their confidence in their own abilities. This will ensure that users’ data and progress metricsaresecureandonlyaccessibletothem.Planning

Featuressuchasgestureandbodyrecognition,multilingual support,andintegrationwithjobportalswill increasethe usabilityandappealoftheplatform.

10. Future Enhancement

- Gesture and Body Language Analysis: Gesture and body language analysis using advanced computer videoanddeeplearningmodels.,gesturesandeye contacttogivegeneralinstructions.impact.

- MultilingualSupportFeatures:Expandtheplatform’s abilitytosupportmultiplelanguages,allowingnonEnglishspeakerstoprepareforinterviewsintheir ownlanguage.Expandtheplatform’sinternational reach and meet the needs of users with different languages.

- Seamless Job Portal Integration: A personal dashboardontheplatformwhereuserscansearch for jobs that match their skills and goals and prepareforjobs.

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