PrepWise: An AI-Based Interview Preparation Platform

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

Volume: 13 Issue: 01 | Mar 2026 www.irjet.net p-ISSN: 2395-0072

PrepWise: An AI-Based Interview Preparation Platform

Isha Mahadik1 , Isha Patil2 , Anuja More3 , Shubhangi Patil4

1Department of Computer Engineering, MGM's College of Engineering and Technology, Navi Mumbai, India

2Department of Computer Engineering, MGM's College of Engineering and Technology, Navi Mumbai, India

3Department of Computer Engineering, MGM's College of Engineering and Technology, Navi Mumbai, India

4Professor, Department of Computer Engineering, MGM's College of Engineering and Technology, Navi Mumbai, India

Abstract - Preparing for an interview is very important for your career growth, but many people don’t have access to tailored, organized, and realistic practice for interviews. Traditional ways of preparing, like using fixed question lists and practice interviews, don't give personalized feedback or evaluate skills based on specific job roles. To fix these issues, this paper introduces PrepWise, an AI-powered toolthat helps people prepare for and practice interviews. It creates realistic interview situations and gives automatic feedback to help users improve. The system lets users pickthe area they want to interview in and choose how hard the questions should be. Then it creates suitable questions for them to answer. Users can send in their answers and get immediate feedback onhow they did. The system uses web technologies along with AI methods to look at how users respond and give useful feedback, which helps keep improving over time. PrepWise is set up as a web-based program so that it can be easily accessed, handled by many users, and used without much difficulty. The platform is designed to help people get better at interviews by providing organized practice, instant feedback, and ways to keep track of their progress. Studies show that this system helps users get more involved and prepares them better for interviews thanusual ways. The suggestedapproach shows how AI systems can help with preparing for a career and evaluating skills.

Key Words: Interview Preparation, Artificial Intelligence, Web Application, Automated Evaluation, NLP, Career Guidance

1. INTRODUCTION

Preparingforaninterviewisaveryimportantpartofthe hiring process, because it plays a big role in helping a job seeker get hired. Many candidates have enough technical skillsbutstillfindithardtodowellininterviewsbecause theydon'tpracticeinastructuredway,lackconfidence,and don'tgettimelyfeedback.Traditionalwaysofpreparingfor interviews,likereadingcommonquestions,takingcoaching classes,ordoingpracticesessionswithfriends,usuallydon't work well. They take a lot of time, aren't very tailored to individualneeds,andcanbehardtorelyon.

As web technologies and artificial intelligence have improved,automatedsystemsforpreparingforinterviews have become a popular and effective option compared to

older methods. Many current websites offer fixed sets of questions or video interviews that don't change based on howwellauserisdoingorwhatjobtheyareapplyingfor. These tools don't adjust to fit each person's needs or the specificrequirementsofdifferentpositions.Moreover,realtime evaluation and detailed feedback are often missing, whichmakesthesesystemslesseffective.

Tosolvetheseissues,thispaperintroducesPrepWise,an AI-poweredtoolthathelpspeopleprepareforandpractice interviewsinawaythatfeelslikearealinterviewsituation. Thesystemletsuserschooseareastointerviewin,practice questions that are specific to their role, send in their answers, and get automatic feedback. PrepWise uses AI methods along with a website to offer organized, flexible, andeasy-to-useinterviewpractice.

Thekeypartofthisworkiscreatingasmartsystemthat helpspeopleprepareforinterviewsinapersonalizedway and keep improving their skills over time. The suggested system shows how using AI can help people get better prepared for their careers and improve their chances of doingwellinjobinterviews.

2. LITERATURE REVIEW

Theevolutionofdigitallearningenvironmentshasledto theemergenceofseveraltoolsdesignedtoassistcandidates in interview preparation. During the review of existing platforms, it was observed that many systems rely on predefined question banks and manual evaluation approaches, which offer limited adaptability to individual learning requirements. Such methods often fail to capture performancevariationsacrossdifferentrolesandexperience levels.

Recentresearchdemonstratesthepotential ofartificial intelligenceandnaturallanguageprocessingtechniquesin automating interview-related processes. Some AI-enabled platformsprovidepreliminaryevaluationofcommunication skills and domain knowledge; however, their functionality remains constrained by static question sets and restricted personalization capabilities. From an implementation perspective, this limitation reduces their effectiveness in practicalrecruitmentpreparationscenarios.

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

Volume: 13 Issue: 01 | Mar 2026 www.irjet.net p-ISSN: 2395-0072

Moreover, although online learning systems are very helpfulintermsoftheoverallskillimprovement,accordingto our analysis, they hardly focus on the interview-specific assessment.Lackofadaptivedifficultyadjustmentandreal time feedback systems may adversely affect the user engagementandtheresultsiniterativelearning.

Resting on these remarks, one may assume that the question of the unified solution, which involves individual interview simulation, automatic assessment, and the structured feedback, remains unexplored. The proposed PrepWisesystemisdrivenbythisgapofresearchandwill provide role-based practice sessions with automated feedbackandanalysisofperformance.

3. PROBLEM STATEMENT

In the initial research conducted on the resources of interview preparation, it was established that candidates usuallyhaveaproblemingettingstructuredandjob-specific practice opportunities. Current solutions usually involve repositoriesofstaticquestionsthataremanuallygradedand leadtoalowlevelofpersonalizationandlackofassociations withspecificlevelsofcompetence. Theotherlimitationthatiswitnessedislackofautomated evaluationandreal-timefeedback.Intheabsenceofprompt feedback on performance, candidates might be unable to recognizewhattheyneedtoworkon,ortheywillnotbeable to notice progress in a consecutive practice session. Moreover,classicwaysofmockinterviewstakequitealong timeandmightnotbereadilyavailabletoeveryuser. These results underline the importance of a versatile and smartinterviewpreparationmodelthatcanofferadaptive practice spaces, computerized assessment and feedback. Thistypeofsystemwouldbeabletoengageintheprocessof iterative learning and could help candidates prepare in generaltotheinterview.

4. PROPOSED SYSTEM (PREPWISE)

PrepWise platform was created in order to curb the shortfalls that had been realized in the current interview preparationprocedures.Toachievethis,thesystemissetto simulateinterviewingsituationsbasedondynamicquestion generationandautomatedresponseassessment.PrepWiseis a web-based application that is implemented and allows accessacrossdevicesaswellasscalabilitytomanyusers. Ontheimplementationsideofthematter,theplatformwill usebasicfeatureslikeuserauthentication,domainchoice, and adjustable difficulty. Through the user interface, interviewquestionsarecreatedanddisplayedbasedonthe parametersthathavebeenselected.Theevaluationmodule receives the submitted responses and evaluates their relevanceandclaritybeforeproducingstructuredfeedback. Thesystemoffersanadaptiveenvironmentbyallowingskill improvementbymeansofincrementalandrepeatedpractice and performance tracking. The combination of AI-based

assessment and web technologies enables the platform to provide a viable and effective method of interview preparation.

5. SYSTEM ARCHITECTURE

PrepWise architecture is the structural arrangement and interactionofthesystemcomponentsthatwillprovidethe services of interview preparation. The architecture has focused on modularity and scalability and also favored effective communication between the interface layer, application server and AI-based evaluation module. The generalplanofthearchitectureisshowninFig.1.

Thesystemhasanumberoffunctionalmodules:

User Interface Module:

It is a browser-based module where users can carry out taskslikeregistration,authentication,selectingtheinterview andenteringtheirresponsesandvisualizingthefeedback.It is the interface that exists between the user and the platform.

Backend Server Module:

Thebackendparthandlesapplication-levellogicandisin charge of the inter-module communication. It manages session, processing requests, delivery of questions and collectionofresponsesbesidesmaintainingreliabilityofthe system.

AI Evaluation Engine:

This module will be involved in generating interview questions and automatically analysing the responses of users. Relevance, correctness, and clarity are the factors used in the evaluation process to generate structured feedback.

Database Module:

Thedatabasealsokeepsacontinuousowningofuserdata, generatedquestions,receivedanswers,andtheresultsofthe evaluation, which allows a measure of performance, and retrieval of past data. The synchronized work of these modules facilitates the smooth flow of work, with user

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

Volume: 13 Issue: 01 | Mar 2026 www.irjet.net p-ISSN: 2395-0072

inputsareprocessedbytheservicesoftheback-end,being analyzedthroughAImechanisms,andstoredtobereused laterbeforetheresultsaredisplayedthroughtheinterface.

6. METHODOLOGY AND IMPLEMENTATION

PrepWisemethodologyprovidesaprocedurethatshouldbe takenduringaninterviewpractice.Thesystemembracesa methodical process of work, which allows automatic assessmentandcreationoffeedback.Fig.2representsthe processflow.

– 2: FlowchartofInterviewPreparationProcessin PrepWise

First,userauthenticationisdonetocheckaccesscredentials. Whentheysuccessfullylogin,usersdefinetheparametersof aninterview,suchasdomainanddifficultylevel.According to these inputs, the AI engine will dynamically generate pertinentinterviewquestions.

The responses are entered by the users over the web interfaceandthenthebackendserverisprocessedandsent toevaluationmodule.IntheAIevaluationmechanism,the responses are evaluated based on predefined criteria including: contextual relevance, conceptual accuracy, and clarityofexpression.

Afterevaluation,thestructuredfeedbackiscreatedanditis storedinthedatabasewiththeperformancemeasures.The resultsarethenshown totheuserandasinglepractice is completeintermsoftheinterview.Themethodologywillaid initerativelearningasitallowsrepeatingthepracticeand constantlymonitoringtheperformance.

7. RESULTS AND DISCUSSION

A system test was carried out to test the functional performance and interaction with the users in simulated interviewsessions.Ithasbeenobservedthattheplatform was able to create role specific questions and provide automated feedback in a uniform manner. The interface design helped to navigate the session and assist in the effectiveinteraction.

Theanalysisofuserengagementshowedthattherewasan increase in participation due to the benefits of real-time feedback and adjustive practice. Moreover, AI-based question generation was used to increase contextual matching,whereastheintegrationofdatabaseswasusedto tracktheperformanceeffectively.

These results indicate that the PrepWise site can offer a scalable and viable solution of structured interview preparationaswellasautomatedskillevaluation.

Table 1: ComparisonofInterviewPreparationMethods

Feature Traditional Methods PrepWiseSystem

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

Volume: 13 Issue: 01 | Mar 2026 www.irjet.net p-ISSN: 2395-0072

Tablesandgraphicalanalysisareusedtopresenttheresults anddeterminetheefficacyofthePrepWisesystem.Table1 compares the conventional interview preparation techniquesandthesuggestedsystem.PrepWiseisseen to have superior personalization, real-time feedback, and automatedevaluation.

The diagrammatic display shows that there was gradual improvementinuser performanceafter repeatedpractice sessions. This implies that the system is efficient in preparing and learning users in a system that provides constantfeedbackanddynamicquestioning.

8. CONCLUSION AND FUTURE SCOPE

In this paper, the authors introduced the concept of PrepWise a web-based interview preparation system that combinesautomatedquestiongenerationandperformance appraisal features. The system fills shortcomings of traditional preparation method offering role-specific practice and systematic feedback via an easy-to-use interface.

Theexperimentshowsimprovedengagementandincreased interviewpreparationwiththehelpoftheiterativepractice andaconstantfeedbacksystem.Theplatformminimizeson the use of manual evaluation and aids in systematic monitoringofperformance.

The next step development can be aimed at integrating speech-basedinterviewsimulator,resume-basedquestion generator,andadvancedperformanceassessmentanalytics. Other upgrades (e.g., multilingual support and real-time mockinterviews)canhelptoincreasetheapplicabilityofthe systemandreinforceitspurposeasatooltopreparepeople tothework-relatedlife.

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