NEXTSTEP AI: All-in-One Hiring & Job Assistance Platform

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

Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN:2395-0072

NEXTSTEP AI: All-in-One Hiring & Job Assistance Platform

Angad Kumar1 , Divyanshu Kumar2 , Amanatullah3, Manoj Kumar Yadav4

1-4 Student Computer Science and Information Technology, Dronacharya Group of Institutions, U.P, India

5 Assist. Professor, Dept. of Computer Science and Information Technology, Dronacharya Group of Institutions, U.P, India

Abstract - The increasing complexity of remote technical recruitment necessitates integrated solutions that address both assessment accuracy and hiring efficiency. NEXTSTEP AI – All-in-One Hiring & Job AssistancePlatform is designed as a comprehensive, modular system that unifies collaborative coding environments, AI-driven interview analysis, automated resume screening, and secure video conferencing into a single intelligent hiring solution. This study presents the architecture, implementation, and performance evaluation of NEXTSTEP AI. The platform supports live coding using Socket.IO and Monaco Editor, WebRTC-based interview communication, resume parsing through TF-IDF and cosine similarity, and recruiter dashboards powered by AI analytics to ensure unbiased and data-driven decision-making. A simulated pilot deployment involving recruiters and software engineering candidates demonstrated notable improvements in candidate evaluation, recruiter efficiency, and overall user experience compared to traditional platforms. The results suggest that NEXTSTEP AI’s unified approach significantly enhances the fairness, speed, and effectiveness of remote hiring. Future work will focus on scalability to support enterprise-level loads, broader role inclusion beyond software development, and deeper AI integration to further personalize the recruitmentexperience.

Key Words: AI-Powered Recruitment Platform, RealTime Code Collaboration, WebRTC Video Interviews, Automated Resume Parsing, Collaborative Coding Environment.

1.INTRODUCTION

The rapid evolution of remote work and global talent acquisition has significantly reshaped technical recruitment, driving the need for platforms that support real-time interaction, unbiased evaluation, and seamless userexperience.Traditionaljobportalsoftenrelyonstatic assessments, manual resume screening, and fragmented tools, which limit their effectiveness in accurately identifying top technical talent particularly in remotefirstenvironments.

While several platforms offer solutions like resume filtering,codingtests,orvideointerviews,theyfunctionin isolation. This fragmentation results in inefficient

workflows and inconsistent evaluations. Static coding assessments fail to simulate real-world collaboration, and interviews conducted separately from coding tasks hinder a holistic viewofcandidatecapabilities.Moreover, manual resume reviews are time-consuming, error-prone, and often biased due to subjective judgment or keyword dependency.

To address these gaps, there is a growing demand for an integrated recruitment solution that combines real-time collaborative coding, AI-driven evaluation, resume automation, and secure video communication within a single platform. Such a system would enhance recruiter productivity, streamline the hiring process, and offer candidates a more engaging, realistic assessment experience.

NEXTSTEP AI – All-in-One Hiring & Job AssistancePlatform fulfils this need by offering a unified, scalableplatform.ItincorporateslivecodingusingMonaco Editor and Socket.IO, AI-based candidate analysis, resume parsing via TF-IDF and cosine similarity, and WebRTCpowered video interviews. Recruiter dashboards provide actionableinsightstosupportdata-drivenhiringdecisions.

This paper discusses the motivation, architecture, and implementation of NEXTSTEP AI, alongside its simulated evaluation, to demonstrate its impact on improving fairness, efficiency, and effectiveness in modern technical recruitment.

1.1 Objectives

TheprimaryobjectiveofNEXTSTEPAI–All-in-OneHiring &JobAssistancePlatformistodesignaunified,intelligent recruitmentplatformthatstreamlinesthe technicalhiring workflow by integrating collaborative coding tools, automated resume analysis, AI-powered evaluations, and secure video communication within a single, scalable system. By merging key recruitment functionalities into a modular architecture, NEXTSTEP AI eliminates the inefficiencies and subjectivity often associated with conventional hiring platforms. The platform is developed with a strong focus on automation, data security, and seamlesscandidate-recruiterinteraction,aimingtodeliver afaster,fairer,andmoreeffectivehiringexperience.

International Research Journal of Engineering and Technology (IRJET) e-

Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN:2395-0072

 To implement a modular, real-time collaboration environment for technical interviews. The platform incorporates a Monaco-based collaborative code editor synchronized through Socket.IO, enabling recruiters and candidates to code simultaneously in real-time. Combined with EMKC’s multi-language code execution and live WebRTC-based video sessions (secured via STUN/TURN servers), this feature replicates realworld pair programming and facilitates deeper assessment of coding skills and communication abilities.

 To develop robust, role-based access control and secure authentication mechanisms. Using OAuth 2.0 and JSON Web Tokens (JWT), NEXTSTEP AI enforces secure login, session handling, and user authorization. All sensitive data, including interview footage and submitted code, is encrypted and stored securely, ensuring compliance with privacy standards and reducing risksofdatabreaches.

 To integrate automated resume parsing and intelligent job matching tools. Resumes are parsed using natural language techniquesincludingTF-IDFandcosinesimilarity, which enables the platform to match candidate profileswithjobdescriptionsmoreaccuratelyand efficiently.Recruitersreceivepre-screened,ranked applications, significantly reducing manual screeningtime.

 To deploy AI-powered evaluation modules that support data-driven hiring decisions. The system features recruiter dashboards enhanced with AI analytics that offer automated scoring, code quality metrics, and behavioural insightsduringliveinterviews.Thesetoolsreduce bias, promote consistency, and enhance the transparencyofevaluationprocesses

 To unify real-time communication with collaborative assessment tools in a single interface.

By combining coding, video, chat, and evaluation tools within one window, the platform eliminates tool-switching and enhances user experience for both recruiters and candidates, streamlining the entireinterviewworkflow.

 To empirically evaluate system usability, performance, and recruiter satisfaction through simulated pilot deployments. Benchmarks such as code sync latency, resume parsing accuracy, AI-human score alignment, and platform uptime are tracked to validate JobZee’s performanceunderrealistichiringscenarios.

 To identify potential scalability and domainspecific limitations and guide future platform enhancements.

Future work will include support for non-technical roles, improved load balancing for large-scale enterprise usage, and the integration of collaborative analytics, ATS compatibility, and multilingual coding environments to increaseaccessibilityandadoption.

1.2 Methodology

This study employed a structured, mixed-methods engineeringapproachtodesign,develop,andevaluatethe NEXTSTEPAIplatformoversixiterativephases.

1.2.1 Requirements Analysis

Foundationalinsightswerecollectedthrough:

 Literature Review: A comprehensive review of 150+academicandindustrysources(2015–2023) from IEEE, ACM Digital Library, and Springer exploredcurrentlimitationsindigitalhiringtools, live coding platforms, and AI-driven recruitment workflows.

 Comparative Benchmarking: Analysis of leading platforms such as Hacker Rank, LeetCode, CodePair,andHireVueidentifiedfunctionalgapsin collaborative coding, candidate assessment accuracy,andinterfaceintegration.

 Stakeholder Interviews: Semi-structured interviews with 10 recruiters and 15 job candidates revealed challenges in traditional hiring such as tool-switching, bias in manual evaluations, and a lack of real-time collaboration features.

Key insights emphasized the need for integration, scalability,real-timefeedback,andbias-freeautomationin recruitmentsystems.

1.2.2 System Design

Athree-tierarchitecturewasadopted:

 Frontend: React.js with Tailwind CSS, providing responsiveUI

 Backend: Node.js with Express.js for RESTful APIs.

 Database Layer: MongoDB Atlas for documentbased storage and Cloudinary for secure media handling(e.g.,resumesandcoverletters).

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

Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN:2395-0072

JWT-based authentication was implemented alongside OAuth2.0forrole-basedaccesscontrol. Figmaprototypes were evaluated with 20 users to refine UX for both recruitersandcandidates.

1.2.3 Agile Development

ThesystemfollowedaScrum-basedagileworkflowacross sixtwo-weeksprints:

 Sprints 1–2: Core features user registration, role-basedlogin,dashboardUI,andjobposting.

 Sprints 3–4: Real-time code editor integration usingMonacoEditorandSocket.IO,jobapplication module, and resume parser (TF-IDF + cosine similarity).

 Sprints 5–6: WebRTC interview system with STUN/TURN setup, recruiter analytics, and AIdrivenevaluationcomponents.

Test-Driven Development (TDD) practices ensured 85%+ unit test coverage. GitHub Actions supported CI/CD with automaticbuildanddeployment.

1.2.4

Integration & Evaluation

Systemintegrationtestingvalidated:

 Live Code Synchronization: Via WebSocket and EMKCAPIforreal-timecompilation.

 Interview Workflow Stability: Tested under 200 concurrentusersessionsusingJMeter.

 Security Measures: OWASP-based vulnerability scans, rate-limiting, CORS policies, and encrypted passwordstorage.

 Usability Testing: Conducted with 30 users using the System Usability Scale (SUS), targeting recruiter experience, response time, and flow consistency.

1.2.5 Data Analysis

Platformperformancewasevaluatedacross8metrics:

 Code sync latency, resume parsing accuracy, AI–recruiter evaluation alignment, and candidate satisfactionrates.

 Real-time metrics were logged using MongoDB analytics and compared to industry benchmarks (e.g.,CodePair,HackerRank).

 Feedback themes were coded in NVivo to identify UXpainpointsandareasforimprovement.

Results demonstrated improved recruiter efficiency, reducedtool-switching,andincreasedsatisfaction

1.3 Implementation

1.3.1

System Architecture Design

NEXTSTEP AI follows a modular, full-stack architecture to enable real-time collaboration and AI automation in recruitment.

Frontend Architecture:

 React.js with Context API and Redux Toolkit for statemanagement.

 TailwindCSSforresponsive,unifiedUI.

 Monaco Editor for collaborative, in-browser coding.

 WebRTC for low-latency video calls during interviews.

Backend Services:

 Node.js with Express for RESTful API development.

 MongoDB Atlas for storing jobs, users, and analytics.

 Cloudinary for resume/media storage and retrieval.

1.3.2

Security Implementation

 OAuth 2.0 and JWT for secure, role-based authentication.

 BCryptencryptionforpasswords.

 Isolatedrolepermissionsfordifferentusers.

 Secure cookies, CORS, and encrypted WebSocket/WebRTCprotocols.

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

Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN:2395-0072

1.3.3 Evaluation Framework

 Latency:Codesync(65ms),videodelay(150ms).

 Security: OWASP-compliant audits and token validation

 Usability:SUStestingwith30users.

 Integration: JMeter tests and >85% unit test coverage.

1.3.4 Core Functionalities Implemented

 Userroles,registration,loginwithsecureaccess.

 Jobpostingandapplicationworkflows.

 Livecodeeditorwithreal-timeexecution.

 WebRTCinterviewswithSTUN/TURNsupport.

A. Sample API Endpoints:

Table-1: UserAuthenticationEndpoints

Method Endpoint Description

POST /api/v1/user /register Register new user (Job Seeker/Employer)

POST /api/v1/user /login Userloginwithcredentials

GET /api/v1/user /logout Logoutcurrentuser

Table-2: JobManagementEndpoints

Method Endpoint Description

GET /api/v1/job/getall Getallavailablejobs

POST /api/v1/job/post Post new job (Employeronly)

GET /api/v1/job/getmyjobs Get employer's postedjobs

PUT /api/v1/job/update/:id Updatejobposting

DELETE /api/v1/job/delete/:id Deletejobposting

GET /api/v1/job/:id Getsinglejobdetails

Table-3: ApplicationManagementEndpoints

Method Endpoint Description

POST /api/v1/application/post Submit job application

GET /api/v1/application/emp loyer/getall Get all applications (Employer)

GET /api/v1/application/jobs eeker/getall Get user's applications

DELETE /api/v1/application/dele te/:id Deleteapplication

Table-4: ResumeManagementEndpoints

Method Endpoint Description

POST /api/createpdf GenerateresumePDF

GET /api/fetch-pdf Downloadgeneratedresume

Table-5: ChatbotEndpoints

Method Endpoint Description

POST /api/v1/chatbot/start Startchatsession

POST /api/v1/chatbot/message Send message to chatbot

POST /api/v1/chatbot/end Endchatsession

POST /api/v1/chatbot/fallback Get offline mode responses

Table-6: InterviewSystemSocketEvents

Event Direction Description join-room ClienttoServer Joininterviewroom message Bidirectional Codeeditorsync changelanguage Bidirectional Languageselectionsync text-change Bidirectional Texteditorsync call-user Bidirectional Startvideocall call-accepted Bidirectional Acceptincomingcall

2:FunctionalArchitectureofNEXTSTEPAI

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2. RESULTS AND FINDINGS

The evaluation of the NEXTSTEP AI platform was conducted through a simulated pilot involving 20 participants comprising 5 recruiters and 15 software engineering candidates from three early-stage startups. Over a three-week period, the platform was assessed across five core dimensions: functionality, usability, performance, cross-platform support, and AI-assisted evaluationaccuracy.

2.1 Usability and User Experience

The System Usability Scale (SUS) was administered after eachparticipantcompletedliveinterviewsimulations.The average SUS score was 85.7 (SD = 4.9), indicating strong user satisfaction and system intuitiveness. Recruiters noted streamlined workflows due to unified dashboards, and candidates appreciated the minimal learning curve required for using the code editor and video tools. Platform analytics indicated an average interview session duration of 47 minutes, with a 91% task completion rate across features such as code editor, resume submission, andfeedbackreview.

2.2 Functional Validation and Performance

System functionality was validated through unit and integrationtests.Keyresultsinclude:

 Real-time code synchronization was successful acrossmultipleparticipantswith<70mslatency.

 WebRTC-based video interviews demonstrated stable performance across network conditions, with98.5%uptime.

 Resume parsing via TF-IDF and cosine similarity achieved 91.2% accuracy against manually benchmarkedmatches.

 Post-interview reports were generated automaticallywithinanaverageof2.3seconds.

Table-3: PerformanceMetrics

Metric Result Benchmark/ Threshold

Code SyncLatency (avg)

Video Stream Latency(avg)

Resume Parsing Accuracy

(vs manually labeleddata)

AI Interview AnalysisAccuracy 88.6% agreement with expertratings >80% agreement threshold

Interview Report GenerationTime 2.3seconds

Server

Stress testing using JMeter confirmed reliable operation with up to 250 concurrent users. EMKC-based code executionaveragedaresponsetimeof1.4seconds.

2.3 Cross-Platform and Mobile Compatibility

The system was tested on major browsers (Chrome, Firefox, Edge) and OS environments (Windows 10/11, macOS Ventura, Ubuntu). Responsive behaviour was verified across Android and iOS devices. Mobile compatibility-maintained core feature availability with a fallbackUIforvideoandeditorcomponents.

2.4 Recruiter and Candidate Feedback

Qualitative feedback through structured post-interview surveysrevealed:

 85% of recruiters reported reduced interview preparation time due to integrated evaluation dashboards.

 93%ofcandidatesratedtheirexperienceasbetter thantraditionalvideointerviews.

 Participants highlighted clarity in assessments enabled by AI-generated metrics and behavioural insights.

AcasestudysimulationdemonstratedthatNEXTSTEPAI’s all-in-one model significantly reduced tool-switching, improved communication, and increased recruiter confidenceinhiringdecisions.

2.5 Security and Compliance Testing

TheplatformunderwentsecurityevaluationunderOWASP guidelines.Findings:

 JWT-based API protection with token expiry and replayattackmitigation.

 Encryption protocols: TLS for in-transit data and AES-256forstoredinterviewdata.

 CORS policies and environment-based variable managementensuredfrontend–backendisolation.

Nodatalossorbreachincidentswerereportedduringthe pilot. All API endpoints passed automated security validationscripts.

2.6 Identified Limitations and Scalability Insights

The platform, while functionally robust, exhibited the followingconstraints:

 AI scoring, though effective, occasionally misinterpreted subtle context or language nuancesincandidateresponses

 Scalability beyond 250 concurrent users remains under optimization for enterprise-scale deployments.

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

 Stableinternetwasrequiredforoptimalreal-time interaction;videoandeditorsyncdegradedunder poorconnectivity.

 Current language support is limited to JavaScript, Python, and Java. Expansion to non-tech roles requiressignificantarchitectureupdates.

2.7 Comparison to Traditional Platforms

Table-3: TraditionalPlatformsVSNEXTSTEPAI

Aspect Traditional Platforms

Coding Assessment Static tests or take-home challenges

Interview Tools Separate video +codingtools

Proposed System

Real-time collaborative coding

Integrated video, code, andAI

Evaluation Process Manual, subjective, and inconsistent AI-assisted, standardized, datadriven

Resume Screening Manual filtering, often keyword-based

Candidate Engagement Passive, onesided

Scalability and Speed Timeconsuming and fragmented

Automatedparsingwith skillmatching

Interactive, real-time, collaborative

Faster, unified, and streamlined

Compared to legacy hiring platforms, this system offers a modern, interactive, and AI-augmented approach that aligns with the needs of tech-driven, remote-first organizations.

3. CONCLUSIONS

NEXTSTEP AI – All-in-One Hiring & Job AssistancePlatform delivers a comprehensive, scalable solution to streamline and modernize technical hiring. By unifying live collaborative coding, resume parsing, video interviews,andAI-drivenevaluationinasingleplatform,it addresses major inefficiencies and biases inherent in traditional recruitment. Tools such as the Monaco-based editor, Socket.IO sync, and WebRTC video enable realworld interview simulations and improve evaluation accuracy. Recruiter dashboards and intelligent resume matching further reduce decision-making time while improvingquality.

The system architecture built using Node.js/Express, MongoDB Atlas, Cloudinary, and React.js supports modular design and seamless interactions between job seekers and recruiters. OAuth 2.0 and JWT secure rolebased access, and scalable APIs enable future integration withATSplatformsandadvancedanalytics.

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Evaluation through simulated interviews and crossplatform tests validated system robustness. Users noted higher satisfaction, reduced prep time, and improved assessment clarity. Resume parsing showed 91.2% accuracy; AI-generated insights aligned closely with human evaluations. Platform performance remained strong under 250 concurrent users, with low latency and broadOS/devicecompatibility.

Future improvements include support for additional languages (Rust, Swift), broader role coverage (UI/UX, project management), mobile enhancements, and offline access. Deeper AI integration is needed for real-time feedback, interview guidance, and predictive hiring analytics.

Enterprise readiness will require load balancing, horizontal scaling, and GDPR-compliant data flows. ATS integration, peer-review options, and fairness-aware AI models will help foster inclusive, scalable hiring ecosystems.

In conclusion, NEXTSTEP AI represents a next-generation recruitment platform that enhances decision accuracy, reduces hiring friction, and elevates both recruiter and candidate experiences. With iterative development and user-centered innovation, it sets the foundation for the futureofremotetechnicalhiring.

4. References

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[5] Haines, S. (2019). Socket.IOReal-TimeWebApplication Development.PacktPublishing.

[6] IBM Research. (2021). AI Fairness 360 Toolkit: Metrics andAlgorithmsforExaminingBiasinMachineLearning Models.Retrievedfromhttps://aif360.mybluemix.net

[7] MongoDB Inc. (2024). MongoDB Documentation Retrievedfromhttps://www.mongodb.com/docs/

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

Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN:2395-0072

[8] OpenAI. (2023). GPT and Codex Models for Code Understanding and Generation. Retrieved from https://openai.com/research

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