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AI HireMap: A Survey Paper

p-ISSN: 2395-0072

Anusha

Karakanti1 , Kaveri Kallennavar2 , Chandana Hiremath3 , Deepa Kyadiggeri4 , V.S.Patil5

1,2,3,4Student , Department of Information Science and Engineering , Basaveshwar Engineering College, Bagalkote, India

5Assistant Professor, Department of Information Science and Engineering ,Basaveshwar Engineering College, Bagalkote, India.

Abstract-AIHireMapisasmartplatformdesignedtohelp students and job seekers improve their employability. The system allows users to upload their resumes, which are scanned using artificial intelligence to extract skills. It highlights the skills the user already possesses, identifies missingskills,andprovidesrecommendationsforlearning resources such as online courses and tutorials. The platform’s user-friendly interface ensures a simple and transparent experience, allowing users to quickly understand their strengths, weaknesses, and areas for improvement. Byfocusingsolelyonresumeanalysis,skillgap detection, and actionable learning guidance, AI HireMap empowers users to take concrete steps toward career development. Ultimately, the platform helps job seekersbecomemorecompetitive,informed,andprepared for employment, making the process of finding the right jobmoreefficientandaccessible..

Keywords-Resume Parsing, Skill Extraction, Missing Skills Detection, Skill Gap Analysis, Personalized Learning Resources, Career Guidance, Student Career Support, Learning Recommendations, Resume Analysis, Machine Learning for Resume Analysis, Skill Development, Career Advancement.

1. Introduction

AI HireMap is an AI-driven platform designed to support students and job seekers in improving their career readiness and employability. In today’s competitive market,manycandidatesstruggletoidentifytheskillsthey lack compared to industry demands, while traditional career guidance systems often remain generic and ineffective. To overcome these challenges, AI HireMap leverages intelligent automation to analyze resumes, extract existing skills, and detect gaps against job requirements. The platform is developed using Django (Python) for the backend and MySQL for data storage, ensuringsecureandscalableperformance.Auser-friendly interface is built with HTML, CSS, and JavaScript, allowing seamlessnavigationandinteraction.Atitscore,anAISkillMatching Engine provides personalized recommendations tohelpusersbridgetheirskillgaps.

2.Objectives

1. To analyze resumes using AI and extract key skills accurately, giving users a clear understanding of their existingcapabilities.

2. To design a user-friendly interface that allows simple, transparent,andsmoothnavigationforallusers.

3. To support career development by highlighting strengths and weaknesses, enabling users to focus on areasforgrowth.

4.Toenhanceefficiencyinjobpreparationbyreducingthe time and effort required for skill matching and readiness andenablefasterandmorefocusedcareerpreparationby providingactionableinsightsonskilldevelopment.

5. To empower users with insights for informed decisionmaking, helping them choose the right career paths and opportunities.

6. To promote continuous improvement by motivating userstoregularlyupgradeandadapttheirskillstomarket trends. To track user progress over time, providing feedbackonskillacquisitionanddevelopment.

2.Related Work

[1] The paper “Enhancing Student Placement Preparation Through Web Application” focuses on developing an integrated web-based platform to assist students in improvingtheiremployabilityandplacementreadiness.In today’s highly competitive job market, effective preparationforplacementdriveshasbecomeessentialdue to economic uncertainties and frequent layoffs. Many studentsfacechallengesinaccessingstructuredresources and personalized guidance. The proposed system aims to bridgethisgapbyofferingacomprehensive,user-friendly, and interactive web application that consolidates various aspects of placement training into a single platform. The system provides multiple modules covering aptitude practice, technical tests, coding challenges, resumebuildingassistance,andinterviewpreparation.Itsupports

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

Volume: 12 Issue: 10 | Oct 2025 www.irjet.net p-ISSN: 2395-0072

both technical and non-technical skill development to ensureholisticreadinessforplacementopportunities.One of the major highlights of the platform is its personalized learning path feature, which classifies students as fast, medium, or slow learners. This categorization enables the placement cell and faculty to provide targeted guidance, additional questions, and special sessions for weaker students. The inclusion of real-time practice assessments, constructive feedback, and progress tracking allows students to continuously evaluate and enhance their performance.

[2] The paper “Developing an Intelligent Resume Screening Tool With AI‐Driven Analysis and Ranking” presents an innovative approach to automating the recruitment process using Artificial Intelligence (AI). Traditional resume screening is a time-consuming and subjective process that often leads to inefficiency and bias in candidate selection. The proposed AI-driven system aims to address these challenges by introducing an intelligent model capable of automatically analyzing, ranking, and shortlistingresumesbasedonjobrequirements.Thepaper concludes that AI-powered resume screening tools can revolutionize recruitment by providing data-driven, unbiased, and scalable solutions for talent acquisition. However, the authors acknowledge certain limitations suchasdataquality,varyingresumeformats,andpotential ethicalconsiderationsinautomateddecision-making.They suggestfuturework inareassuchasintegratingadvanced NLP models (e.g., BERT or GPT), improving multilingual support, and enhancing interpretability for HR professionals.

[3] The reviewed paper, “A Review of Resume Analysis and Job DescriptionMatchingUsingMachineLearning” (Modak et al., 2024), presents a comprehensive study on how machine learning (ML) and natural language processing (NLP)techniquesaretransformingtherecruitmentdomain by automating the process of matching resumes with job descriptions.Theauthorsemphasizethatinthemodernjob market, efficient candidate–job alignment is crucial for optimizing talent acquisition and reducing the time and bias associated with manual screening. The paper outlines the main stages of automated resume and job description matching. These include resume parsing, skill extraction, job description analysis, similarity measurement, and candidate-jobmatching.

[4] The paper “A Survey on Resume Analysis Using NLP” byHarshithaR.andVeenaB.(2024)exploreshowNatural Language Processing (NLP) and Machine Learning (ML) techniques can automate and optimize the recruitment process by parsing and analyzing resumes. The study

focuses on improving efficiency, fairness, and accuracy in candidate selection while reducing manual workload in hiring processes. The authors begin by explaining that traditional recruitment methods in India, which involve manual screening of resumes, are highly time-consuming and inefficient, especially when companies hire in bulk. The Indian job market’s rapid expansion, with millions of new job seekers annually, increases the need for automated systems capable of analyzing large volumes of unstructuredresumedata.NLP-basedsystemscanextract relevant information such as education, experience, and skills fromresumesandpresentitinastructuredformat thatiseasierforrecruiterstointerpret.

[5]Thepaper“ASurveyonResumeParsingUsingNLPand Machine Learning Techniques” discusses the growing importance of automating the recruitment process using NaturalLanguageProcessing(NLP)andMachineLearning (ML) technologies. The study highlights how resume parsing extracting structured data from unstructured resumes can significantly enhance recruitment efficiency,reducemanualeffort,andimprovetheaccuracy of candidate-job matching. The authors begin by emphasizing that the recruitment process has become increasingly complex with the exponential rise in job applicants. Manually screening resumes is timeconsuming, prone to human bias, and often inconsistent. Since resumes come in various formats and structures, identifyingrelevantdetailslikeeducation,experience,and skills becomes a tedious task for recruiters. The integration of NLP and ML aims to overcome these challenges by enabling systems to automatically read, interpret,andanalyzeresumesinahuman-likemanner.

[6]Thepaper“JobPosting-EnrichedKnowledgeGraphfor Skills-Based Matching” by Maurits de Groot, Jelle Schutte, andDavidGrausaddressesthegrowingneedfordynamic and data-driven tools to bridge the gap between job seekers’ skills and employers’ requirements in an everchanging labor market. Rapid globalization, digitization, and the COVID-19 pandemic have accelerated changes in occupational demands, increasing the importance of skills-based matching for employment and career development.TheauthorsproposeconstructingaSkills& Occupations Knowledge Graph (KG) that integrates structured taxonomies specifically the International Standard Classification of Occupations (ISCO) and the European Skills, Competences, Qualifications and Occupations(ESCO) withunstructuredjobpostingdata. This hybrid approach ensures that the KG reflects the current state of the labor market while maintaining semantic consistency from established frameworks. ISCO

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

Volume: 12 Issue: 10 | Oct 2025 www.irjet.net p-ISSN: 2395-0072

provides the occupational hierarchy, and ESCO defines skillconceptsandtheirrelationships.

[7] Thepaper“AI-Powered ResumeAnalysisUsingSpaCy for Skill Extraction and Job Matching” by Dr. A. Karunamurthy and M. Barath (ISJEM, 2025) presents an advancedartificial intelligence–drivensystemdesigned to automate resume analysis, skill extraction, and job matching using the SpaCy Natural Language Processing (NLP) library. The authors aim to overcome the inefficiencies of traditional keyword-based resume screening methods, which often fail to capture contextual meaning, transferable skills, and nuanced candidate information. By integrating NLP and AI, the proposed model enhances recruitment accuracy, reduces manual effort, and promotes fairer and data-driven hiring decisions.

[8] The project “Resume Analyzer Using NLP” is designed to simplify and enhance the recruitment process using Natural Language Processing techniques. It automatically extracts details such as skills, education, and experience from resumes and analyzes them to provide personalized recommendations.Basedontheextracteddata,thesystem suggests relevant job roles, tools, technologies, and certificationcoursestohelpusersimprovetheirprofiles.It also includes data analytics for admin insights and YouTube video recommendations for resume and interview preparation. This smart system benefits both recruiters and job seekers by saving time, improving accuracy,andincreasingthechancesofselectingthemost suitablecandidates.Overall,itactsasanintelligentbridge between applicants and employers in the digital hiring world

[9]TheResumeAnalyzerandJobRecommendationSystem is designed to make the recruitment process faster and smarter using technologies like Natural Language Processing(NLP),OpticalCharacterRecognition(OCR),and MachineLearning.Itextractskeyinformationfromresumes such as skills, education, and experience, then matches candidates to the most suitable job roles using algorithms like TF-IDF, Cosine Similarity, and K-Nearest Neighbors (KNN). The system also gives personalized job and course recommendations, helping candidates improve their profiles. It benefits both recruiters and job seekers by saving time, improving accuracy, and ensuring better job matches

[10]Theproject“ResumeAnalyser:AnalysingandFiltering System” helps make hiring easier and faster using NLP and Machine Learning. It reads resumes in PDF or text format and extracts important details like skills, education, and

experience. The system then filters and ranks the most suitable candidates for a job using smart algorithms like NER and KNN.It also suggests courses to improve the candidate’sskillsandhelpsrecruitersfindthe rightperson quickly. This system saves time, reduces manual work, and makestherecruitmentprocessmoreaccurateandfair.

[11] The "Smart Resume Analyzer" paper introduces an automated system that uses Natural Language Processing (NLP) and text mining to streamline the recruitment process. Designed to reduce recruiters' workload, the system automatically extracts key details such as name, contact information, experience, and qualifications from resumes. A crucial feature is a novel scoring mechanism that evaluates resumes based on the presence of essential elements like declarations, interests, and skill sets. This system processes resumes in various formats, including PDF and Word documents. For model training, the researchers used a Kaggle dataset of 1,924 resumes. The methodology involved pre-processing steps like Lemmatization and the elimination of stop words before applying machine learning. The study compared the performance of several classifiers, including KNN, Naive Bayes, Decision Tree, Random Forest, and SVM. Ultimately, the SVM algorithm outperformed the others, achieving the highestaccuracyinclassifyingtheresumes.Thisresponsive web application acts as an Automatic Tracking System (ATS)component, providing bothrecruitersandapplicants with valuable insights and suggestions for improvement. The technology aims to automate and expedite the employmentprocess,ensuringimpartialityandsavingtime andeffortforallparties.

[12] The AI Resume Analyzer is an automated system that leverages Natural Language Processing (NLP) and Machine Learning (ML) to streamline the recruitment process. It operates by parsing resumes to extract key skills and experience, then accurately categorizing candidates as "freshers" or "experienced". A crucial feature is a novel scoring mechanism that evaluates resume strength and providesapplicantswithsuggestionsforimprovement.The heart of the tool is a dynamic job recommendation engine that uses sophisticated filtering systems to match candidates with highly relevant opportunities. This objective approach significantly expedites screening while helping to mitigate potential human biases in hiring decisions.Inmodelevaluation,theSVMalgorithmachieved the highest accuracy in classification tasks. Furthermore, the system incorporates stringent measures for data securityandprivacytosafeguardalluserinformation. This tooloffersahighlyefficientandsecuresolutionforbothjob seekersandhiringmanagersinthemodernworkforce.

Volume: 12 Issue: 10 | Oct 2025 www.irjet.net

[13 The AI Resume Analyzer is an automated system that uses Natural Language Processing (NLP) and Machine Learning (ML) to streamline the hiring process. The primary goal is to efficiently screen a high volume of unstructuredresumesandmitigatepotentialhumanbiases. The system parses resumes to accurately extract skills and experience, classifying candidates into categories like "fresher" or "experienced". A core feature is a dynamic job recommendation engine that employs various filtering systems to match candidates with highly relevant job descriptions. This data-driven approach significantly expedites initial screening, saving valuable time and resources for HR departments. Furthermore, the system may offer applicants suggestions for improving their resume strength. In classification model testing, the Linear SVM algorithm demonstrated the highest accuracy. Finally, thesystemincorporatesstringentdatasecurityandprivacy measures to safeguard sensitive user information and complywithregulations.

[14] The "Smart Resume Scoring & Recommendations" system is an AI-powered solution that leverages Machine Learning (ML) and Natural Language Processing (NLP) to automate resume evaluation and provide actionable insights to job seekers. It addresses the limitations of manual, subjective screening by offering standardized assessment and a quantitative score (up to 100) based on weighted factors like word count, identified skills, and experience duration. The system integrates three core functionalities: an intelligent Resume Analyzer for evaluation, a Power BI Visualization module to present interactive, data-driven insights on resume strengths and weaknesses, and an Intelligent Resume Builder to help users create professional, ATS-compliant documents. Additionally, it functions as a comprehensive career development tool by providing personalized course recommendations to address skill gaps and offering ATS optimization tips and career advice, ultimately aiming to enhance employability and streamline the recruitment process.

[15]ResumeScreeningisacriticalinitialphaseinthehiring process, which evaluates a candidate's qualifications, skills, and experience against the requirements of a specific job role.Whiletraditionalmanualscreeningisstillcommon,its inefficiencies including being time-consuming, prone to human error, and susceptible to bias have prompted the need for automated solutions. This survey details various modern methodologies, including the application of Artificial Intelligence and Machine Learning, that aim to streamline, improve the accuracy, and enhance the objectivityoftheresumescreeningmechanism.

3.Proposed Work

2395-0056

-ISSN: 2395-0072

The proposed system, AI HireMap, aims to develop an intelligent and automated hiring support platform that streamlines the recruitment process using Artificial Intelligence (AI) and Natural Language Processing (NLP). Traditional recruitment systems rely heavily on manual resume screening, which is time-consuming, prone to human bias, and inefficient when handling large applicant pools.Toovercomethesechallenges,AIHireMapintroduces an automated, data-driven, and fair approach to candidate evaluation.

ThesystemleveragesArtificialIntelligence(AI)andNatural LanguageProcessing(NLP) toanalyzeresumesandextract key information such as skills, educational background, projects,certifications,and experience.Usinga pre-defined knowledge base and machine learning models, the system compares the extracted skills with industry-standard job role requirements. Based on this comparison, it highlights missingorunderdevelopedcompetenciesinthecandidate’s profile.

Oncetheweakareasareidentified,AIHireMapintelligently recommends learning resources such as online courses, tutorials, and articles to help the candidate strengthen those areas. This creates a personalized learning roadmap foreachuser,makingthesystemnotjustaresumescanner butalsoacareerguidanceassistant.

4.Result and Inference

The implementation of AI HireMap successfully demonstrated an intelligent career-readiness platform that helpsstudentsandjobseekersenhancetheiremployability. The system accurately analyzed resumes, extracted key skills,andcomparedthemwithjobrequirementstoidentify skill gaps. Based on these insights, AI HireMap provided personalized recommendations for learning resources, helpingusersbridgetheirdeficiencieseffectively.

The login and signup modules ensured secure user authentication for both students and administrators, while the database integration enabled efficient data storage and retrieval. The system’s AI-driven analysis improved the accuracy and relevance of career suggestions compared to manual evaluation methods. AIHireMapis an effective tool for automating the career assessment and guidance process. It not only reduces the time required for skill analysis but also enhances decision-making through datadriveninsights.TheintegrationofAIandreal-timeanalytics ensures that users stay updated with current industry

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

Volume: 12 Issue: 10 | Oct 2025 www.irjet.net p-ISSN: 2395-0072

trends, ultimately increasing their chances of employment success.

5.Future Scope

The proposed system, AI HireMap, has strong potential for futureenhancementandexpansioninseveraldirections.As technology and industry skill requirements evolve, the system can be continuously improved to provide more accurate,personalized,andadaptivecareerguidance.Some possibleareasoffutureworkinclude:

1. Integration with Job Portals: AI HireMap can be integrated with major job portals and professional networks such as LinkedIn, Naukri, or Indeed to automaticallyfetchuserprofilesandsuggestimprovements basedonlivejobmarkettrends.

2. AI-Based Interview Preparation: The system can be extendedtoanalyzejobdescriptionsandgenerateAI-driven interviewquestionsormockinterviewsessionsbased ona candidate’sweakskillareas.

3. Real-Time Skill Trend Analysis: Incorporating real-time labor market analytics can help the system suggest emerging skills and technologies relevant to a candidate’s careerdomain.

4. Personalized Learning Path Generation: Future versions can include AI-based adaptive learning paths, where the system tracks user progress in recommended courses and dynamicallyupdatesnewmaterialsastheuserimproves.

5. Multilingual Resume Support: Expanding the system to support multiple languages will make it accessible to a largeraudienceacrossdifferentregionsandindustries.

6.Integration of Predictive Analytics: Predictive models can be used to forecast a candidate’s job readiness or success probabilityinspecificrolesbasedonskilldevelopmentand learninghistory.

7. Cloud-Based and Mobile Application: Deploying AI HireMap onthecloudanddevelopinga mobileversion will improve accessibility, scalability, and real-time feedback deliverytousersanywhere.

6. Conclusion

The development of AI HireMap provides an innovative solutionforbridgingthegapbetweenjobseekers’skillsand industry requirements. By utilizing artificial intelligence and data-driven analysis, the system effectively identifies missing skills, recommends relevant learning paths, and

enhances users’ employability. The platform simplifies the career preparation process through automated resume analysis, personalized guidance, and secure user management. Overall, AI HireMap demonstrates how AI technologycantransformtraditionalcareercounselinginto a more efficient, accessible, and intelligent process. With further integration of predictive analytics, cloud deployment, and mobile support, AI HireMap has strong potential to evolve into a comprehensive employability enhancement tool for students and professionals worldwide.

7. References

[1] P Shanmugam, Janhavi Lenka, M Yousuf Suhail, “EnhancingStudentPlacementPreparationThroughWeb Application”, ProceedingsoftheInternationalConference onInnovativeComputing&Communication(ICICC2024), June2024.

[2] K.L .Abhishek, M.Niranjanamurthi, Shonit Aric, Syed ImmamulAnsarullah,AnuragSinha,G.Tejani,“Developing and Intelligent Resume Screening Tool With AI-Driven Analysis And Recommendations Features”, WILEY Online Library,Volume:6Issue:02|May2025

[3] Swanand Modak, Prasanna Shinde, Aniket Tiwari, Sonali Nalamwar, “A Review of Resume Analysis and Job Description Matching Using Machine Learning”, InternationalJournalonRecentandInnovationTrendsin ComputingandCommunication,Volume:12Issue:02|Jan 2024.

[4] Harshita R, Mrs. Veena B, ”A Survey on Resume Analysis Using NLP”, International Research Journal of Modernization in Engineering Technology and Science, Volume:06Issue:04|July2024.

[5] Changmao Li, Elaine Fisher, Rebecca Thomas, Steve Pittard,VickiHertzberg,JinhoD.Choi,“Competence-Level Prediction and Resume and Job Description Matching UsingContext-AwareTransformerModels”

[6]Maurits de Groot, Jelle Schutte, David Graus, ”Job Posting-Enriched Knowledge Graph for Skills-based Matching”,September2021.

[7] Dr.A. Karunamurthy, M.Barath, “AI-Powered Resume Analysis Using SpaCy for Skill Extraction and Job Matching”, International Scientific Journal of Engineering andManagement,volume:04Issue:05|May2025.

[8] Nagisetti Saideepthi, Movva Hema Latha, Kalluri Deepthi, Guttula Krupa Harik, “Resume Analyzer Using

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

Volume: 12 Issue: 10 | Oct 2025 www.irjet.net p-ISSN: 2395-0072

NLP”, International Journal of Current Science(IJCSPUB), Volume:4Issue:02|March2024.

[9] K S Varshith Reddy, Kiran N J, Likhith Gowda T R, MohammedAmanullah,HariniSS,“ResumeAnalyzerand Job Recommendation System”, Iconic Research And EngineeringJournals(IRE),Volume:8Issue:09|Mar2025.

[10] Prof. Deveshree Wankhede, Aniket Ganesh Singh, Vihaan Biju Nair, Manikandan Kanagaraj Nadar, “Journal of Emerging Technologies And Innovative Research(JETIR),Volume:11Issue:07|July2024.

[11] Kajal Rai, Pawan Kumar, “Smart Resume Analyzer: An Automated Approach For Recruitment Process”, ResearchGate,Feb2025.

[12] Ashvini Chavan, Nikita Tatewar, Pavina Naicker, Prof.SareekaDeore, “AI Resume Analyzer”, International JournalofCreativeResearchThoughts(IJCRT),Volume:11, Issue:12|Dec2023.

[13] Mr. Panil Jain, Mr. Nandkishor Kamble, Ms. Risha Nadar, Mr. ShaikhLuqman, “Resume scanning usingpython”, International Research Journal of Engineering and Technology(IRJET), Volume09, Issue:04 |Apr2022.

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[15] Tumula Mani Harsha, Gangaraju Sai Moukthika, Dudipalli Siva Sai, Mannuru Naga Rajeswari Pravallika, Satish Anamalamudi, “Survey on Resume Screening Mechanisms”, International Journal of Computer Science andEngineering,Volume:09,Issue:04|Apr2022.