International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
KaamKhojAI: AI-Driven Employment Accessibility for Low-Literate Job Seekers Manthan Tighare 1, Yachin Verma2, Pratham Potdar3, Rishabh Prajapati4, Aashish Choudhari5, Prof. Swati Dronkar 6 6Professor, CSE, Priyadarshini College of Engineering Nagpur, Maharashtra, India 12345UG Student, CSE, Priyadarshini College of Engineering Nagpur, Maharashtra, India
---------------------------------------------------------------------***--------------------------------------------------------------------2. Methodology of Review
Abstract - India’s working population comprises individuals from a wide range of professional and skill-based categories, including unskilled laborers, service workers, technicians, and educated professionals. Despite technological advancements, many job platforms still lack accessibility features to support diverse user groups who struggle with language barriers, digital literacy, and complex registration processes. Artificial Intelligence (AI), Natural Language Processing (NLP), and conversational technologies are transforming the recruitment sector by enabling AI-driven assistance, automated interactions, and personalized job recommendations. This review paper analyzes the evolution of AI-assisted job portals designed to support all categories of workers in India, examining their architectures, features, and usability components that simplify employment access. The study identifies key limitations such as insufficient multilingual support, weak contextual job matching, and limited regional employment integration, while emphasizing the need for inclusive and scalable AI solutions for employment accessibility.
This review is based on academic research published between 2014 and 2025, sourced from Google Scholar, IEEE Xplore, ResearchGate and Many more. Studies were selected according to three major criteria: they must address technologies that improve job searching, include AI-based user interaction features, and discuss accessibility for workers with limited digital proficiency. A broad range of research papers were reviewed to understand the development of AI-assisted job platforms. Among them, several studies were found highly relevant due to their emphasis on conversational AI support, intelligent recommendation systems, and solutions tailored for users with limited technical skills. These selected works were examined to compare emerging technological trends, architectural approaches, user experience improvements, and the existing limitations affecting the adoption of AIdriven employment portals.
3. Literature Review Key Words: AI Assistant, Job Recommendation, Speech Recognition, MERN Stack, Workforce Inclusion, Employment Accessibility
3.1 Job Portals for Blue-Collar Workers Sudam Fegade, Priya Biradar, Vivekanand Dukare, and Nivedita Rawate (2022) presented a job portal specifically designed for blue-collar professionals to address employment challenges faced by semi-skilled and uneducated workers. The platform enables job posting, job search, application tracking, and recruiter–job seeker interaction through a simplified mobile application. The study emphasizes reducing time, cost, and accessibility barriers for workers from small towns seeking employment in urban areas. While the system effectively improves outreach and usability, it relies on conventional matching techniques and lacks intelligent automation, personalized job recommendations, and AI-driven decision-making features.
1. INTRODUCTION Nagpur, being a rapidly developing metro city and a major logistics hub of Maharashtra, hosts a diverse workforce ranging from daily wage laborers and manufacturing workers to retail staff, technicians, and service professionals. However, most popular job portals such as LinkedIn, Indeed, and Naukri remain oriented toward urban white-collar job seekers, limiting their usage among Nagpur’s semi-skilled and informal workers who may not be comfortable with digital interfaces. An AI-based job portal specifically designed for the Nagpur region can bridge this gap by offering regional language guidance, simplified registration, and AI-assisted job searching, making employment discovery effortless for all categories of job seekers. This review consolidates previous research on AIenabled employment systems and analyzes how these technologies can be successfully adapted for Nagpur's local employment ecosystem.
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3.2 Intelligent Job Recommendation Systems Priyanka Singla and Vishal Verma (2025) introduced an intelligent job recommendation system based on semantic embeddings and machine learning techniques. Their proposed hybrid approach performs bi-directional matching between job seekers’ CVs and job descriptions, overcoming
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