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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62136
Volume:
10 |
4 |
April 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of computer science & engineering
Dr. J.J. Magdum College Of Engineering
Department of computer science & engineering
Dr. J.J. Magdum College Of Engineering
Department of computer science & engineering
Dr. J.J. Magdum College Of Engineering
Department of computer science & engineering
Dr. J.J. Magdum College Of Engineering
Department of computer science & engineering
Dr. J.J. Magdum College Of Engineering
Career guidance systems often provide static recommendations that fail to adapt to changing job markets and individual learner profiles. This work presents an AI-assisted Career Mentor platform that combines profile-based analytics with large language model (LLM) augmentation to generate personalized career pathways. The system uses structured user attributes—interests, skills, education level, preferred domains, location, and assessment outcomes—to produce ranked career suggestions, learning resources, and gap-based guidance. A hybrid recommendation pipeline is employed: deterministic local scoring ensures reliability, while optional LLM-based enrichment expands career options and improves contextual relevance. The platform also supports dynamic skill assessments, profile-driven category generation, and mentor-style conversational guidance. By integrating explainable matching logic with AI-generated alternatives and live-trend-aware ranking (when available), the system balances transparency, robustness, and adaptability. The proposed architecture is suitable for students and early professionals seeking data-informed career exploration, and can be extended toward multilingual, region-aware, and labor-market-grounded decision support. .
KEYWORDS
· Labor Market Grounding at Scale Integrate verified job-market datasets (government labor stats, job-board trends, salary APIs) for evidence-backed recommendations and demand forecasting. · Fine-Grained Regional Intelligence Add state/district-specific opportunities, language requirements, local institute mapping, and region-wise career viability scores. · Personalized Learning Path Optimization Use reinforcement or adaptive planning to generate month-wise, milestone-driven roadmaps based on user progress and assessment deltas. · Explainable AI Layer Provide confidence intervals, contribution scores per feature (skills, interest, education, etc.), and “why not this career” contrastive explanations. · Longitudinal Outcome Tracking Measure recommendation effectiveness via user outcomes (course completion, internships, interviews, placements) and continuously refine ranking logic. · Multilingual and Accessibility Expansion Support Indian regional languages, voice interaction, low-bandwidth modes, and inclusive UX for wider adoption. · Institutional Integration Build counselor dashboards, cohort-level analytics, and curriculum alignment tools for schools, colleges, and skilling centers.
1. K. Jaspin; G. Mercy Kiruba; V. Nivedha, “AI Assisted Career Guidance and Resume Screening System” 2024 5th IEEE Global Conference for Advancement in Technology (GCAT) 2. Arshad T K; Danish Mohammed; Arjun A; Anshad M, “VocaVisionary: A Career Guidance Chatbot” 2024 Second International Conference on Advances in Information Technology (ICAIT) 3. Madhuri Ghuge; Torana Kamble, “Envisioning Tomorrow: AI Powered Career Counseling” 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) 4. Xing Pan; Xia Li, “A MultiView Courses Recommendation System Based on Deep Learning” 2020 International Conference on Big Data and Informatization Education (ICBDIE) 5. Wei Xiong; Jiayi Wu, “Personalized Course Recommendation Model Based on User’s Interests and Feature Information” 2022 3rd International Conference on Computer Science and Management Technology (ICCSMT) 6. Umesh Guru Sai Guntupalli; Madhavi Latha Pandala, “Carrer Compass: A Career Path Recommender using Machine Learning” 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC) 7. Andrew Freed, “Conversational AI: Chatbots that work” 2021 8. Jay Ashok Shah; Nisha Ramesh Iyer, “Building Generative AI Chatbot Using Oracle Cloud Infrastructure” 2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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