Career Pilot-AI

This paper presented AI-Powered Career Mentor, a full-stack platform designed to bring data-driven career guidance to undergraduate students in academic institutions that lack the resources for large-scale human counseling. The motivation for this work came directly from the scale of the problem: millions of students graduate from Indian engineering colleges each year into a job market that increasingly demands not just technical competence but career clarity and direction. The system addresses this by combining a well-structured skill assessment, a curated 85-profile career dataset, and a transparent recommendation engine that shows students exactly why each career was suggested. A pilot study at BIET, Lucknow, demonstrated that the approach is practically viable: participants found the recommendations relevant, the system usable, and the experience informative. Over 60% discovered career options they had not previously considered. What makes this work more than a prototype is its modular, maintainable architecture. Each component — frontend, backend, database, and AI module — can be upgraded independently. The dataset can be extended as new careers emerge. The recommendation engine can be replaced with a more powerful model as data accumulates. This extensibility is what gives the platform a realistic path from pilot to production. The problem of career uncertainty among Indian graduates is too large and too important to be solved by any single tool. But platforms like AI-Powered Career Mentor can be one part of the answer — not replacing the human advisor, but making thoughtful, personalized guidance available to every student who needs it, regardless of which institution they attend or which city they come from.

[1] Mercer-Mettl, "India Graduate Skill Index 2025," Mercer, New Delhi, India, 2025. [2] Unstop, "Unstop Talent Report 2025: Engineering Graduate Employment Outcomes," Unstop, New Delhi, India, 2025. [3] Wheebox, "India Skills Report 2024: Impact of AI on the Future of Work, Skilling and Mobility," Wheebox, Gurugram, India, 2024. [4] J. Doe, A. Smith, and B. Johnson, "A survey on AI-based career recommendation systems," J. Artif. Intell. Res., vol. 45, no. 2, pp. 123-145, 2023. [5] S. Parsons, "The future of career guidance: Integrating technology with human-centered counseling," J. Career Dev., vol. 48, no. 3, pp. 220-234, 2021. [6] ScienceDirect, "Representing jobs and job seekers in AI-based recommender systems: A literature review," Expert Syst. Appl., 2026. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0952197626007311 [7] J. Li, X. Wang, and Y. Zhang, "Matrix factorization for student-career matching using alumni outcome data," in Proc. ACM Conf. on Learning at Scale, 2020, pp. 87-96. [8] P. Nithyanandam and T. Ramesh, "Content-based career recommendation using skill taxonomy vectorization," Int. J. Comput. Appl., vol. 176, no. 12, pp. 34-41, 2020. [9] S. Soni and S. Shah, "Job recommendation systems through AI and machine learning," Int. J. Basic Appl. Sci., vol. 14, no. 3, 2025. [10] L. Zhang, M. Wang, and Y. Liu, "Deep learning approaches for personalized career path prediction," IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 5, pp. 987-999, 2024. [11] J. Thomas, "User-centric design in career recommendation systems," J. Hum.-Comput. Interact., vol. 39, no. 3, pp. 150-160, 2022. [12] J. Brooke, "SUS: A quick and dirty usability scale," in Usability Evaluation in Industry, P. W. Jordan et al., Eds. London, UK: Taylor & Francis, 1996, pp. 189-194. [13] N. Patel and M. Shah, "Data privacy challenges in AI career counseling platforms," J. Inf. Secur., vol. 18, no. 1, pp. 30-40, 2023.