Choosing an appropriate career path has become increasingly difficult for students due to expanding academic options, rapidly evolving job markets, and limited access to personalized guidance. Traditional counseling methods are often generic, subjective, and unable to address the diverse needs of learners. This paper presents a comprehensive survey of AI-powered career path recommendation systems that leverage machine learning, natural language processing, and intelligent data analytics to provide accurate and personalized career suggestions. By analyzing key student-specific factors—such as academic performance, interests, skills, aptitude, and personality traits—AI-based models identify suitable career opportunities aligned with individual potential and current industry trends. The study reviews existing career guidance platforms, highlights their limitations, and identifies research gaps in personalization, cultural adaptation, scalability, and real-time labor market relevance. Additionally, a conceptual framework is proposed to enhance recommendation accuracy through adaptive learning models, multilingual support, and dynamic career data integration. This survey emphasizes the transformative potential of AI-driven guidance systems in empowering students, reducing career mismatches, improving decision-making, and offering equitable, accessible, and future-ready career support across diverse educational environments.

Artificial Intelligence has transformed various domains, and career guidance is one of the most promising areas where AI can create meaningful impact. The survey highlights that traditional counselling methods are insufficient to meet modern student needs. Existing systems lack personalization, Indian context adaptation, and multi-factor analysis. The proposed AI-powered system integrates psychometrics, academic data, skill profiling, and ML algorithms to deliver customized, unbiased, and reliable career recommendations. It not only supports students in choosing suitable career pathways but also increases their awareness about emerging opportunities, required industry skills, and future trends. Developing such a system can significantly improve student decision-making, reduce career mismatch, and help institutions adopt technology-backed counseling solutions.

Sharma, R. (2023). AI-Driven Career Guidance Systems: A Review. International Journal of Educational Technology.  Gupta, S., & Mehta, P. (2022). Machine Learning Approaches for Student Career Prediction. IJERT.  Brown, J. (2021). Psychometric Evaluation in Academic Counselling. Educational Research Review.  CareerGuide.com – Online Career Counselling Platform.  Univariety.com – Counselling and Mentorship Services.  various IEEE and Springer publications on AI-based recommendation systems.