Using AI in Career Guidance for Students

Authors

  • Kaldybaeva Uldana Saparovna Teacher of Kazakh Language and Literature, Master of Pedagogical Sciences, Pedagogue-Master, M. Abenova General Secondary School, Turkestan City, Turkestan Region. https://orcid.org/0000-0001-5942-2230

DOI:

https://doi.org/10.63034/esr-310

Keywords:

Artificial Intelligence (AI), career guidance, personalized recommendations, career counseling, machine learning

Abstract

The integration of artificial intelligence (AI) in career guidance has transformed traditional approaches to helping students choose their future professions. AI-powered tools enable personalized recommendations based on students' interests, skills, and preferences, providing a more effective and data-driven method for career planning. This study explores the potential of AI in career counseling, focusing on its ability to analyze individual profiles and offer tailored career pathways. By leveraging machine learning algorithms and predictive analytics, AI systems can match students with professions that align with labor market trends and their personal aspirations. The research also addresses challenges, such as ethical considerations, data privacy, and the potential risks of over-reliance on AI in decision-making. The findings suggest that incorporating AI in educational institutions enhances the career guidance process, making it more accessible and relevant for students. This paper emphasizes the importance of combining AI with traditional counseling methods to ensure a holistic approach. Practical applications include developing AI-based platforms for schools to guide students in making informed career choices. The study concludes that AI has the potential to revolutionize career guidance by offering innovative solutions that align with the evolving demands of the modern workforce.

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Published

2025-01-05

How to Cite

Kaldybaeva, U. (2025). Using AI in Career Guidance for Students. Eurasian Science Review An International Peer-Reviewed Multidisciplinary Journal, 2(Special Issue), 2211–2222 . https://doi.org/10.63034/esr-310