This paper investigates the transformative impact of generative artificial intelligence (AI) on vocational education career planning, transitioning from traditional methodologies to personalized, dynamic strategies. By leveraging Natural Language Processing (NLP) and Machine Learning (ML), it delves into how generative AI can provide tailored career guidance, adaptive learning pathways, and labor market insights, underpinned by constructivist learning theory and career development models. The study's methodology blends theoretical analysis with practical implementation, focusing on strategic planning, stakeholder engagement, technology customization, and ethical considerations. It discusses the implications for educators, students, and institutions, emphasizing the necessity for continuous adaptation and innovation in the face of technological advancements. Additionally, the paper identifies future research avenues, including the long-term impact of AI on employment outcomes, its scalability across vocational disciplines, and ethical challenges, advocating for the strategic employment of generative AI to align vocational education more closely with the evolving job market and enhance students' readiness for future careers.

Jingyi Duan, Suhan Wu. Beyond Traditional Pathways: Leveraging Generative AI for Dynamic Career Planning in Vocational Education. International Journal of New Developments in Education (2024), Vol. 6, Issue 2: 24-31. https://doi.org/10.25236/IJNDE.2024.060205.

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