Research on the Application of Generative AI in Media Vocational Education
Abstract
Generative AI is profoundly reshaping the ecosystem of media vocational education. Its multi-modal generation capabilities are
driving the transformation of teaching models towards human-machine collaboration, facilitated by tools such as intelligent lesson preparation assistants and virtual laboratories, thereby enabling personalized learning path design. Research focuses on the systematic impact
of generative AI on the teaching model-skill structure-ethical governance of media education, revealing a coexistence of technological
empowerment and ethical risks. On one hand, media talents need to transition from a professional skills-based approach to a smart
&skills composite training model, with an emphasis on enhancing data thinking and cross-modal creative abilities, yet a skills gap persists. On the other hand, technology application faces challenges such as data bias, copyright disputes, and academic integrity crises. The
study proposes achieving value alignment under controllable risks through policy regulation, technological empowerment, and educational
reform. Future efforts should explore the construction of dynamic evaluation systems and global regulatory collaboration to balance innovation with fairness.
driving the transformation of teaching models towards human-machine collaboration, facilitated by tools such as intelligent lesson preparation assistants and virtual laboratories, thereby enabling personalized learning path design. Research focuses on the systematic impact
of generative AI on the teaching model-skill structure-ethical governance of media education, revealing a coexistence of technological
empowerment and ethical risks. On one hand, media talents need to transition from a professional skills-based approach to a smart
&skills composite training model, with an emphasis on enhancing data thinking and cross-modal creative abilities, yet a skills gap persists. On the other hand, technology application faces challenges such as data bias, copyright disputes, and academic integrity crises. The
study proposes achieving value alignment under controllable risks through policy regulation, technological empowerment, and educational
reform. Future efforts should explore the construction of dynamic evaluation systems and global regulatory collaboration to balance innovation with fairness.
Keywords
Generative AI; Media; Vocational Education
Full Text:
PDFReferences
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DOI: http://dx.doi.org/10.70711/neet.v3i1.6373
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