Quality Evaluation of Talent Cultivation in Higher Vocational Education based on Artificial Intelligence Algorithms

Vocational education is facing unparalleled challenges and opportunities within the backdrop of technological innovation, industrial change, and the diversification of labour markets. While historically, vocational education systems have tended to concentrate on industryspecific technical tasks, the fast pace of emerging technologies calls for a shift towards developing cross-disciplinary capabilities. The diffusion of digitalization and artificial intelligence requires vocational education to cultivate "tool users" and innovative skills that equip their owners with creativity, critical thinking abilities, and a high level of adaptability. In this regard, artificial intelligence algorithms provide good answers for the assessment of quality and efficiency of talent cultivation mechanisms. This paper suggests an Attention Evoked Bi-LSTM (AE-BILSTM) model to measure the quality of talent development in higher vocational education through the capture of temporal patterns and the highlight of key features in learning data. The model with proposed suggestion resulted in a prediction accuracy of 98.4% and F1-score of 97.5, thus proving its efficiency in measuring talent development outcomes. Further, vocational education still operates under deeply embedded social biases. In certain nations, it is still considered a "second-class" type of education, being less socially esteemed compared to academic education. Such cognitive bias, being especially common in certain cultural environments, restricts the general acceptance of vocational education. Consequently, numerous families consider it supplementary education, which affects students' decisions and discourages parental support. The vocational education's social value and potential are not realized in full, resulting in some youngsters who seek to pursue technical professions lacking the acknowledgment and support they should receive from their families and society. This, in turn, worsens the plight of vocational education and discourages it from becoming a respected and credible route for future career.

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Published
2025-06-06
DOI
https://doi.org/10.1109/icicke65317.2025.11136434
Primary Topic
Innovative Educational Techniques
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article
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article

Quality Evaluation of Talent Cultivation in Higher Vocational Education based on Artificial Intelligence Algorithms

Haiyan Lan
Innovative Educational Techniques
article

Quality Evaluation of Talent Cultivation in Higher Vocational Education based on Artificial Intelligence Algorithms

Haiyan Lan
article en

Abstract

Vocational education is facing unparalleled challenges and opportunities within the backdrop of technological innovation, industrial change, and the diversification of labour markets. While historically, vocational education systems have tended to concentrate on industryspecific technical tasks, the fast pace of emerging technologies calls for a shift towards developing cross-disciplinary capabilities. The diffusion of digitalization and artificial intelligence requires vocational education to cultivate "tool users" and innovative skills that equip their owners with creativity, critical thinking abilities, and a high level of adaptability. In this regard, artificial intelligence algorithms provide good answers for the assessment of quality and efficiency of talent cultivation mechanisms. This paper suggests an Attention Evoked Bi-LSTM (AE-BILSTM) model to measure the quality of talent development in higher vocational education through the capture of temporal patterns and the highlight of key features in learning data. The model with proposed suggestion resulted in a prediction accuracy of 98.4% and F1-score of 97.5, thus proving its efficiency in measuring talent development outcomes. Further, vocational education still operates under deeply embedded social biases. In certain nations, it is still considered a "second-class" type of education, being less socially esteemed compared to academic education. Such cognitive bias, being especially common in certain cultural environments, restricts the general acceptance of vocational education. Consequently, numerous families consider it supplementary education, which affects students' decisions and discourages parental support. The vocational education's social value and potential are not realized in full, resulting in some youngsters who seek to pursue technical professions lacking the acknowledgment and support they should receive from their families and society. This, in turn, worsens the plight of vocational education and discourages it from becoming a respected and credible route for future career.

Beijing Polytechnic (CN)
Openalex Percentile: Top 72%
Innovative Educational Techniques
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Quality Evaluation of Talent Cultivation in Higher Vocational Education based on Artificial Intelligence Algorithms — Haiyan Lan · (2025) | TGRS Research Map | TGRS