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<p>There are fatal defects in both the simulation practical training model and the actual combat practical training model. This paper firstly takes artificial intelligence technology as the basis for the construction of digital wisdom training classrooms and constructs the artificial intelligence digital wisdom training model. Secondly, it combines multi-factor reasoning and integrated clustering ideas to portray the vocational ability portrait of the learner group in the model and realizes the recommendation of students’ personalized training teaching resources with the learner’s portrait. Finally, the effectiveness is verified through the application practice of the digital training model for AI in higher vocational colleges. The results show that the use of multi-factor reasoning and integrated clustering can realize the accurate portrayal of the learner portrait of the digital intelligent training students and the difference between the loss value of the training and validation of the personalized recommendation model based on the learner portrait is about 0.01, and the average time consumed is about 1.05s. The digital training model supported by artificial intelligence technology can help students establish higher-order scientific thinking, promote students’ mastery of skills, and fully compensate for the shortcomings of the existing practical training model.</p>
</abstract>

There are fatal defects in both the simulation practical training model and the actual combat practical training model. This paper firstly takes artificial intelligence technology as the basis for the construction of digital wisdom training classrooms and constructs the artificial intelligence digital wisdom training model. Secondly, it combines multi-factor reasoning and integrated clustering ideas to portray the vocational ability portrait of the learner group in the model and realizes the recommendation of students’ personalized training teaching resources with the learner’s portrait. Finally, the effectiveness is verified through the application practice of the digital training model for AI in higher vocational colleges. The results show that the use of multi-factor reasoning and integrated clustering can realize the accurate portrayal of the learner portrait of the digital intelligent training students and the difference between the loss value of the training and validation of the personalized recommendation model based on the learner portrait is about 0.01, and the average time consumed is about 1.05s. The digital training model supported by artificial intelligence technology can help students establish higher-order scientific thinking, promote students’ mastery of skills, and fully compensate for the shortcomings of the existing practical training model.

Research and Practice of Digital Training Model Based on Artificial Intelligence Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

There are fatal defects in both the simulation practical training model and the actual combat practical training model. This paper firstly takes artificial...

Research and Practice of Digital Training Model Based on Artificial Intelligence Technology

Pubblicato online: 16 dic 2023

Pagine: -

Ricevuto: 06 mar 2023

Accettato: 26 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01507

Parole chiave
Digitization, Integrated clustering, Personalized recommendations, Learner profiling, Modes of operation and training

© 2023 Haipeng Duan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research and Practice of Digital Training Model Based on Artificial Intelligence Technology

Pubblicato online: 16 dic 2023

Pagine: -

Ricevuto: 06 mar 2023

Accettato: 26 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01507

Parole chiaveDigitization, Integrated clustering, Personalized recommendations, Learner profiling, Modes of operation and training

© 2023 Haipeng Duan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Digitization, Integrated clustering, Personalized recommendations, Learner profiling, Modes of operation and training