Abstract

Conclusions. The study revealed that automation in microteaching evaluation operates through an eight-stage workflow integrating CNN and RNN systems. This model enables consistent and objective assessment of multimodal cues and offers a replicable foundation for AI-driven teacher evaluation. Further validation through quasi-experimental research is recommended.

Keywords: Artificial intelligence, automated microteaching evaluation, convolutional neural network, recurrent neural network, video analysis

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