Entrepreneurial Skill Augmented Neural Network (ESANN): A Deep Learning Approach for Enhancing Entrepreneurial Competencies in Teachers

Modern education requires teachers to develop entrepreneurial competencies because traditional training practices fail to provide individualized training methods. The developments in deep learning provide educational institutions with new data-based methods to develop professional development training programs. Entrepreneurial Skill Augmented Neural Network (ESANN) presents itself as a deep learning framework that serves to individualize educator training while upgrading their acquisition of entrepreneurial skills. The model leverages Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and a Transformer-based recommendation engine to deliver adaptive, real-time feedback and optimize training pathways based on individual learning patterns. A model training process used data from 300 teachers, including their activity records, questionnaires, and evaluation feedback, to achieve valid model outcomes. Educators received assessment through an established entrepreneurial competency framework at the commencement and completion of ESANN-based instruction. The quantitative evaluation included measuring skill score improvement between participants in the ESANN group and those in traditional training through accuracy metrics evaluated within ten-fold cross-validation methods. Members of the training group that received ESANN training outperformed traditional training participants by 24% in their entrepreneurial competency scores. The developed model delivered 92.5% predictive accuracy, surpassing other baseline approaches regarding performance efficiency and accuracy. ESANN serves as a deep learning framework for educational development, which provides individualized and flexible learning solutions for teacher training on a large scale. These findings highlight artificial intelligence's (AI) transformative potential in fostering entrepreneurial education through real-time, data-informed feedback.