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The Artificial Intelligence and Neural Network in Teaching.

Qun Luo1, Jiliang Yang2

  • 1Information Engineering Department, ChongQing City Vocational College, Yongchuan, ChongQing 402160, China.

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Artificial intelligence (AI) and network technology enhance smart classrooms. Convolutional Neural Network (CNN) models outperform Long Short-Term Memory (LSTM) in classifying teacher questions, improving accuracy for knowledge points and question types.

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Area of Science:

  • Educational Technology
  • Artificial Intelligence
  • Computer Science

Background:

  • The integration of artificial intelligence (AI) and network technology is transforming educational paradigms.
  • Smart classroom environments offer new possibilities for interactive and personalized learning experiences.
  • Analyzing teacher questioning is crucial for understanding and improving pedagogical strategies.

Purpose of the Study:

  • To explore the application of AI and network technology in teaching, specifically within a mathematics smart classroom context.
  • To intelligently analyze the questioning link in classroom teaching by classifying teacher-generated questions.
  • To compare the effectiveness of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models for question classification.

Main Methods:

  • Utilized an AI-based smart classroom teaching mode incorporating network technology.
  • Employed network classification models, specifically Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), to categorize teacher questions.
  • Conducted experimental verification to assess the performance of CNN and LSTM models in classifying question content and types.

Main Results:

  • The Convolutional Neural Network (CNN) model demonstrated superior performance over the Long Short-Term Memory (LSTM) model in classifying teacher question content.
  • CNN achieved higher accuracy in classifying essential knowledge points (86.3%) compared to LSTM (79.2%), an improvement of 8.96%.
  • CNN also showed higher accuracy in classifying teacher question types (87.82% for prompt questions) compared to LSTM (83.2%), an improvement of 4.95%.

Conclusions:

  • Convolutional Neural Network (CNN) models are more effective than Long Short-Term Memory (LSTM) models for classifying teacher questions in an AI-enhanced smart classroom setting.
  • AI-driven question classification can significantly improve the analysis of pedagogical interactions and identify key knowledge areas.
  • The findings support the use of advanced AI techniques like CNN for optimizing teaching strategies and enhancing educational outcomes in digital learning environments.