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This pilot study explores pre-service teachers’ acceptance, usage patterns, and training needs regarding ChatGPT in teaching. Integrating constructs from TAM and UTAUT, a survey with 47 university students identified four user typologies to inform differentiated training strategies. Results show that 97.87% had used generative AI tools, with 57.45% applying ChatGPT to teaching tasks. K-means cluster analysis identified Innovators (20%), Pragmatists (42%), Cautious Evaluators (28%), and Resistors (10%), each differing in perceived usefulness, ease of use, and adoption intention. STEM-background participants were more likely to be Innovators or Pragmatists, while non-STEM participants tended to be Cautious Evaluators. The study proposes training strategies tailored to each user type, laying a foundation for future AI-integrated teacher education programs.

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From Technology Acceptance to Teaching Practice: Exploring Pre-service Teachers’ User Typologies and Training Needs in ChatGPT Adoption

  • 会议论文

  • 发表日期：

    2026年

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  • Pei-Hua Chen,
  • Lisbet Rønningsbakk,
  • Yueh-Min Huang

摘要

This pilot study explores pre-service teachers’ acceptance, usage patterns, and training needs regarding ChatGPT in teaching. Integrating constructs from TAM and UTAUT, a survey with 47 university students identified four user typologies to inform differentiated training strategies. Results show that 97.87% had used generative AI tools, with 57.45% applying ChatGPT to teaching tasks. K-means cluster analysis identified Innovators (20%), Pragmatists (42%), Cautious Evaluators (28%), and Resistors (10%), each differing in perceived usefulness, ease of use, and adoption intention. STEM-background participants were more likely to be Innovators or Pragmatists, while non-STEM participants tended to be Cautious Evaluators. The study proposes training strategies tailored to each user type, laying a foundation for future AI-integrated teacher education programs.