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This study investigated how video content types regulate the technical acceptance mechanism of primary school students towards science education videos generated by artificial intelligence. The extended Technical Acceptance Model (TAM) was validated on data from 105 students using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multiple Group Analysis (MGA). The research results revealed a significant regulatory effect, with demonstration videos being “experience driven”, where technical fluency (FC) strongly enhances perceived ease of use (PEU), thereby improving perceived usefulness (PU) and adoption intention. Information videos are content driven, and perceived usefulness (PU) directly and strongly shapes people's attitudes. Content types systematically change key TAM pathways, providing a differentiated theoretical framework for understanding the acceptance of educational videos. I hope to provide practical insights for designing AI generated video resources in educational practice.

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How Does Content Type Influence Acceptance? A Study on Student Acceptance of AI-Generated Science Popularization Short Videos Based on the TAM Model

  • 会议论文

  • 发表日期：

    2026年

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  • Mengxia He,
  • Zi Huang,
  • Xiuxiu Sima,
  • Ke Zhu

摘要

This study investigated how video content types regulate the technical acceptance mechanism of primary school students towards science education videos generated by artificial intelligence. The extended Technical Acceptance Model (TAM) was validated on data from 105 students using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multiple Group Analysis (MGA). The research results revealed a significant regulatory effect, with demonstration videos being “experience driven”, where technical fluency (FC) strongly enhances perceived ease of use (PEU), thereby improving perceived usefulness (PU) and adoption intention. Information videos are content driven, and perceived usefulness (PU) directly and strongly shapes people's attitudes. Content types systematically change key TAM pathways, providing a differentiated theoretical framework for understanding the acceptance of educational videos. I hope to provide practical insights for designing AI generated video resources in educational practice.