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This study explores the integration of Artificial Intelligence (AI) and remote experimentation in STEM education. AI-assisted instruction offers personalized feedback to foster critical thinking, while remote experimentation provides hands-on learning experience remotely. The research focuses on the combined use of AI-assisted instruction and remote experimentation in remote laboratories utilizing real hardware. A mixed-methods approach will be used to assess the system’s impact on student engagement, learning outcomes, and problem-solving abilities. Quantitative data, including task completion times, error rates, and AI intervention frequency, will be paired with qualitative insights from student surveys and interviews. The anticipated outcomes include improved student performance, reduced errors, and faster task completion, alongside enhanced confidence and critical thinking. The findings will highlight the potential of integrating AI and remote experimentation to enhance STEM education and guide future system development.

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Integrating AI-Assisted Instruction with Remote Experimentation: Promoting Personalized Learning in STEM Education

  • 会议论文

  • 发表日期：

    2026年

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  • Rania Hussein,
  • Zhiyun Zhang,
  • Fiorella Lizano-Sanchez,
  • Carlos Arguedas-Matarrita,
  • Miguel-Angel Cabeza-Rodriguez,
  • Ignacio Idoyaga,
  • Luis Rodriguez-Gil,
  • Pablo Orduña

摘要

This study explores the integration of Artificial Intelligence (AI) and remote experimentation in STEM education. AI-assisted instruction offers personalized feedback to foster critical thinking, while remote experimentation provides hands-on learning experience remotely. The research focuses on the combined use of AI-assisted instruction and remote experimentation in remote laboratories utilizing real hardware. A mixed-methods approach will be used to assess the system’s impact on student engagement, learning outcomes, and problem-solving abilities. Quantitative data, including task completion times, error rates, and AI intervention frequency, will be paired with qualitative insights from student surveys and interviews. The anticipated outcomes include improved student performance, reduced errors, and faster task completion, alongside enhanced confidence and critical thinking. The findings will highlight the potential of integrating AI and remote experimentation to enhance STEM education and guide future system development.