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<p>The article presents an investigation into the perceptions of preservice science teachers about AI adoption to support multilingual learners (MLLs) in science classrooms. AI has tremendous potential for tailoring learning, real-time translation, and support with engaging educational resources, uses that the pre-service teachers consider positive (Gladwin, Redefining Education for the AI Era: Strategies to Foster Critical Thinking with Advanced Technologies, 2024). On the other hand, effective integration is fraught with technical complexity and issues relating to data privacy and is limited in terms of formal AI training within teacher education programs (Kilinç, Asian J Distance Educ 18(1):205–237, 2023). This is a mixed-methods study that evaluates these teachers’ attitudes, training needs, and barriers. The quantitative data collected from Likert-scale surveys and qualitative insights obtained from open-ended responses indicate that most preservice teachers favor comprehensive hands-on AI training, from practical classroom applications to ethical data management, to make teachers confident and competent while using AI. Findings indicate that while preservice teachers recognize AIs potential for inequitable science education, they require comprehensive training in both practical AI applications and ethical considerations. This study provides a series of recommendations about how teacher education can incorporate AI training to ensure that the preservice teachers are well-positioned to improve the full potential of AI in equitable and effective science teaching.</p>

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AI integration in science education classrooms: insights from preservice teachers on application, support, and training for multilingual learners

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月14日

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  • Dini Noor Arini,
  • Almubarak,
  • Jihee Im,
  • Hadir Alderaan,
  • Ahsani Taqwiem,
  • Achmad Rante Suparman

摘要

The article presents an investigation into the perceptions of preservice science teachers about AI adoption to support multilingual learners (MLLs) in science classrooms. AI has tremendous potential for tailoring learning, real-time translation, and support with engaging educational resources, uses that the pre-service teachers consider positive (Gladwin, Redefining Education for the AI Era: Strategies to Foster Critical Thinking with Advanced Technologies, 2024). On the other hand, effective integration is fraught with technical complexity and issues relating to data privacy and is limited in terms of formal AI training within teacher education programs (Kilinç, Asian J Distance Educ 18(1):205–237, 2023). This is a mixed-methods study that evaluates these teachers’ attitudes, training needs, and barriers. The quantitative data collected from Likert-scale surveys and qualitative insights obtained from open-ended responses indicate that most preservice teachers favor comprehensive hands-on AI training, from practical classroom applications to ethical data management, to make teachers confident and competent while using AI. Findings indicate that while preservice teachers recognize AIs potential for inequitable science education, they require comprehensive training in both practical AI applications and ethical considerations. This study provides a series of recommendations about how teacher education can incorporate AI training to ensure that the preservice teachers are well-positioned to improve the full potential of AI in equitable and effective science teaching.