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This chapter presents the Constructivist Inquiry-Based Learning Prompting (CILP) Framework, a pedagogical model that guides the ethical and effective integration of generative artificial intelligence (AI) into STEM education. Grounded in constructivist learning theory and inquiry-based pedagogy, the framework positions large language models (e.g., ChatGPT) as cognitive partners that promote conceptual reasoning, reflection, and metacognitive growth. A quasi-experimental study found that AI-mediated inquiry using the CILP Framework enhanced students’ conceptual understanding and reduced misconceptions in thermodynamics compared with traditional instruction. Through iterative prompting, reflection, and evaluation, learners achieved deeper conceptual integration and stronger epistemic agency. Building on these results, the chapter translates findings into strategies for K–12 pre-service teacher education, emphasizing AI literacy, prompt engineering, hybrid (human–AI) instructional design, and ethical awareness. Ultimately, the CILP Framework offers a research-informed approach for preparing AI-ready educators who integrate generative AI responsibly and innovatively to advance inquiry and scientific literacy in K–12 STEM classrooms.

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Integrating Generative AI into Science Teaching: Preparing Pre-Service Science Teachers for the AI-Driven STEM Classroom

  • 章节

  • 发表日期：

    2026年

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  • Tarik El Fathi,
  • Driss Lamri,
  • El Mehdi Al Ibrahmi

摘要

This chapter presents the Constructivist Inquiry-Based Learning Prompting (CILP) Framework, a pedagogical model that guides the ethical and effective integration of generative artificial intelligence (AI) into STEM education. Grounded in constructivist learning theory and inquiry-based pedagogy, the framework positions large language models (e.g., ChatGPT) as cognitive partners that promote conceptual reasoning, reflection, and metacognitive growth. A quasi-experimental study found that AI-mediated inquiry using the CILP Framework enhanced students’ conceptual understanding and reduced misconceptions in thermodynamics compared with traditional instruction. Through iterative prompting, reflection, and evaluation, learners achieved deeper conceptual integration and stronger epistemic agency. Building on these results, the chapter translates findings into strategies for K–12 pre-service teacher education, emphasizing AI literacy, prompt engineering, hybrid (human–AI) instructional design, and ethical awareness. Ultimately, the CILP Framework offers a research-informed approach for preparing AI-ready educators who integrate generative AI responsibly and innovatively to advance inquiry and scientific literacy in K–12 STEM classrooms.