Embed AI training for pre-service science teachers within science pedagogy and inquiry-based teaching rather than general tool operation¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that teacher educators go beyond general AI tool operation, such as using ChatGPT or generating teaching materials, and instead embed AI training within science pedagogy and inquiry-based teaching. Suggested activities include guiding pre-service teachers in using AI to design inquiry questions, evaluate AI-generated scientific models, employ simulations for experimental reasoning, judge the scientific accuracy of AI-generated content, and identify ethical risks and biases in AI-supported instruction. This follows from the finding that AI literacy relates to integration intention through AI-TPACK and self-efficacy.

Design Implications¶

Context¶

Requirements¶

• Teacher educators with capacity to design science-specific AI activities spanning inquiry design, model evaluation, simulation use, accuracy judgment, and ethical reflection

Constraints¶

• The supporting evidence is cross-sectional and self-report; the article states findings should be interpreted as associations rather than a demonstrated developmental sequence

Target Learners¶

• pre-service science teachers in teacher education programs

Target Learning Goals¶

• subject-specific AI-related pedagogical competence for inquiry-based science teaching
• critical evaluation of AI-generated scientific content and ethical risks

Affordances¶

• Ai Tpack Professional Knowledge Bridge

Related Strategies¶

• Embed the ReACT reasoning-acting-observing-reflecting cycle in tutoring systems to mirror inquiry-based learning
• Integrate computational thinking into existing disciplinary teaching across grade bands rather than adding it on
• Embed AI as a learning partner and redesign assessments for AI-rich contexts
• Redesign structurally: grade the reasoning behind AI-assisted work and teach AI-use rules and AI literacy explicitly
• Train teachers in prompt engineering, evaluating AI-generated responses, ethical and privacy considerations, and integrating AI outputs into pedagogical decision-making
• Fund AI literacy initiatives integrating AI ethics into curricula and adapting assessments
• Introduce prompt-engineering training before students use Gen AI for structured self-study techniques
• Strategies for integrating GenAI in STEAM teaching: outcome-anchored lesson planning, differentiation, inquiry and project variants, assessment redesign, and integrity as pedagogy
• Structure AI-focused professional development as a supported trajectory of planning, reflection, design autonomy, and embedded critical AI literacy
• Integrate LLM-based CRP support tools within professional development to encourage teacher reflection beyond template-based outputs
• Embed GenAI into existing pedagogical strategies instead of undertaking full curriculum redesign
• Introduce computational thinking to preservice teachers within teacher education programs
• Integrate structured opportunities for critical engagement with AI into teacher education programs

Examples¶

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Key Sources¶

• Zou J, Li N, Wang X and Du J (2026) From AI literacy to AI-integrated inquiry-based science teaching: the serial mediating roles of AI-TPACK and science teaching self-efficacy among Chinese pre-service science teachers. Front. Psychol. 17:1911909. https://doi.org/10.3389/fpsyg.2026.1911909