Use AI-based simulations as low-risk environments for practising and making visible teacher noticing¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends AI-based simulations as controlled, low-risk settings where preservice teachers can practise noticing without ethical concerns of classroom experimentation. The authors "suggest that AI-based simulations may serve as a powerful tool for making visible how PTs decide to respond to students' thinking and how they shape interactional opportunities during noticing processes". LLMs acting as both student and mentor enable fine-grained analysis of noticing processes difficult to capture in real classrooms, and may let teachers explore how questioning strategies can be redesigned to create richer noticing opportunities.

Design Implications¶

Context¶

Requirements¶

• Careful design and facilitation, critical use, pedagogical framing, and integration into structured learning environments

Constraints¶

• The deficit-oriented, misconception-centred design may have constrained strength-based noticing; the text-based format underrepresents perceptual and multimodal demands of mathematics teaching

Target Learners¶

• Preservice mathematics teachers

Target Learning Goals¶

• Developing noticing of students' mathematical thinking and shaping interactional opportunities

Related Strategies¶

Examples¶

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Key Sources¶

• Selen Galiç, Mathias Tejera, Sebastián Parodi, Markus Hohenwarter, Zsolt Lavicza. (2026). Preservice mathematics teachers' noticing in AI-based simulations: transitions among attending, interpreting, and shaping. ZDM – Mathematics Education. https://doi.org/10.1007/s11858-026-01805-4