Train teachers to use generative AI as a scaffold for mathematical thinking rather than as a source of answers¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that mathematics teacher education prepare teachers to deploy generative AI pedagogically as a thinking scaffold. It states that "Mathematics teacher education programmes should provide training on the pedagogically appropriate use of generative AI as a scaffold for mathematical thinking rather than as a source of answers." Professional development should equip practicing teachers with strategies for integrating AI-supported activities into geometry lessons, and curriculum developers should incorporate activities connecting visualization with deductive reasoning.

Design Implications¶

Context¶

Requirements¶

• AI tools must be configured not to provide complete proofs or final answers, so students justify relationships and organize deductive steps themselves.

Constraints¶

• Evidence comes from a single senior high school quasi-experiment, which the article states may limit generalizability to other educational contexts.

Target Learners¶

• senior high school students
• preservice and practicing mathematics teachers

Target Learning Goals¶

• geometric reasoning
• proof construction

Affordances¶

• Genai Scaffold Vanhiele Clt Framework

Related Strategies¶

• Train teachers in prompt engineering, evaluating AI-generated responses, ethical and privacy considerations, and integrating AI outputs into pedagogical decision-making
• Research directions: scaffold reasoning without displacing epistemic agency and rethink curricula to preserve core human capabilities
• Scaffolded AI integration with teacher training and reflective tasks to reduce over-reliance

Examples¶

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Key Sources¶

• Davor I (2026) Generative AI-supported instruction as a cognitive scaffold: effects on senior high school students' geometric reasoning and proof construction. Front. Educ. 11:1869961. https://doi.org/10.3389/feduc.2026.1869961