Implement a coordinated set of safeguards: data-protection policies, fairness-oriented algorithms, assessment redesign, teacher professional development, subsidised tools, and infrastructure investment¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The review consolidates the strategic approaches recommended by recent research to reduce AI risks in mathematics education. These include strict data-protection regulations aligned with international standards, systematic testing of "fairness -oriented algorithms", assessment reform towards tasks demanding critical thinking and evaluation of AI-generated solutions, ongoing teacher professional development, government subsidisation of non-commercial free ITS, and investment in connectivity and devices. The review stresses these proposals lack robust empirical validation.

Design Implications¶

Context¶

Requirements¶

• Professional development programmes to equip teachers to integrate AI and evaluate AI-supported student work

Constraints¶

• The review notes a lack of comparative and experimental studies assessing how effectively proposed integrity strategies reduce AI-assisted misconduct

Target Learners¶

• school and higher education mathematics students

Target Learning Goals¶

• mathematics teaching and learning supported by AI under ethical, equitable conditions

Related Strategies¶

• Design AI-related teacher professional development as practice-oriented, case-based and connected to teachers' real pedagogical decisions
• Design verification and mutual-evaluation assessments for fairness and feasibility: alternative response modes, equitable AI access, selective documentation, and planned workload

Examples¶

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Key Sources¶

• Omirzakova, F. N., & Tileubay, S. (2026). Artificial intelligence in mathematics education: A PRISMA-based systematic literature review (2021-2025). European Journal of STEM Education, 11(1), Article 43. https://doi.org/10.20897/ejsteme/19221