Increase the proportion of AI-resistant problem formats in learning-task design to preserve authentic engagement without proctoring¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends a design-level response to AI-related disengagement: favor problem formats that resist AI assistance, such as graph-based problems requiring visual interpretation and interactive widget manipulation, which cannot be transcribed into a text prompt. The authors state that "problem formats that are inherently resistant to AI assistance can preserve authentic engagement without requiring supervision" and that this approach does not depend on proctoring infrastructure or impose surveillance burdens.

Design Implications¶

Context¶

Requirements¶

• Requires curricula to include graph-based or interactive problem types whose content cannot be faithfully transcribed into a text-only prompt

Constraints¶

• The authors caution the susceptible/resistant boundary is a moving target as multimodal and agentic AI systems advance, and will need re-evaluation

Target Learners¶

• Grade 5 through college mathematics students

Target Learning Goals¶

• Sustained cognitive engagement with practice problems
• Durable knowledge retention

Related Strategies¶

• Redesign assessments to require critical thinking, originality, interactive activities, and real-time or proctored formats

Examples¶

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Key Sources¶

• Rismanchian, S., Uzun, H., Matayoshi, J., Cosyn, E., & Kurd-Misto, E. (2026). Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build. arXiv preprint. https://arxiv.org/abs/2605.21629. doi:10.48550/arxiv.2605.21629