Classify and design AI teaching tools by governance mechanism, using transferable design questions rather than generic tool categories¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article applies its framework to four published tools as a transfer test and concludes that responsible AI integration "should be studied at the level of governance mechanisms", since questions such as whether a tool is an "AI tutor" are too coarse to guide design or evaluation. It offers reusable questions — what authority the AI has, what intellectual work the learner retains, which human role is accountable, what course-specific artifacts constrain the system, and what traces allow improvement — and shows mechanisms are composable across future systems.

Design Implications¶

Context¶

Requirements¶

• Educators and researchers should evaluate AI tools by the learning activity and accountability structure they create, not only by output quality or model capability

Constraints¶

• The four external cases are a transfer test, not an exhaustive taxonomy

Target Learners¶

• computing educators
• tool builders
• researchers

Target Learning Goals¶

• designing and evaluating AI-mediated learning environments

Related Strategies¶

• Institutional governance should establish practical conditions supporting AI literacy, learner agency, and critical evaluation rather than relying on prohibition and detection
• Foster communities of practice for AI governance instead of rigid top-down policies
• Higher education leaders should develop more inclusive and future-oriented GenAI policies integrating social equity, interdisciplinary experimentation, and sustainability considerations
• When integrating GenAI, deliberately weigh control-oriented structures against designs that promote learner agency and creativity
• Design generative production tools with originality feedback, divergence-promoting generation, and deliberate friction anchored to documented mechanisms

Examples¶

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Key Sources¶

• Ethan Dickey. (2026). Instructional Governance by Design: A Framework for AI in Computing Education. https://arxiv.org/abs/2609.26098