Evaluate instructor-authored AI teaching cases by checking seeds, attempting the analysis with student materials only, and keeping data and teaching materials under joint review¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that instructors treat case revision as a joint review of data and teaching materials: strengthening an interaction requires rerunning the reference analysis, while wording or grading changes may leave the dataset intact. Because generation and verification may share the same mistaken assumption, the authors suggest that "Checking different seeds and attempting the analysis with only the student materials can help assess whether the intended discoveries are stable and accessible." Verification summaries should make weakly supported patterns easy to identify.

Design Implications¶

Context¶

Requirements¶

• Instructor review of the first package in the agreed formats; acceptance applies to the version the instructor reviewed

Constraints¶

• The article notes a correct calculation can still answer the wrong question, and familiar business concepts remain available to AI models in any new case

Target Learners¶

• instructors preparing AI-assisted analytics courses

Target Learning Goals¶

• assessing student reasoning about AI-generated and AI-analyzed data

Affordances¶

• Datacanvas Edu Framework

Related Strategies¶

• Use the DOT Framework's Empathize and Test/Feedback stages as practical checklists, and build professional learning communities around shared prompting and critique practices

Examples¶

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Key Sources¶

• Bang An, Maria Hamdani, Joseph Fox. (2026). DataCanvas-EDU: An Agentic Framework for Instructor-Guided Synthetic Data Generation in Business Analytics Education. https://arxiv.org/abs/2609.19617