Building CASE by Generative AI Making Cases Out of Control AutomationScience and Engineering

Tian Yonglin

Abstract

Control and Automation Science and Engineering (CASE) is often taught through a small set of familiar examples: inverted pendulums, cruise controllers, tank-level systems, and similar benchmark problems. These examples are useful, but they no longer reflect the diversity and pace of modern automation practice. Developing new cases is difficult because it requires control-theoretic expertise, domain knowledge, access to realistic operating data, and repeated pedagogical calibration. This paper introduces CASE-Gen, a generative-AI-assisted framework for making teaching cases out of control and automation source materials. The framework follows three steps: scenario extraction from technical sources, schema-guided case structuring, and multi-dimensional quality assurance. Unlike direct prompting, CASE-Gen constrains generation through a CASE-specific schema and verifies outputs using retrieval grounding, Bloom’s taxonomy checks, difficulty calibration, and symbolic consistency tests. We describe the design rationale, implementation workflow, an illustrative case package, and an evaluation plan, then show how the framework can support accreditation-oriented curriculum development, professional upskilling, and cross-institutional case sharing. The paper is a framework proposal rather than a completed empirical evaluation. The central argument is modest but practical: generative AI should not replace expert case authors, but it can turn the most time-consuming parts of case development into a supervised, reviewable workflow.

Artificial Intelligence for Education

Building CASE by Generative AI Making Cases Out of Control AutomationScience and Engineering

Abstract

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