Generative AI-Supported Spreadsheet Learning for Process Monitoring and Verification in Undergraduate Engineering Education

Introduction: Process-related engineering education increasingly requires students to interpret data, monitor conditions, evaluate thresholds, and verify spreadsheet-based models. Generative artificial intelligence (GenAI) can assist explanation, debugging, and formula generation, but unstructured use may encourage answer copying and weaken verification responsibility. This study proposes a process-oriented instructional framework for integrating GenAI into Excel- and VBA-based learning for undergraduate engineering students. Methods: A design-based instructional development approach was used to construct a five-phase framework comprising process problem framing, prompt drafting, AI-assisted modeling, spreadsheet/VBA implementation, and verification-oriented revision. The framework requires students to retain the original task, prompt history, revised spreadsheet or code, and a verification note. A planned classroom evaluation will use assignment artifacts, prompt logs, rubric-based assessment, classroom observations, and reflection sheets as evidence sources. Results: This abstract reports the design-stage framework rather than completed empirical evaluation. The framework specifies how GenAI can be incorporated into process-data interpretation, formula debugging, threshold classification, and monitoring-oriented reasoning while preserving explicit verification responsibilities. It also defines safeguards for anticipated risks, including superficial copying, misplaced trust in generated formulas, and uneven participation, through prompt templates, peer explanation, boundary-case checking, and a rubric covering problem framing, prompt quality, implementation, verification, and reflection. Conclusions: The proposed framework offers a transparent and testable structure for GenAI-supported spreadsheet learning in process-related engineering education. Claims regarding student learning, verification performance, or instructional effectiveness require subsequent classroom implementation and empirical evaluation.