From Program Design to Continuous Improvement: An AI-Supported Decision Framework for Vocational Training Operations
Year: 2026
Type: preprint
Abstract: Running a vocational training program requires connected decisions, not merely a published course. This conceptual framework article combines design-science research with a tutorial format to show how AI-supported decisions can be linked without confusing model advice with organizational authority. Nine decision models (M1–M9) support program features, price and class size, demand, learner journeys, partners, service coverage, three resource types, cohort economics, and the next permitted action. Across them, four shared mechanisms clarify decision objects, put required conditions before preference, reconstruct realistic action spaces, and permit abstention. An optional tenth model (M10) reviews whether the model set should continue, change, simplify, pause, or stop. Each model has a stated basis, input–output contract, procedure, adaptation, checkable design property, and stop condition. The models sit within a nine-stage service journey, while people and authorized bodies retain responsibility for approval, examination, and certification. Evidence consists of a directed narrative review, a design-conformance check, and researcher-generated synthetic mechanism checks. The findings are intentionally mixed: some prototypes enforce stated conditions at an economic cost; the demand model improves only half of the synthetic paths; joint price–capacity planning loses profit after conditions change; and model-set review fails when assumptions about corrective action are wrong. These illustrations reveal rule behavior, trade-offs, and failure modes, not field performance or universal superiority. The contribution is an adaptable decision framework and tutorial that organizations can examine, simplify, and later test with their own evidence.
Source: Preprints.org
Author: Haitong Wei
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7202039603