A Schema-Driven Form Engine Architecture for Data Capture in Field Service Environments

This paper introduced a schema-driven form engine architecture for capturing enterprise field service data. The architecture aims to ensure consistent rendering across platforms, work offline, use a structured block taxonomy, and enforce compliance-grade validation. Its key contribution is separating the form definition, shown as a versioned JSON schema, from form rendering, which is assigned to native platform implementations under a shared agreement. A single authoring action thus creates consistent and validated form experiences across Android, iOS, and Web clients. The framework was tested and validated in a real enterprise deployment at a multinational healthcare equipment manufacturer. It replaced a discontinued third-party SaaS data capture tool with a complete in-house system. The deployment demonstrated the framework operating under ISO 13485-regulated field service conditions. Three areas for future work stem from this implementation. First, conditional block visibility would allow previous field values to control the display of later blocks, enabling forms that adapt to only show relevant inputs. Second, formal testing of rendering across platforms and syncing under realistic connectivity would improve the empirical foundation of the framework. Third, AI-assisted form generation using large language models to suggest block composition and validation settings from natural language service procedures is a natural extension that fits with enterprise AI integration..

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