The EDITT Framework: Responsible AI Adoption and Demonstrable Value in Everyday Work

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

The EDITT Framework (Empathize, Diagnose, Ideate, Test, Translate) is a practice-informed, five-stage methodology for helping individual professionals apply AI responsibly to real tasks and demonstrate its value with evidence rather than impression. It addresses a specific, recurring gap: the difference between having access to AI tools and knowing how to apply one appropriately to a specific workflow, test it responsibly, and communicate what it actually delivered. This paper is practitioner testimony describing the framework's design and its five stages in detail, including a structured workshop format, a facilitator checklist, and a participant experiment record. It is not a retrospective trial, a published dataset, or evidence of customer outcomes attributable to EDITT, and it does not claim independent validation. Version 0.2 incorporates practitioner feedback and an independent expert review of Version 0.1.