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Happy Sunday! I’m Liz DiLuzio and today I’m writing from my roles as adjunct professor at New York University and freelance data analysis instructor. Over the past year, my colleagues and I have begun incorporating ChatGPT into the data analysis workshops we teach in order to provide a learning environment where those interested in exploring AI’s application in data analytics may do so. Observing my participants as they interact with ChatGPT has highlighted for me the necessity for clear guidelines and structured approaches when integrating AI into our evaluation processes. This blog post presents a structured framework for effectively harnessing AI that is inspired by the principles of data-driven decision-making.

In 2019, Sharon, Natalie, and I (The ARCHES Evaluation Team) held a series of meetings that focused on developing and implementing the ARCHES Collaborative Diagnostic Tool (ACDT).  It was decided that this tool would be used in a multi-year pilot with the Tulare-Kings College and Career Collaborative (TKCCC).  The focus of this effort was to implement the ACDT with the goals of testing and refining the language, validating and calibrating outcome indicators, clarifying roles for collaboration, developing data visualizations, and developing protocols that support the collaborative analysis of data.