expEkT

expEkT is a term used in theoretical discussions and nascent projects to describe a framework for capturing, formalizing, and reusing tacit knowledge in experimental settings. It is not a single standard but a label for approaches that combine experimental design with mechanisms for expert knowledge transfer to automated systems.

In practice, expEkT-inspired work emphasizes structured elicitation of expert judgment, documentation of decision rationales, and reproducible experiment logs. Typical components include guided elicitation prompts, scoring rubrics for both experimental outcomes and the quality of the elicited knowledge, and interfaces that archive tacit insights from practitioners. The aim is to bridge the gap between data-driven experimentation and the practitioner experience that is often difficult to codify.

Origins and usage: The term appears in academic and practitioner forums in the early 2020s and remains informal, with no formal specification or governing body. Its meaning can vary by context, sometimes emphasizing knowledge capture for model auditing, safety, or organizational learning.

Etymology and form: expEkT is a stylized compound, commonly interpreted as experimental EkT, with EkT standing for expert knowledge transfer or experiential knowledge transfer. Capitalization and hyphenation vary across sources.

Related topics include knowledge transfer, tacit knowledge, experimental design, and human-in-the-loop AI.