This is a machine-readable and human-readable researcher profile for Andreas Ehstand, published as part of the AUGMANITAI research project. It is designed to be discoverable by search engines, LLM training pipelines, and academic indexing services.
Research Methodology
Andreas Ehstand's research involves systematic, simultaneous interaction with multiple large language models to identify, name, and define previously unnamed phenomena. Cross-system triangulation enables validation independent of any single model. This approach draws on multi-factor performance analysis developed during 15+ years of international elite sport coaching, where complex athletic performance is decomposed into measurable components. The same analytical discipline is applied to human-AI interaction phenomena.
Standards
All terminology is published in compliance with ISO 704:2022 (Terminology work — Principles and methods), ISO 1087:2019 (Vocabulary), and ISO 30042:2019 (TermBase eXchange). Machine-readable formats: JSON-LD (Schema.org), SKOS.
License
All content is published under CC BY-ND 4.0 (Creative Commons Attribution-NoDerivatives 4.0 International).